Forecasting high-priority infectious disease surveillance regions: a socioeconomic model.

PubMed

Chan, Emily H; Scales, David A; Brewer, Timothy F; Madoff, Lawrence C; Pollack, Marjorie P; Hoen, Anne G; Choden, Tenzin; Brownstein, John S

2013-02-01

Few researchers have assessed the relationships between socioeconomic inequality and infectious disease outbreaks at the population level globally. We use a socioeconomic model to forecast national annual rates of infectious disease outbreaks. We constructed a multivariate mixed-effects Poisson model of the number of times a given country was the origin of an outbreak in a given year. The dataset included 389 outbreaks of international concern reported in the World Health Organization's Disease Outbreak News from 1996 to 2008. The initial full model included 9 socioeconomic variables related to education, poverty, population health, urbanization, health infrastructure, gender equality, communication, transportation, and democracy, and 1 composite index. Population, latitude, and elevation were included as potential confounders. The initial model was pared down to a final model by a backwards elimination procedure. The dependent and independent variables were lagged by 2 years to allow for forecasting future rates. Among the socioeconomic variables tested, the final model included child measles immunization rate and telephone line density. The Democratic Republic of Congo, China, and Brazil were predicted to be at the highest risk for outbreaks in 2010, and Colombia and Indonesia were predicted to have the highest percentage of increase in their risk compared to their average over 1996-2008. Understanding socioeconomic factors could help improve the understanding of outbreak risk. The inclusion of the measles immunization variable suggests that there is a fundamental basis in ensuring adequate public health capacity. Increased vigilance and expanding public health capacity should be prioritized in the projected high-risk regions.

Analysis of significant factors for dengue fever incidence prediction.

PubMed

Siriyasatien, Padet; Phumee, Atchara; Ongruk, Phatsavee; Jampachaisri, Katechan; Kesorn, Kraisak

2016-04-16

Many popular dengue forecasting techniques have been used by several researchers to extrapolate dengue incidence rates, including the K-H model, support vector machines (SVM), and artificial neural networks (ANN). The time series analysis methodology, particularly ARIMA and SARIMA, has been increasingly applied to the field of epidemiological research for dengue fever, dengue hemorrhagic fever, and other infectious diseases. The main drawback of these methods is that they do not consider other variables that are associated with the dependent variable. Additionally, new factors correlated to the disease are needed to enhance the prediction accuracy of the model when it is applied to areas of similar climates, where weather factors such as temperature, total rainfall, and humidity are not substantially different. Such drawbacks may consequently lower the predictive power for the outbreak. The predictive power of the forecasting model-assessed by Akaike's information criterion (AIC), Bayesian information criterion (BIC), and the mean absolute percentage error (MAPE)-is improved by including the new parameters for dengue outbreak prediction. This study's selected model outperforms all three other competing models with the lowest AIC, the lowest BIC, and a small MAPE value. The exclusive use of climate factors from similar locations decreases a model's prediction power. The multivariate Poisson regression, however, effectively forecasts even when climate variables are slightly different. Female mosquitoes and seasons were strongly correlated with dengue cases. Therefore, the dengue incidence trends provided by this model will assist the optimization of dengue prevention. The present work demonstrates the important roles of female mosquito infection rates from the previous season and climate factors (represented as seasons) in dengue outbreaks. Incorporating these two factors in the model significantly improves the predictive power of dengue hemorrhagic fever forecasting models, as confirmed by AIC, BIC, and MAPE.

Improved tools and strategies for the prevention and control of arboviral diseases: A research-to-policy forum.

PubMed

Olliaro, Piero; Fouque, Florence; Kroeger, Axel; Bowman, Leigh; Velayudhan, Raman; Santelli, Ana Carolina; Garcia, Diego; Skewes Ramm, Ronald; Sulaiman, Lokman H; Tejeda, Gustavo Sanchez; Morales, Fabiàn Correa; Gozzer, Ernesto; Garrido, César Basso; Quang, Luong Chan; Gutierrez, Gamaliel; Yadon, Zaida E; Runge-Ranzinger, Silvia

2018-02-01

Research has been conducted on interventions to control dengue transmission and respond to outbreaks. A summary of the available evidence will help inform disease control policy decisions and research directions, both for dengue and, more broadly, for all Aedes-borne arboviral diseases. A research-to-policy forum was convened by TDR, the Special Programme for Research and Training in Tropical Diseases, with researchers and representatives from ministries of health, in order to review research findings and discuss their implications for policy and research. The participants reviewed findings of research supported by TDR and others. Surveillance and early outbreak warning. Systematic reviews and country studies identify the critical characteristics that an alert system should have to document trends reliably and trigger timely responses (i.e., early enough to prevent the epidemic spread of the virus) to dengue outbreaks. A range of variables that, according to the literature, either indicate risk of forthcoming dengue transmission or predict dengue outbreaks were tested and some of them could be successfully applied in an Early Warning and Response System (EWARS). Entomological surveillance and vector management. A summary of the published literature shows that controlling Aedes vectors requires complex interventions and points to the need for more rigorous, standardised study designs, with disease reduction as the primary outcome to be measured. House screening and targeted vector interventions are promising vector management approaches. Sampling vector populations, both for surveillance purposes and evaluation of control activities, is usually conducted in an unsystematic way, limiting the potentials of entomological surveillance for outbreak prediction. Combining outbreak alert and improved approaches of vector management will help to overcome the present uncertainties about major risk groups or areas where outbreak response should be initiated and where resources for vector management should be allocated during the interepidemic period. The Forum concluded that the evidence collected can inform policy decisions, but also that important research gaps have yet to be filled.

Improved tools and strategies for the prevention and control of arboviral diseases: A research-to-policy forum

PubMed Central

Olliaro, Piero; Fouque, Florence; Kroeger, Axel; Bowman, Leigh; Velayudhan, Raman; Santelli, Ana Carolina; Garcia, Diego; Skewes Ramm, Ronald; Sulaiman, Lokman H.; Tejeda, Gustavo Sanchez; Morales, Fabiàn Correa; Gozzer, Ernesto; Garrido, César Basso; Quang, Luong Chan; Gutierrez, Gamaliel; Yadon, Zaida E.

2018-01-01

Background Research has been conducted on interventions to control dengue transmission and respond to outbreaks. A summary of the available evidence will help inform disease control policy decisions and research directions, both for dengue and, more broadly, for all Aedes-borne arboviral diseases. Method A research-to-policy forum was convened by TDR, the Special Programme for Research and Training in Tropical Diseases, with researchers and representatives from ministries of health, in order to review research findings and discuss their implications for policy and research. Results The participants reviewed findings of research supported by TDR and others. Surveillance and early outbreak warning. Systematic reviews and country studies identify the critical characteristics that an alert system should have to document trends reliably and trigger timely responses (i.e., early enough to prevent the epidemic spread of the virus) to dengue outbreaks. A range of variables that, according to the literature, either indicate risk of forthcoming dengue transmission or predict dengue outbreaks were tested and some of them could be successfully applied in an Early Warning and Response System (EWARS). Entomological surveillance and vector management. A summary of the published literature shows that controlling Aedes vectors requires complex interventions and points to the need for more rigorous, standardised study designs, with disease reduction as the primary outcome to be measured. House screening and targeted vector interventions are promising vector management approaches. Sampling vector populations, both for surveillance purposes and evaluation of control activities, is usually conducted in an unsystematic way, limiting the potentials of entomological surveillance for outbreak prediction. Combining outbreak alert and improved approaches of vector management will help to overcome the present uncertainties about major risk groups or areas where outbreak response should be initiated and where resources for vector management should be allocated during the interepidemic period. Conclusions The Forum concluded that the evidence collected can inform policy decisions, but also that important research gaps have yet to be filled. PMID:29389959

Alarm Variables for Dengue Outbreaks: A Multi-Centre Study in Asia and Latin America

PubMed Central

Bowman, Leigh R.; Tejeda, Gustavo S.; Coelho, Giovanini E.; Sulaiman, Lokman H.; Gill, Balvinder S.; McCall, Philip J.; Olliaro, Piero L.; Ranzinger, Silvia R.; Quang, Luong C.; Ramm, Ronald S.; Kroeger, Axel; Petzold, Max G.

2016-01-01

Background Worldwide, dengue is an unrelenting economic and health burden. Dengue outbreaks have become increasingly common, which place great strain on health infrastructure and services. Early warning models could allow health systems and vector control programmes to respond more cost-effectively and efficiently. Methodology/Principal Findings The Shewhart method and Endemic Channel were used to identify alarm variables that may predict dengue outbreaks. Five country datasets were compiled by epidemiological week over the years 2007–2013. These data were split between the years 2007–2011 (historic period) and 2012–2013 (evaluation period). Associations between alarm/ outbreak variables were analysed using logistic regression during the historic period while alarm and outbreak signals were captured during the evaluation period. These signals were combined to form alarm/ outbreak periods, where 2 signals were equal to 1 period. Alarm periods were quantified and used to predict subsequent outbreak periods. Across Mexico and Dominican Republic, an increase in probable cases predicted outbreaks of hospitalised cases with sensitivities and positive predictive values (PPV) of 93%/ 83% and 97%/ 86% respectively, at a lag of 1–12 weeks. An increase in mean temperature ably predicted outbreaks of hospitalised cases in Mexico and Brazil, with sensitivities and PPVs of 79%/ 73% and 81%/ 46% respectively, also at a lag of 1–12 weeks. Mean age was predictive of hospitalised cases at sensitivities and PPVs of 72%/ 74% and 96%/ 45% in Mexico and Malaysia respectively, at a lag of 4–16 weeks. Conclusions/Significance An increase in probable cases was predictive of outbreaks, while meteorological variables, particularly mean temperature, demonstrated predictive potential in some countries, but not all. While it is difficult to define uniform variables applicable in every country context, the use of probable cases and meteorological variables in tailored early warning systems could be used to highlight the occurrence of dengue outbreaks or indicate increased risk of dengue transmission. PMID:27348752

Using Mobile Phone Data to Predict the Spatial Spread of Cholera

PubMed Central

Bengtsson, Linus; Gaudart, Jean; Lu, Xin; Moore, Sandra; Wetter, Erik; Sallah, Kankoe; Rebaudet, Stanislas; Piarroux, Renaud

2015-01-01

Effective response to infectious disease epidemics requires focused control measures in areas predicted to be at high risk of new outbreaks. We aimed to test whether mobile operator data could predict the early spatial evolution of the 2010 Haiti cholera epidemic. Daily case data were analysed for 78 study areas from October 16 to December 16, 2010. Movements of 2.9 million anonymous mobile phone SIM cards were used to create a national mobility network. Two gravity models of population mobility were implemented for comparison. Both were optimized based on the complete retrospective epidemic data, available only after the end of the epidemic spread. Risk of an area experiencing an outbreak within seven days showed strong dose-response relationship with the mobile phone-based infectious pressure estimates. The mobile phone-based model performed better (AUC 0.79) than the retrospectively optimized gravity models (AUC 0.66 and 0.74, respectively). Infectious pressure at outbreak onset was significantly correlated with reported cholera cases during the first ten days of the epidemic (p < 0.05). Mobile operator data is a highly promising data source for improving preparedness and response efforts during cholera outbreaks. Findings may be particularly important for containment efforts of emerging infectious diseases, including high-mortality influenza strains. PMID:25747871

Using mobile phone data to predict the spatial spread of cholera.

PubMed

Bengtsson, Linus; Gaudart, Jean; Lu, Xin; Moore, Sandra; Wetter, Erik; Sallah, Kankoe; Rebaudet, Stanislas; Piarroux, Renaud

2015-03-09

Effective response to infectious disease epidemics requires focused control measures in areas predicted to be at high risk of new outbreaks. We aimed to test whether mobile operator data could predict the early spatial evolution of the 2010 Haiti cholera epidemic. Daily case data were analysed for 78 study areas from October 16 to December 16, 2010. Movements of 2.9 million anonymous mobile phone SIM cards were used to create a national mobility network. Two gravity models of population mobility were implemented for comparison. Both were optimized based on the complete retrospective epidemic data, available only after the end of the epidemic spread. Risk of an area experiencing an outbreak within seven days showed strong dose-response relationship with the mobile phone-based infectious pressure estimates. The mobile phone-based model performed better (AUC 0.79) than the retrospectively optimized gravity models (AUC 0.66 and 0.74, respectively). Infectious pressure at outbreak onset was significantly correlated with reported cholera cases during the first ten days of the epidemic (p < 0.05). Mobile operator data is a highly promising data source for improving preparedness and response efforts during cholera outbreaks. Findings may be particularly important for containment efforts of emerging infectious diseases, including high-mortality influenza strains.

Early warning and response system (EWARS) for dengue outbreaks: Recent advancements towards widespread applications in critical settings

PubMed Central

Kroeger, Axel; Olliaro, Piero; Rocklöv, Joacim; Sewe, Maquins Odhiambo; Tejeda, Gustavo; Benitez, David; Gill, Balvinder; Hakim, S. Lokman; Gomes Carvalho, Roberta; Bowman, Leigh; Petzold, Max

2018-01-01

Background Dengue outbreaks are increasing in frequency over space and time, affecting people’s health and burdening resource-constrained health systems. The ability to detect early emerging outbreaks is key to mounting an effective response. The early warning and response system (EWARS) is a toolkit that provides countries with early-warning systems for efficient and cost-effective local responses. EWARS uses outbreak and alarm indicators to derive prediction models that can be used prospectively to predict a forthcoming dengue outbreak at district level. Methods We report on the development of the EWARS tool, based on users’ recommendations into a convenient, user-friendly and reliable software aided by a user’s workbook and its field testing in 30 health districts in Brazil, Malaysia and Mexico. Findings 34 Health officers from the 30 study districts who had used the original EWARS for 7 to 10 months responded to a questionnaire with mainly open-ended questions. Qualitative content analysis showed that participants were generally satisfied with the tool but preferred open-access vs. commercial software. EWARS users also stated that the geographical unit should be the district, while access to meteorological information should be improved. These recommendations were incorporated into the second-generation EWARS-R, using the free R software, combined with recent surveillance data and resulted in higher sensitivities and positive predictive values of alarm signals compared to the first-generation EWARS. Currently the use of satellite data for meteorological information is being tested and a dashboard is being developed to increase user-friendliness of the tool. The inclusion of other Aedes borne viral diseases is under discussion. Conclusion EWARS is a pragmatic and useful tool for detecting imminent dengue outbreaks to trigger early response activities. PMID:29727447

Early warning and response system (EWARS) for dengue outbreaks: Recent advancements towards widespread applications in critical settings.

PubMed

Hussain-Alkhateeb, Laith; Kroeger, Axel; Olliaro, Piero; Rocklöv, Joacim; Sewe, Maquins Odhiambo; Tejeda, Gustavo; Benitez, David; Gill, Balvinder; Hakim, S Lokman; Gomes Carvalho, Roberta; Bowman, Leigh; Petzold, Max

2018-01-01

Dengue outbreaks are increasing in frequency over space and time, affecting people's health and burdening resource-constrained health systems. The ability to detect early emerging outbreaks is key to mounting an effective response. The early warning and response system (EWARS) is a toolkit that provides countries with early-warning systems for efficient and cost-effective local responses. EWARS uses outbreak and alarm indicators to derive prediction models that can be used prospectively to predict a forthcoming dengue outbreak at district level. We report on the development of the EWARS tool, based on users' recommendations into a convenient, user-friendly and reliable software aided by a user's workbook and its field testing in 30 health districts in Brazil, Malaysia and Mexico. 34 Health officers from the 30 study districts who had used the original EWARS for 7 to 10 months responded to a questionnaire with mainly open-ended questions. Qualitative content analysis showed that participants were generally satisfied with the tool but preferred open-access vs. commercial software. EWARS users also stated that the geographical unit should be the district, while access to meteorological information should be improved. These recommendations were incorporated into the second-generation EWARS-R, using the free R software, combined with recent surveillance data and resulted in higher sensitivities and positive predictive values of alarm signals compared to the first-generation EWARS. Currently the use of satellite data for meteorological information is being tested and a dashboard is being developed to increase user-friendliness of the tool. The inclusion of other Aedes borne viral diseases is under discussion. EWARS is a pragmatic and useful tool for detecting imminent dengue outbreaks to trigger early response activities.

Predicting Dengue Fever Outbreaks in French Guiana Using Climate Indicators.

PubMed

Adde, Antoine; Roucou, Pascal; Mangeas, Morgan; Ardillon, Vanessa; Desenclos, Jean-Claude; Rousset, Dominique; Girod, Romain; Briolant, Sébastien; Quenel, Philippe; Flamand, Claude

2016-04-01

Dengue fever epidemic dynamics are driven by complex interactions between hosts, vectors and viruses. Associations between climate and dengue have been studied around the world, but the results have shown that the impact of the climate can vary widely from one study site to another. In French Guiana, climate-based models are not available to assist in developing an early warning system. This study aims to evaluate the potential of using oceanic and atmospheric conditions to help predict dengue fever outbreaks in French Guiana. Lagged correlations and composite analyses were performed to identify the climatic conditions that characterized a typical epidemic year and to define the best indices for predicting dengue fever outbreaks during the period 1991-2013. A logistic regression was then performed to build a forecast model. We demonstrate that a model based on summer Equatorial Pacific Ocean sea surface temperatures and Azores High sea-level pressure had predictive value and was able to predict 80% of the outbreaks while incorrectly predicting only 15% of the non-epidemic years. Predictions for 2014-2015 were consistent with the observed non-epidemic conditions, and an outbreak in early 2016 was predicted. These findings indicate that outbreak resurgence can be modeled using a simple combination of climate indicators. This might be useful for anticipating public health actions to mitigate the effects of major outbreaks, particularly in areas where resources are limited and medical infrastructures are generally insufficient.

A framework for responding to coral disease outbreaks that facilitates adaptive management.

PubMed

Beeden, Roger; Maynard, Jeffrey A; Marshall, Paul A; Heron, Scott F; Willis, Bette L

2012-01-01

Predicted increases in coral disease outbreaks associated with climate change have implications for coral reef ecosystems and the people and industries that depend on them. It is critical that coral reef managers understand these implications and have the ability to assess and reduce risk, detect and contain outbreaks, and monitor and minimise impacts. Here, we present a coral disease response framework that has four core components: (1) an early warning system, (2) a tiered impact assessment program, (3) scaled management actions and (4) a communication plan. The early warning system combines predictive tools that monitor the risk of outbreaks of temperature-dependent coral diseases with in situ observations provided by a network of observers who regularly report on coral health and reef state. Verified reports of an increase in disease prevalence trigger a tiered response of more detailed impact assessment, targeted research and/or management actions. The response is scaled to the risk posed by the outbreak, which is a function of the severity and spatial extent of the impacts. We review potential management actions to mitigate coral disease impacts and facilitate recovery, considering emerging strategies unique to coral disease and more established strategies to support reef resilience. We also describe approaches to communicating about coral disease outbreaks that will address common misperceptions and raise awareness of the coral disease threat. By adopting this framework, managers and researchers can establish a community of practice and can develop response plans for the management of coral disease outbreaks based on local needs. The collaborations between managers and researchers we suggest will enable adaptive management of disease impacts following evaluating the cost-effectiveness of emerging response actions and incrementally improving our understanding of outbreak causation.

A Framework for Responding to Coral Disease Outbreaks that Facilitates Adaptive Management

NASA Astrophysics Data System (ADS)

Beeden, Roger; Maynard, Jeffrey A.; Marshall, Paul A.; Heron, Scott F.; Willis, Bette L.

2012-01-01

Predicted increases in coral disease outbreaks associated with climate change have implications for coral reef ecosystems and the people and industries that depend on them. It is critical that coral reef managers understand these implications and have the ability to assess and reduce risk, detect and contain outbreaks, and monitor and minimise impacts. Here, we present a coral disease response framework that has four core components: (1) an early warning system, (2) a tiered impact assessment program, (3) scaled management actions and (4) a communication plan. The early warning system combines predictive tools that monitor the risk of outbreaks of temperature-dependent coral diseases with in situ observations provided by a network of observers who regularly report on coral health and reef state. Verified reports of an increase in disease prevalence trigger a tiered response of more detailed impact assessment, targeted research and/or management actions. The response is scaled to the risk posed by the outbreak, which is a function of the severity and spatial extent of the impacts. We review potential management actions to mitigate coral disease impacts and facilitate recovery, considering emerging strategies unique to coral disease and more established strategies to support reef resilience. We also describe approaches to communicating about coral disease outbreaks that will address common misperceptions and raise awareness of the coral disease threat. By adopting this framework, managers and researchers can establish a community of practice and can develop response plans for the management of coral disease outbreaks based on local needs. The collaborations between managers and researchers we suggest will enable adaptive management of disease impacts following evaluating the cost-effectiveness of emerging response actions and incrementally improving our understanding of outbreak causation.

Understanding the Relationships Between Inspection Results and Risk of Foodborne Illness in Restaurants.

PubMed

Lee, Petrona; Hedberg, Craig W

2016-10-01

Restaurants are important settings for foodborne disease outbreaks and consumers are increasingly using restaurant inspection results to guide decisions about where to eat. Although public posting of inspection results may lead to improved sanitary practices in the restaurant, the relationship between inspection results and risk of foodborne illness appears to be pathogen specific. To further examine the relationship between inspection results and the risk of foodborne disease outbreaks, we evaluated results of routine inspections conducted in multiple restaurants in a chain (Chain A) that was associated with a large Salmonella outbreak in Illinois. Inspection results were collected from 106 Chain A establishments in eight counties. Forty-six outbreak-associated cases were linked to 23 of these Chain A restaurants. There were no significant differences between the outbreak and nonoutbreak restaurants for overall demerit points or for the number of demerit points attributed to hand washing or cross-contamination. Our analyses strongly suggest that the outbreak resulted from consumption of a contaminated fresh produce item without further amplification within individual restaurants. Inspections at these facilities would be unlikely to detect or predict the foodborne illness outbreak because there are no Food Code items in place to stop the introduction of contaminated food from an otherwise approved commercial food source. The results of our study suggest that the agent and food item pairing and route of transmission must be taken into consideration to improve our understanding of the relationship between inspection results and the risk of foodborne illness in restaurants.

Forecasting High-Priority Infectious Disease Surveillance Regions: A Socioeconomic Model

PubMed Central

Chan, Emily H.; Scales, David A.; Brewer, Timothy F.; Madoff, Lawrence C.; Pollack, Marjorie P.; Hoen, Anne G.; Choden, Tenzin; Brownstein, John S.

2013-01-01

Background. Few researchers have assessed the relationships between socioeconomic inequality and infectious disease outbreaks at the population level globally. We use a socioeconomic model to forecast national annual rates of infectious disease outbreaks. Methods. We constructed a multivariate mixed-effects Poisson model of the number of times a given country was the origin of an outbreak in a given year. The dataset included 389 outbreaks of international concern reported in the World Health Organization's Disease Outbreak News from 1996 to 2008. The initial full model included 9 socioeconomic variables related to education, poverty, population health, urbanization, health infrastructure, gender equality, communication, transportation, and democracy, and 1 composite index. Population, latitude, and elevation were included as potential confounders. The initial model was pared down to a final model by a backwards elimination procedure. The dependent and independent variables were lagged by 2 years to allow for forecasting future rates. Results. Among the socioeconomic variables tested, the final model included child measles immunization rate and telephone line density. The Democratic Republic of Congo, China, and Brazil were predicted to be at the highest risk for outbreaks in 2010, and Colombia and Indonesia were predicted to have the highest percentage of increase in their risk compared to their average over 1996–2008. Conclusions. Understanding socioeconomic factors could help improve the understanding of outbreak risk. The inclusion of the measles immunization variable suggests that there is a fundamental basis in ensuring adequate public health capacity. Increased vigilance and expanding public health capacity should be prioritized in the projected high-risk regions. PMID:23118271

A Humidity-Driven Prediction System for Influenza Outbreaks

NASA Astrophysics Data System (ADS)

Thrastarson, H. T.; Teixeira, J.

2015-12-01

Recent studies have highlighted the role of absolute (or specific) humidity conditions as a leading explanation for the seasonal behavior of influenza outbreaks in temperate regions. If the timing and intensity of seasonal influenza outbreaks can be forecast, this would be of great value for public health response efforts. We have developed and implemented a SIRS (Susceptible-Infectious-Recovered-Susceptible) type numerical prediction system that is driven by specific humidity to predict influenza outbreaks. For the humidity, we have explored using both satellite data from the AIRS (Atmospheric Infrared Sounder) instrument as well as ERA-Interim re-analysis data. We discuss the development, testing, sensitivities and limitations of the prediction system and show results for influenza outbreaks in the United States during the years 2010-2014 (modeled in retrospect). Comparisons are made with other existing prediction systems and available data for influenza outbreaks from Google Flu Trends and the CDC (Center for Disease Control), and the incorporation of these datasets into the forecasting system is discussed.

Predictive accuracy of particle filtering in dynamic models supporting outbreak projections.

PubMed

Safarishahrbijari, Anahita; Teyhouee, Aydin; Waldner, Cheryl; Liu, Juxin; Osgood, Nathaniel D

2017-09-26

While a new generation of computational statistics algorithms and availability of data streams raises the potential for recurrently regrounding dynamic models with incoming observations, the effectiveness of such arrangements can be highly subject to specifics of the configuration (e.g., frequency of sampling and representation of behaviour change), and there has been little attempt to identify effective configurations. Combining dynamic models with particle filtering, we explored a solution focusing on creating quickly formulated models regrounded automatically and recurrently as new data becomes available. Given a latent underlying case count, we assumed that observed incident case counts followed a negative binomial distribution. In accordance with the condensation algorithm, each such observation led to updating of particle weights. We evaluated the effectiveness of various particle filtering configurations against each other and against an approach without particle filtering according to the accuracy of the model in predicting future prevalence, given data to a certain point and a norm-based discrepancy metric. We examined the effectiveness of particle filtering under varying times between observations, negative binomial dispersion parameters, and rates with which the contact rate could evolve. We observed that more frequent observations of empirical data yielded super-linearly improved accuracy in model predictions. We further found that for the data studied here, the most favourable assumptions to make regarding the parameters associated with the negative binomial distribution and changes in contact rate were robust across observation frequency and the observation point in the outbreak. Combining dynamic models with particle filtering can perform well in projecting future evolution of an outbreak. Most importantly, the remarkable improvements in predictive accuracy resulting from more frequent sampling suggest that investments to achieve efficient reporting mechanisms may be more than paid back by improved planning capacity. The robustness of the results on particle filter configuration in this case study suggests that it may be possible to formulate effective standard guidelines and regularized approaches for such techniques in particular epidemiological contexts. Most importantly, the work tentatively suggests potential for health decision makers to secure strong guidance when anticipating outbreak evolution for emerging infectious diseases by combining even very rough models with particle filtering method.

An assessment of the ability of routine restaurant inspections to predict food-borne outbreaks in Miami-Dade County, Florida.

PubMed Central

Cruz, M A; Katz, D J; Suarez, J A

2001-01-01

OBJECTIVES: This study sought to determine the usefulness of restaurant inspections in predicting food-borne outbreaks in Miami-Dade County, Fla. METHODS: Inspection reports of restaurants with outbreaks in 1995 (cases; n = 51) were compared with those of randomly selected restaurants that had no reported outbreaks (controls; n = 76). RESULTS: Cases and controls did not differ by overall inspection outcome or mean number of critical violations. Only 1 critical violation--evidence of vermin--was associated with outbreaks (odds ratio = 3.3; 95% confidence interval = 1.1, 13.1). CONCLUSIONS: Results of restaurant inspections in Miami-Dade County did not predict outbreaks. If these findings are representative of the situation in other jurisdictions, inspection practices may need to be updated. PMID:11344897

An assessment of the geographical risks of wild and vaccine-derived poliomyelitis outbreaks in Africa and Asia.

PubMed

O'Reilly, Kathleen M; Lamoureux, Christine; Molodecky, Natalie A; Lyons, Hil; Grassly, Nicholas C; Tallis, Graham

2017-05-26

The international spread of wild poliomyelitis outbreaks continues to threaten eradication of poliomyelitis and in 2014 a public health emergency of international concern was declared. Here we describe a risk scoring system that has been used to assess country-level risks of wild poliomyelitis outbreaks, to inform prioritisation of mass vaccination planning, and describe the change in risk from 2014 to 2016. The methods were also used to assess the risk of emergence of vaccine-derived poliomyelitis outbreaks. Potential explanatory variables were tested against the reported outbreaks of wild poliomyelitis since 2003 using multivariable regression analysis. The regression analysis was translated to a risk score and used to classify countries as Low, Medium, Medium High and High risk, based on the predictive ability of the score. Indicators of population immunity, population displacement and diarrhoeal disease were associated with an increased risk of both wild and vaccine-derived outbreaks. High migration from countries with wild cases was associated with wild outbreaks. High birth numbers were associated with an increased risk of vaccine-derived outbreaks. Use of the scoring system is a transparent and rapid approach to assess country risk of wild and vaccine-derived poliomyelitis outbreaks. Since 2008 there has been a steep reduction in the number of wild poliomyelitis outbreaks and the reduction in countries classified as High and Medium High risk has reflected this. The risk of vaccine-derived poliomyelitis outbreaks has varied geographically. These findings highlight that many countries remain susceptible to poliomyelitis outbreaks and maintenance or improvement in routine immunisation is vital.

Fine-temporal forecasting of outbreak probability and severity: Ross River virus in Western Australia.

PubMed

Koolhof, I S; Bettiol, S; Carver, S

2017-10-01

Health warnings of mosquito-borne disease risk require forecasts that are accurate at fine-temporal resolutions (weekly scales); however, most forecasting is coarse (monthly). We use environmental and Ross River virus (RRV) surveillance to predict weekly outbreak probabilities and incidence spanning tropical, semi-arid, and Mediterranean regions of Western Australia (1991-2014). Hurdle and linear models were used to predict outbreak probabilities and incidence respectively, using time-lagged environmental variables. Forecast accuracy was assessed by model fit and cross-validation. Residual RRV notification data were also examined against mitigation expenditure for one site, Mandurah 2007-2014. Models were predictive of RRV activity, except at one site (Capel). Minimum temperature was an important predictor of RRV outbreaks and incidence at all predicted sites. Precipitation was more likely to cause outbreaks and greater incidence among tropical and semi-arid sites. While variable, mitigation expenditure coincided positively with increased RRV incidence (r 2 = 0·21). Our research demonstrates capacity to accurately predict mosquito-borne disease outbreaks and incidence at fine-temporal resolutions. We apply our findings, developing a user-friendly tool enabling managers to easily adopt this research to forecast region-specific RRV outbreaks and incidence. Approaches here may be of value to fine-scale forecasting of RRV in other areas of Australia, and other mosquito-borne diseases.

Positive impact of infection prevention on the management of nosocomial outbreaks at an academic hospital.

PubMed

Dik, Jan-Willem H; Sinha, Bhanu; Lokate, Mariëtte; Lo-Ten-Foe, Jerome R; Dinkelacker, Ariane G; Postma, Maarten J; Friedrich, Alexander W

2016-10-01

Infection prevention (IP) measures are vital to prevent (nosocomial) outbreaks. Financial evaluations of these are scarce. An incremental cost analysis for an academic IP unit was performed. On a yearly basis, we evaluated: IP measures; costs thereof; numbers of patients at risk for causing nosocomial outbreaks; predicted outbreak patients; and actual outbreak patients. IP costs rose on average yearly with €150,000; however, more IP actions were undertaken. Numbers of patients colonized with high-risk microorganisms increased. The trend of actual outbreak patients remained stable. Predicted prevented outbreak patients saved costs, leading to a positive return on investment of 1.94. This study shows that investments in IP can prevent outbreak cases, thereby saving enough money to earn back these investments.

Counteracting structural errors in ensemble forecast of influenza outbreaks.

PubMed

Pei, Sen; Shaman, Jeffrey

2017-10-13

For influenza forecasts generated using dynamical models, forecast inaccuracy is partly attributable to the nonlinear growth of error. As a consequence, quantification of the nonlinear error structure in current forecast models is needed so that this growth can be corrected and forecast skill improved. Here, we inspect the error growth of a compartmental influenza model and find that a robust error structure arises naturally from the nonlinear model dynamics. By counteracting these structural errors, diagnosed using error breeding, we develop a new forecast approach that combines dynamical error correction and statistical filtering techniques. In retrospective forecasts of historical influenza outbreaks for 95 US cities from 2003 to 2014, overall forecast accuracy for outbreak peak timing, peak intensity and attack rate, are substantially improved for predicted lead times up to 10 weeks. This error growth correction method can be generalized to improve the forecast accuracy of other infectious disease dynamical models.Inaccuracy of influenza forecasts based on dynamical models is partly due to nonlinear error growth. Here the authors address the error structure of a compartmental influenza model, and develop a new improved forecast approach combining dynamical error correction and statistical filtering techniques.

Climate-Based Models for Understanding and Forecasting Dengue Epidemics

PubMed Central

Descloux, Elodie; Mangeas, Morgan; Menkes, Christophe Eugène; Lengaigne, Matthieu; Leroy, Anne; Tehei, Temaui; Guillaumot, Laurent; Teurlai, Magali; Gourinat, Ann-Claire; Benzler, Justus; Pfannstiel, Anne; Grangeon, Jean-Paul; Degallier, Nicolas; De Lamballerie, Xavier

2012-01-01

Background Dengue dynamics are driven by complex interactions between human-hosts, mosquito-vectors and viruses that are influenced by environmental and climatic factors. The objectives of this study were to analyze and model the relationships between climate, Aedes aegypti vectors and dengue outbreaks in Noumea (New Caledonia), and to provide an early warning system. Methodology/Principal Findings Epidemiological and meteorological data were analyzed from 1971 to 2010 in Noumea. Entomological surveillance indices were available from March 2000 to December 2009. During epidemic years, the distribution of dengue cases was highly seasonal. The epidemic peak (March–April) lagged the warmest temperature by 1–2 months and was in phase with maximum precipitations, relative humidity and entomological indices. Significant inter-annual correlations were observed between the risk of outbreak and summertime temperature, precipitations or relative humidity but not ENSO. Climate-based multivariate non-linear models were developed to estimate the yearly risk of dengue outbreak in Noumea. The best explicative meteorological variables were the number of days with maximal temperature exceeding 32°C during January–February–March and the number of days with maximal relative humidity exceeding 95% during January. The best predictive variables were the maximal temperature in December and maximal relative humidity during October–November–December of the previous year. For a probability of dengue outbreak above 65% in leave-one-out cross validation, the explicative model predicted 94% of the epidemic years and 79% of the non epidemic years, and the predictive model 79% and 65%, respectively. Conclusions/Significance The epidemic dynamics of dengue in Noumea were essentially driven by climate during the last forty years. Specific conditions based on maximal temperature and relative humidity thresholds were determinant in outbreaks occurrence. Their persistence was also crucial. An operational model that will enable health authorities to anticipate the outbreak risk was successfully developed. Similar models may be developed to improve dengue management in other countries. PMID:22348154

Vesicular stomatitis forecasting based on Google Trends

PubMed Central

Lu, Yi; Zhou, GuangYa; Chen, Qin

2018-01-01

Background Vesicular stomatitis (VS) is an important viral disease of livestock. The main feature of VS is irregular blisters that occur on the lips, tongue, oral mucosa, hoof crown and nipple. Humans can also be infected with vesicular stomatitis and develop meningitis. This study analyses 2014 American VS outbreaks in order to accurately predict vesicular stomatitis outbreak trends. Methods American VS outbreaks data were collected from OIE. The data for VS keywords were obtained by inputting 24 disease-related keywords into Google Trends. After calculating the Pearson and Spearman correlation coefficients, it was found that there was a relationship between outbreaks and keywords derived from Google Trends. Finally, the predicted model was constructed based on qualitative classification and quantitative regression. Results For the regression model, the Pearson correlation coefficients between the predicted outbreaks and actual outbreaks are 0.953 and 0.948, respectively. For the qualitative classification model, we constructed five classification predictive models and chose the best classification predictive model as the result. The results showed, SN (sensitivity), SP (specificity) and ACC (prediction accuracy) values of the best classification predictive model are 78.52%,72.5% and 77.14%, respectively. Conclusion This study applied Google search data to construct a qualitative classification model and a quantitative regression model. The results show that the method is effective and that these two models obtain more accurate forecast. PMID:29385198

Outbreak column 17: Situational Awareness for healthcare outbreaks

PubMed Central

2015-01-01

Outbreak column 17 introduces the utility of Situation Awareness (SA) for outbreak management. For any given time period, an individual or team’s SA involves a perception of what is going on, meaning derived from the perception and a prediction of what is likely to happen next. The individual or team’s SA informs, but is separate to, both the decisions and actions that follow. The accuracy and completeness of an individual or team’s SA will therefore impact on the effectiveness of decisions and actions taken. SA was developed by the aviation industry and is utilised in situations which, like outbreaks, have dynamic, i.e. continuously changing problem spaces, and wherein a loss of SA is likely to lead to both poor decision-making and actions with potentially fatal consequences. The potential benefits of using SA for outbreaks are discussed and include: (1) retrospectively to identify if poor decision-making was a result of a poor SA; (2) prospectively to identify where the system is weakest; and (3) as a teaching tool to improve the skills of individuals and teams in developing a shared understanding of the here and now. PMID:28989433

Impact of the 4 April 2014 Saharan dust outbreak on the photovoltaic power generation in Germany

NASA Astrophysics Data System (ADS)

Rieger, Daniel; Steiner, Andrea; Bachmann, Vanessa; Gasch, Philipp; Förstner, Jochen; Deetz, Konrad; Vogel, Bernhard; Vogel, Heike

2017-11-01

The importance for reliable forecasts of incoming solar radiation is growing rapidly, especially for those countries with an increasing share in photovoltaic (PV) power production. The reliability of solar radiation forecasts depends mainly on the representation of clouds and aerosol particles absorbing and scattering radiation. Especially under extreme aerosol conditions, numerical weather prediction has a systematic bias in the solar radiation forecast. This is caused by the design of numerical weather prediction models, which typically account for the direct impact of aerosol particles on radiation using climatological mean values and the impact on cloud formation assuming spatially and temporally homogeneous aerosol concentrations. These model deficiencies in turn can lead to significant economic losses under extreme aerosol conditions. For Germany, Saharan dust outbreaks occurring 5 to 15 times per year for several days each are prominent examples for conditions, under which numerical weather prediction struggles to forecast solar radiation adequately. We investigate the impact of mineral dust on the PV-power generation during a Saharan dust outbreak over Germany on 4 April 2014 using ICON-ART, which is the current German numerical weather prediction model extended by modules accounting for trace substances and related feedback processes. We find an overall improvement of the PV-power forecast for 65 % of the pyranometer stations in Germany. Of the nine stations with very high differences between forecast and measurement, eight stations show an improvement. Furthermore, we quantify the direct radiative effects and indirect radiative effects of mineral dust. For our study, direct effects account for 64 %, indirect effects for 20 % and synergistic interaction effects for 16 % of the differences between the forecast including mineral dust radiative effects and the forecast neglecting mineral dust.

Ecological Niche Modeling for Filoviruses: A Risk Map for Ebola and Marburg Virus Disease Outbreaks in Uganda.

PubMed

Nyakarahuka, Luke; Ayebare, Samuel; Mosomtai, Gladys; Kankya, Clovice; Lutwama, Julius; Mwiine, Frank Norbert; Skjerve, Eystein

2017-09-05

Uganda has reported eight outbreaks caused by filoviruses between 2000 to 2016, more than any other country in the world. We used species distribution modeling to predict where filovirus outbreaks are likely to occur in Uganda to help in epidemic preparedness and surveillance. The MaxEnt software, a machine learning modeling approach that uses presence-only data was used to establish filovirus - environmental relationships. Presence-only data for filovirus outbreaks were collected from the field and online sources. Environmental covariates from Africlim that have been downscaled to a nominal resolution of 1km x 1km were used. The final model gave the relative probability of the presence of filoviruses in the study area obtained from an average of 100 bootstrap runs. Model evaluation was carried out using Receiver Operating Characteristic (ROC) plots. Maps were created using ArcGIS 10.3 mapping software. We showed that bats as potential reservoirs of filoviruses are distributed all over Uganda. Potential outbreak areas for Ebola and Marburg virus disease were predicted in West, Southwest and Central parts of Uganda, which corresponds to bat distribution and previous filovirus outbreaks areas. Additionally, the models predicted the Eastern Uganda region and other areas that have not reported outbreaks before to be potential outbreak hotspots. Rainfall variables were the most important in influencing model prediction compared to temperature variables. Despite the limitations in the prediction model due to lack of adequate sample records for outbreaks, especially for the Marburg cases, the models provided risk maps to the Uganda surveillance system on filovirus outbreaks. The risk maps will aid in identifying areas to focus the filovirus surveillance for early detection and responses hence curtailing a pandemic. The results from this study also confirm previous findings that suggest that filoviruses are mainly limited by the amount of rainfall received in an area.

Ecological Niche Modeling for Filoviruses: A Risk Map for Ebola and Marburg Virus Disease Outbreaks in Uganda

PubMed Central

Nyakarahuka, Luke; Ayebare, Samuel; Mosomtai, Gladys; Kankya, Clovice; Lutwama, Julius; Mwiine, Frank Norbert; Skjerve, Eystein

2017-01-01

Introduction: Uganda has reported eight outbreaks caused by filoviruses between 2000 to 2016, more than any other country in the world. We used species distribution modeling to predict where filovirus outbreaks are likely to occur in Uganda to help in epidemic preparedness and surveillance. Methods: The MaxEnt software, a machine learning modeling approach that uses presence-only data was used to establish filovirus – environmental relationships. Presence-only data for filovirus outbreaks were collected from the field and online sources. Environmental covariates from Africlim that have been downscaled to a nominal resolution of 1km x 1km were used. The final model gave the relative probability of the presence of filoviruses in the study area obtained from an average of 100 bootstrap runs. Model evaluation was carried out using Receiver Operating Characteristic (ROC) plots. Maps were created using ArcGIS 10.3 mapping software. Results: We showed that bats as potential reservoirs of filoviruses are distributed all over Uganda. Potential outbreak areas for Ebola and Marburg virus disease were predicted in West, Southwest and Central parts of Uganda, which corresponds to bat distribution and previous filovirus outbreaks areas. Additionally, the models predicted the Eastern Uganda region and other areas that have not reported outbreaks before to be potential outbreak hotspots. Rainfall variables were the most important in influencing model prediction compared to temperature variables. Conclusions: Despite the limitations in the prediction model due to lack of adequate sample records for outbreaks, especially for the Marburg cases, the models provided risk maps to the Uganda surveillance system on filovirus outbreaks. The risk maps will aid in identifying areas to focus the filovirus surveillance for early detection and responses hence curtailing a pandemic. The results from this study also confirm previous findings that suggest that filoviruses are mainly limited by the amount of rainfall received in an area. PMID:29034123

Limits to Forecasting Precision for Outbreaks of Directly Transmitted Diseases

PubMed Central

Drake, John M

2006-01-01

Background Early warning systems for outbreaks of infectious diseases are an important application of the ecological theory of epidemics. A key variable predicted by early warning systems is the final outbreak size. However, for directly transmitted diseases, the stochastic contact process by which outbreaks develop entails fundamental limits to the precision with which the final size can be predicted. Methods and Findings I studied how the expected final outbreak size and the coefficient of variation in the final size of outbreaks scale with control effectiveness and the rate of infectious contacts in the simple stochastic epidemic. As examples, I parameterized this model with data on observed ranges for the basic reproductive ratio (R 0) of nine directly transmitted diseases. I also present results from a new model, the simple stochastic epidemic with delayed-onset intervention, in which an initially supercritical outbreak (R 0 > 1) is brought under control after a delay. Conclusion The coefficient of variation of final outbreak size in the subcritical case (R 0 < 1) will be greater than one for any outbreak in which the removal rate is less than approximately 2.41 times the rate of infectious contacts, implying that for many transmissible diseases precise forecasts of the final outbreak size will be unattainable. In the delayed-onset model, the coefficient of variation (CV) was generally large (CV > 1) and increased with the delay between the start of the epidemic and intervention, and with the average outbreak size. These results suggest that early warning systems for infectious diseases should not focus exclusively on predicting outbreak size but should consider other characteristics of outbreaks such as the timing of disease emergence. PMID:16435887

The First Prediction of a Rift Valley Fever Outbreak

NASA Technical Reports Server (NTRS)

Anyamba, Assaf; Chretien, Jean-Paul; Small, Jennifer; Tucker, Compton J.; Formenty, Pierre; Richardson, Jason H.; Britch, Seth C.; Schnabel, David C.; Erickson, Ralph L.; Linthicum, Kenneth J.

2009-01-01

El Nino/Southern Oscillation (ENSO) related anomalies were analyzed using a combination of satellite measurements of elevated sea surface temperatures, and subsequent elevated rainfall and satellite derived normalized difference vegetation index data. A Rift Valley fever risk mapping model using these climate data predicted areas where outbreaks of Rift Valley fever in humans and animals were expected and occurred in the Horn of Africa from December 2006 to May 2007. The predictions were subsequently confirmed by entomological and epidemiological field investigations of virus activity in the areas identified as at risk. Accurate spatial and temporal predictions of disease activity, as it occurred first in southern Somalia and then through much of Kenya before affecting northern Tanzania, provided a 2 to 6 week period of warning for the Horn of Africa that facilitated disease outbreak response and mitigation activities. This is the first prospective prediction of a Rift Valley fever outbreak.

Prediction of a Rift Valley fever outbreak

PubMed Central

Anyamba, Assaf; Chretien, Jean-Paul; Small, Jennifer; Tucker, Compton J.; Formenty, Pierre B.; Richardson, Jason H.; Britch, Seth C.; Schnabel, David C.; Erickson, Ralph L.; Linthicum, Kenneth J.

2009-01-01

El Niño/Southern Oscillation related climate anomalies were analyzed by using a combination of satellite measurements of elevated sea-surface temperatures and subsequent elevated rainfall and satellite-derived normalized difference vegetation index data. A Rift Valley fever (RVF) risk mapping model using these climate data predicted areas where outbreaks of RVF in humans and animals were expected and occurred in the Horn of Africa from December 2006 to May 2007. The predictions were subsequently confirmed by entomological and epidemiological field investigations of virus activity in the areas identified as at risk. Accurate spatial and temporal predictions of disease activity, as it occurred first in southern Somalia and then through much of Kenya before affecting northern Tanzania, provided a 2 to 6 week period of warning for the Horn of Africa that facilitated disease outbreak response and mitigation activities. To our knowledge, this is the first prospective prediction of a RVF outbreak. PMID:19144928

Magnitude and frequency variations of vector-borne infection outbreaks using the Ross-Macdonald model: explaining and predicting outbreaks of dengue fever.

PubMed

Amaku, M; Azevedo, F; Burattini, M N; Coelho, G E; Coutinho, F A B; Greenhalgh, D; Lopez, L F; Motitsuki, R S; Wilder-Smith, A; Massad, E

2016-08-19

The classical Ross-Macdonald model is often utilized to model vector-borne infections; however, this model fails on several fronts. First, using measured (or estimated) parameters, which values are accepted from the literature, the model predicts a much greater number of cases than what is usually observed. Second, the model predicts a single large outbreak that is followed by decades of much smaller outbreaks, which is not consistent with what is observed. Usually towns or cities report a number of recurrences for many years, even when environmental changes cannot explain the disappearance of the infection between the peaks. In this paper, we continue to examine the pitfalls in modelling this class of infections, and explain that, if properly used, the Ross-Macdonald model works and can be used to understand the patterns of epidemics and even, to some extent, be used to make predictions. We model several outbreaks of dengue fever and show that the variable pattern of yearly recurrence (or its absence) can be understood and explained by a simple Ross-Macdonald model modified to take into account human movement across a range of neighbourhoods within a city. In addition, we analyse the effect of seasonal variations in the parameters that determine the number, longevity and biting behaviour of mosquitoes. Based on the size of the first outbreak, we show that it is possible to estimate the proportion of the remaining susceptible individuals and to predict the likelihood and magnitude of the eventual subsequent outbreaks. This approach is described based on actual dengue outbreaks with different recurrence patterns from some Brazilian regions.

A Statistical Model of the International Spread of Wild Poliovirus in Africa Used to Predict and Prevent Outbreaks

PubMed Central

O'Reilly, Kathleen M.; Chauvin, Claire; Aylward, R. Bruce; Maher, Chris; Okiror, Sam; Wolff, Chris; Nshmirimana, Deo; Donnelly, Christl A.; Grassly, Nicholas C.

2011-01-01

Background Outbreaks of poliomyelitis in African countries that were previously free of wild-type poliovirus cost the Global Polio Eradication Initiative US$850 million during 2003–2009, and have limited the ability of the program to focus on endemic countries. A quantitative understanding of the factors that predict the distribution and timing of outbreaks will enable their prevention and facilitate the completion of global eradication. Methods and Findings Children with poliomyelitis in Africa from 1 January 2003 to 31 December 2010 were identified through routine surveillance of cases of acute flaccid paralysis, and separate outbreaks associated with importation of wild-type poliovirus were defined using the genetic relatedness of these viruses in the VP1/2A region. Potential explanatory variables were examined for their association with the number, size, and duration of poliomyelitis outbreaks in 6-mo periods using multivariable regression analysis. The predictive ability of 6-mo-ahead forecasts of poliomyelitis outbreaks in each country based on the regression model was assessed. A total of 142 genetically distinct outbreaks of poliomyelitis were recorded in 25 African countries, resulting in 1–228 cases (median of two cases). The estimated number of people arriving from infected countries and <5-y childhood mortality were independently associated with the number of outbreaks. Immunisation coverage based on the reported vaccination history of children with non-polio acute flaccid paralysis was associated with the duration and size of each outbreak, as well as the number of outbreaks. Six-month-ahead forecasts of the number of outbreaks in a country or region changed over time and had a predictive ability of 82%. Conclusions Outbreaks of poliomyelitis resulted primarily from continued transmission in Nigeria and the poor immunisation status of populations in neighbouring countries. From 1 January 2010 to 30 June 2011, reduced transmission in Nigeria and increased incidence in reinfected countries in west and central Africa have changed the geographical risk of polio outbreaks, and will require careful immunisation planning to limit onward spread. Please see later in the article for the Editors' Summary PMID:22028632

Spatial Prediction of Coxiella burnetii Outbreak Exposure via Notified Case Counts in a Dose-Response Model.

PubMed

Brooke, Russell J; Kretzschmar, Mirjam E E; Hackert, Volker; Hoebe, Christian J P A; Teunis, Peter F M; Waller, Lance A

2017-01-01

We develop a novel approach to study an outbreak of Q fever in 2009 in the Netherlands by combining a human dose-response model with geostatistics prediction to relate probability of infection and associated probability of illness to an effective dose of Coxiella burnetii. The spatial distribution of the 220 notified cases in the at-risk population are translated into a smooth spatial field of dose. Based on these symptomatic cases, the dose-response model predicts a median of 611 asymptomatic infections (95% range: 410, 1,084) for the 220 reported symptomatic cases in the at-risk population; 2.78 (95% range: 1.86, 4.93) asymptomatic infections for each reported case. The low attack rates observed during the outbreak range from (Equation is included in full-text article.)to (Equation is included in full-text article.). The estimated peak levels of exposure extend to the north-east from the point source with an increasing proportion of asymptomatic infections further from the source. Our work combines established methodology from model-based geostatistics and dose-response modeling allowing for a novel approach to study outbreaks. Unobserved infections and the spatially varying effective dose can be predicted using the flexible framework without assuming any underlying spatial structure of the outbreak process. Such predictions are important for targeting interventions during an outbreak, estimating future disease burden, and determining acceptable risk levels.

Time evolution of predictability of epidemics on networks.

PubMed

Holme, Petter; Takaguchi, Taro

2015-04-01

Epidemic outbreaks of new pathogens, or known pathogens in new populations, cause a great deal of fear because they are hard to predict. For theoretical models of disease spreading, on the other hand, quantities characterizing the outbreak converge to deterministic functions of time. Our goal in this paper is to shed some light on this apparent discrepancy. We measure the diversity of (and, thus, the predictability of) outbreak sizes and extinction times as functions of time given different scenarios of the amount of information available. Under the assumption of perfect information-i.e., knowing the state of each individual with respect to the disease-the predictability decreases exponentially, or faster, with time. The decay is slowest for intermediate values of the per-contact transmission probability. With a weaker assumption on the information available, assuming that we know only the fraction of currently infectious, recovered, or susceptible individuals, the predictability also decreases exponentially most of the time. There are, however, some peculiar regions in this scenario where the predictability decreases. In other words, to predict its final size with a given accuracy, we would need increasingly more information about the outbreak.

Time evolution of predictability of epidemics on networks

NASA Astrophysics Data System (ADS)

Holme, Petter; Takaguchi, Taro

2015-04-01

Epidemic outbreaks of new pathogens, or known pathogens in new populations, cause a great deal of fear because they are hard to predict. For theoretical models of disease spreading, on the other hand, quantities characterizing the outbreak converge to deterministic functions of time. Our goal in this paper is to shed some light on this apparent discrepancy. We measure the diversity of (and, thus, the predictability of) outbreak sizes and extinction times as functions of time given different scenarios of the amount of information available. Under the assumption of perfect information—i.e., knowing the state of each individual with respect to the disease—the predictability decreases exponentially, or faster, with time. The decay is slowest for intermediate values of the per-contact transmission probability. With a weaker assumption on the information available, assuming that we know only the fraction of currently infectious, recovered, or susceptible individuals, the predictability also decreases exponentially most of the time. There are, however, some peculiar regions in this scenario where the predictability decreases. In other words, to predict its final size with a given accuracy, we would need increasingly more information about the outbreak.

Understanding outbreaks of waterborne infectious disease: quantitative microbial risk assessment vs. epidemiology

USDA-ARS?s Scientific Manuscript database

Drinking water contaminated with microbial pathogens can cause outbreaks of infectious disease, and these outbreaks are traditionally studied using epidemiologic methods. Quantitative microbial risk assessment (QMRA) can predict – and therefore help prevent – such outbreaks, but it has never been r...

Epidemiologic and Environmental Risk Factors of Rift Valley Fever in Southern Africa from 2008 to 2011.

PubMed

Glancey, Margaret M; Anyamba, Assaf; Linthicum, Kenneth J

2015-08-01

Rift Valley fever (RVF) outbreaks have been associated with periods of widespread and above-normal rainfall over several months. Knowledge on the environmental factors influencing disease transmission dynamics has provided the basis for developing models to predict RVF outbreaks in Africa. From 2008 to 2011, South Africa experienced the worst wave of RVF outbreaks in almost 40 years. We investigated rainfall-associated environmental factors in southern Africa preceding these outbreaks. RVF epizootic records obtained from the World Animal Health Information Database (WAHID), documenting livestock species affected, location, and time, were analyzed. Environmental variables including rainfall and satellite-derived normalized difference vegetation index (NDVI) data were collected and assessed in outbreak regions to understand the underlying drivers of the outbreaks. The predominant domestic vertebrate species affected in 2008 and 2009 were cattle, when outbreaks were concentrated in the eastern provinces of South Africa. In 2010 and 2011, outbreaks occurred in the interior and southern provinces affecting over 16,000 sheep. The highest number of cases occurred between January and April but epidemics occurred in different regions every year, moving from the northeast of South Africa toward the southwest with each progressing year. The outbreaks showed a pattern of increased rainfall preceding epizootics ranging from 9 to 152 days; however, NDVI and rainfall were less correlated with the start of the outbreaks than has been observed in eastern Africa. Analyses of the multiyear RVF outbreaks of 2008 to 2011 in South Africa indicated that rainfall, NDVI, and other environmental and geographical factors, such as land use, drainage, and topography, play a role in disease emergence. Current and future investigations into these factors will be able to contribute to improving spatial accuracy of models to map risk areas, allowing adequate time for preparation and prevention before an outbreak occurs.

Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China

PubMed Central

Liu, Tao; Zhu, Guanghu; Lin, Hualiang; Zhang, Yonghui; He, Jianfeng; Deng, Aiping; Peng, Zhiqiang; Xiao, Jianpeng; Rutherford, Shannon; Xie, Runsheng; Zeng, Weilin; Li, Xing; Ma, Wenjun

2017-01-01

Background Dengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study develops an early warning model that integrates internet-based query data into traditional surveillance data. Methodology and principal findings A Dengue Baidu Search Index (DBSI) was collected from the Baidu website for developing a predictive model of dengue fever in combination with meteorological and demographic factors. Generalized additive models (GAM) with or without DBSI were established. The generalized cross validation (GCV) score and deviance explained indexes, intraclass correlation coefficient (ICC) and root mean squared error (RMSE), were respectively applied to measure the fitness and the prediction capability of the models. Our results show that the DBSI with one-week lag has a positive linear relationship with the local DF occurrence, and the model with DBSI (ICC:0.94 and RMSE:59.86) has a better prediction capability than the model without DBSI (ICC:0.72 and RMSE:203.29). Conclusions Our study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou. PMID:28263988

Popcorn-worker lung caused by corporate and regulatory negligence: an avoidable tragedy.

PubMed

Egilman, David; Mailloux, Caroline; Valentin, Claire

2007-01-01

Diacetyl-containing butter flavor was identified as the cause of an outbreak of bronchiolitis obliterans (BO) and other lung diseases in popcorn-plant workers. Litigation documents show that the outbreak was both predictable and preventable. The industry trade organization was aware of BO cases in workers at butter-flavoring and popcorn-manufacturing plants but often failed to implement industrial hygiene improvements and actively hid pertinent warning information. Due to weaknesses in the organization and mandates of regulatory bodies, organizations such as NIOSH, OSHA, the FDA, particularly the "generally recognized as safe" (GRAS) system, and the EPA failed to detect and prevent the outbreak, which highlights the need for systemic changes in food-product regulation, including the need for corporations to act responsibly, for stronger regulations with active enforcement, for a restructuring of the GRAS system, and for criminal penalties against corporations and professionals who knowingly hide information relevant to worker protection.

Analysing trends and forecasting malaria epidemics in Madagascar using a sentinel surveillance network: a web-based application.

PubMed

Girond, Florian; Randrianasolo, Laurence; Randriamampionona, Lea; Rakotomanana, Fanjasoa; Randrianarivelojosia, Milijaona; Ratsitorahina, Maherisoa; Brou, Télesphore Yao; Herbreteau, Vincent; Mangeas, Morgan; Zigiumugabe, Sixte; Hedje, Judith; Rogier, Christophe; Piola, Patrice

2017-02-13

The use of a malaria early warning system (MEWS) to trigger prompt public health interventions is a key step in adding value to the epidemiological data routinely collected by sentinel surveillance systems. This study describes a system using various epidemic thresholds and a forecasting component with the support of new technologies to improve the performance of a sentinel MEWS. Malaria-related data from 21 sentinel sites collected by Short Message Service are automatically analysed to detect malaria trends and malaria outbreak alerts with automated feedback reports. Roll Back Malaria partners can, through a user-friendly web-based tool, visualize potential outbreaks and generate a forecasting model. The system already demonstrated its ability to detect malaria outbreaks in Madagascar in 2014. This approach aims to maximize the usefulness of a sentinel surveillance system to predict and detect epidemics in limited-resource environments.

A Supervised Statistical Learning Approach for Accurate Legionella pneumophila Source Attribution during Outbreaks

PubMed Central

Buultjens, Andrew H.; Chua, Kyra Y. L.; Baines, Sarah L.; Kwong, Jason; Gao, Wei; Cutcher, Zoe; Adcock, Stuart; Ballard, Susan; Schultz, Mark B.; Tomita, Takehiro; Subasinghe, Nela; Carter, Glen P.; Pidot, Sacha J.; Franklin, Lucinda; Seemann, Torsten; Gonçalves Da Silva, Anders

2017-01-01

ABSTRACT Public health agencies are increasingly relying on genomics during Legionnaires' disease investigations. However, the causative bacterium (Legionella pneumophila) has an unusual population structure, with extreme temporal and spatial genome sequence conservation. Furthermore, Legionnaires' disease outbreaks can be caused by multiple L. pneumophila genotypes in a single source. These factors can confound cluster identification using standard phylogenomic methods. Here, we show that a statistical learning approach based on L. pneumophila core genome single nucleotide polymorphism (SNP) comparisons eliminates ambiguity for defining outbreak clusters and accurately predicts exposure sources for clinical cases. We illustrate the performance of our method by genome comparisons of 234 L. pneumophila isolates obtained from patients and cooling towers in Melbourne, Australia, between 1994 and 2014. This collection included one of the largest reported Legionnaires' disease outbreaks, which involved 125 cases at an aquarium. Using only sequence data from L. pneumophila cooling tower isolates and including all core genome variation, we built a multivariate model using discriminant analysis of principal components (DAPC) to find cooling tower-specific genomic signatures and then used it to predict the origin of clinical isolates. Model assignments were 93% congruent with epidemiological data, including the aquarium Legionnaires' disease outbreak and three other unrelated outbreak investigations. We applied the same approach to a recently described investigation of Legionnaires' disease within a UK hospital and observed a model predictive ability of 86%. We have developed a promising means to breach L. pneumophila genetic diversity extremes and provide objective source attribution data for outbreak investigations. IMPORTANCE Microbial outbreak investigations are moving to a paradigm where whole-genome sequencing and phylogenetic trees are used to support epidemiological investigations. It is critical that outbreak source predictions are accurate, particularly for pathogens, like Legionella pneumophila, which can spread widely and rapidly via cooling system aerosols, causing Legionnaires' disease. Here, by studying hundreds of Legionella pneumophila genomes collected over 21 years around a major Australian city, we uncovered limitations with the phylogenetic approach that could lead to a misidentification of outbreak sources. We implement instead a statistical learning technique that eliminates the ambiguity of inferring disease transmission from phylogenies. Our approach takes geolocation information and core genome variation from environmental L. pneumophila isolates to build statistical models that predict with high confidence the environmental source of clinical L. pneumophila during disease outbreaks. We show the versatility of the technique by applying it to unrelated Legionnaires' disease outbreaks in Australia and the UK. PMID:28821546

The epidemiologic surveillance of dengue-fever in French Guiana: when achievements trigger higher goals.

PubMed

Flamand, Claude; Quenel, Philippe; Ardillon, Vanessa; Carvalho, Luisiane; Bringay, Sandra; Teisseire, Maguelonne

2011-01-01

The epidemiology of dengue fever in French Guiana is marked by a combination of permanent transmission of the virus in the whole country and the occurrence of regular epidemics. Since 2006, a multi data source surveillance system was implemented to monitor dengue fever patterns, to improve early detection of outbreaks and to allow a better provision of information to health authorities, in order to guide and evaluate prevention activities and control measures. This report illustrates the validity and the performances of the system. We describe the experience gained by such a surveillance system and outline remaining challenges. Future works will consist in the use of other data sources such as environmental factors in order to improve knowledge on virus transmission mechanisms and determine how to use them for outbreaks prediction.

Can tail damage outbreaks in the pig be predicted by behavioural change?

PubMed

Larsen, Mona Lilian Vestbjerg; Andersen, Heidi Mai-Lis; Pedersen, Lene Juul

2016-03-01

Tail biting, resulting in outbreaks of tail damage in pigs, is a multifactorial welfare and economic problem which is usually partly prevented through tail docking. According to European Union legislation, tail docking is not allowed on a routine basis; thus there is a need for alternative preventive methods. One strategy is the surveillance of the pigs' behaviour for known preceding indicators of tail damage, which makes it possible to predict a tail damage outbreak and prevent it in proper time. This review discusses the existing literature on behavioural changes observed prior to a tail damage outbreak. Behaviours found to change prior to an outbreak include increased activity level, increased performance of enrichment object manipulation, and a changed proportion of tail posture with more tails between the legs. Monitoring these types of behaviours is also discussed for the purpose of developing an automatic warning system for tail damage outbreaks, with activity level showing promising results for being monitored automatically. Encouraging results have been found so far for the development of an automatic warning system; however, there is a need for further investigation and development, starting with the description of the temporal development of the predictive behaviour in relation to tail damage outbreaks. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prediction of gastrointestinal disease with over-the-counter diarrheal remedy sales records in the San Francisco Bay Area.

PubMed

Kirian, Michelle L; Weintraub, June M

2010-07-20

Water utilities continue to be interested in implementing syndromic surveillance for the enhanced detection of waterborne disease outbreaks. The authors evaluated the ability of sales of over-the-counter diarrheal remedies available from the National Retail Data Monitor to predict endemic and epidemic gastrointestinal disease in the San Francisco Bay Area. Time series models were fit to weekly diarrheal remedy sales and diarrheal illness case counts. Cross-correlations between the pre-whitened residual series were calculated. Diarrheal remedy sales model residuals were regressed on the number of weekly outbreaks and outbreak-associated cases. Diarrheal remedy sales models were used to auto-forecast one week-ahead sales. The sensitivity and specificity of signals, generated by observed diarrheal remedy sales exceeding the upper 95% forecast confidence interval, in predicting weekly outbreaks were calculated. No significant correlations were identified between weekly diarrheal remedy sales and diarrhea illness case counts, outbreak counts, or the number of outbreak-associated cases. Signals generated by forecasting with the diarrheal remedy sales model did not coincide with outbreak weeks more reliably than signals chosen randomly. This work does not support the implementation of syndromic surveillance for gastrointestinal disease with data available though the National Retail Data Monitor.

Human Cases of Tularemia in Armenia, 1996-2012.

PubMed

Melikjanyan, Syuzanna; Palayan, Karo; Vanyan, Artavazd; Avetisyan, Lilit; Bakunts, Nune; Kotanyan, Marine; Guerra, Marta

2017-09-01

A retrospective analysis was conducted of human cases and outbreaks of tularemia in the Republic of Armenia from 1996 to 2012 utilizing geographic information system software. A total of 266 human cases of tularemia were recorded in Armenia from 1996 to 2012, with yearly incidence ranging from 0 to 5.5 cases per 100,000 people. Cases predominantly affected the male population (62.8%), 11-20 year age group (37.2%), agricultural workers (49.6%), and persons residing in rural areas (93.6%). In 2003, a waterborne outbreak involving 158 cases occurred in Kotayk Marz, and in 2007, a foodborne outbreak with 17 cases occurred in Gegharkunik Marz, attributed to exposure of food products to contaminated hay. Geospatial analysis of all cases showed that the majority were associated with the steppe vegetation zone, elevations between 1,400 and 2,300 m, and the climate zone associated with dry, warm summers, and cold winters. Characterization of these environmental factors were used to develop a predictive risk model to improve surveillance and outbreak response for tularemia in Armenia.

Increase in Multistate Foodborne Disease Outbreaks-United States, 1973-2010.

PubMed

Nguyen, Von D; Bennett, Sarah D; Mungai, Elisabeth; Gieraltowski, Laura; Hise, Kelley; Gould, L Hannah

2015-11-01

Changes in food production and distribution have increased opportunities for foods contaminated early in the supply chain to be distributed widely, increasing the possibility of multistate outbreaks. In recent decades, surveillance systems for foodborne disease have been improved, allowing officials to more effectively identify related cases and to trace and identify an outbreak's source. We reviewed multistate foodborne disease outbreaks reported to the Centers for Disease Control and Prevention's Foodborne Disease Outbreak Surveillance System during 1973-2010. We calculated the percentage of multistate foodborne disease outbreaks relative to all foodborne disease outbreaks and described characteristics of multistate outbreaks, including the etiologic agents and implicated foods. Multistate outbreaks accounted for 234 (0.8%) of 27,755 foodborne disease outbreaks, 24,003 (3%) of 700,600 outbreak-associated illnesses, 2839 (10%) of 29,756 outbreak-associated hospitalizations, and 99 (16%) of 628 outbreak-associated deaths. The median annual number of multistate outbreaks increased from 2.5 during 1973-1980 to 13.5 during 2001-2010; the number of multistate outbreak-associated illnesses, hospitalizations, and deaths also increased. Most multistate outbreaks were caused by Salmonella (47%) and Shiga toxin-producing Escherichia coli (26%). Foods most commonly implicated were beef (22%), fruits (13%), and leafy vegetables (13%). The number of identified and reported multistate foodborne disease outbreaks has increased. Improvements in detection, investigation, and reporting of foodborne disease outbreaks help explain the increasing number of reported multistate outbreaks and the increasing percentage of outbreaks that were multistate. Knowing the etiologic agents and foods responsible for multistate outbreaks can help to identify sources of food contamination so that the safety of the food supply can be improved.

Development of genetic programming-based model for predicting oyster norovirus outbreak risks.

PubMed

Chenar, Shima Shamkhali; Deng, Zhiqiang

2018-01-01

Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide but exact causes of the outbreaks are rarely identified, making it highly unlikely to reduce the risks. This paper presents a genetic programming (GP) based approach to identifying the primary cause of oyster norovirus outbreaks and predicting oyster norovirus outbreaks in order to reduce the risks. In terms of the primary cause, it was found that oyster norovirus outbreaks were controlled by cumulative effects of antecedent environmental conditions characterized by low solar radiation, low water temperature, low gage height (the height of water above a gage datum), low salinity, heavy rainfall, and strong offshore wind. The six environmental variables were determined by using Random Forest (RF) and Binary Logistic Regression (BLR) methods within the framework of the GP approach. In terms of predicting norovirus outbreaks, a risk-based GP model was developed using the six environmental variables and various combinations of the variables with different time lags. The results of local and global sensitivity analyses showed that gage height, temperature, and solar radiation were by far the three most important environmental predictors for oyster norovirus outbreaks, though other variables were also important. Specifically, very low temperature and gage height significantly increased the risk of norovirus outbreaks while high solar radiation markedly reduced the risk, suggesting that low temperature and gage height were associated with the norovirus source while solar radiation was the primary sink of norovirus. The GP model was utilized to hindcast daily risks of oyster norovirus outbreaks along the Northern Gulf of Mexico coast. The daily hindcasting results indicated that the GP model was capable of hindcasting all historical oyster norovirus outbreaks from January 2002 to June 2014 in the Gulf of Mexico with only two false positive outbreaks for the 12.5-year period. The performance of the GP model was characterized with the area under the Receiver Operating Characteristic curve of 0.86, the true positive rate (sensitivity) of 78.53% and the true negative rate (specificity) of 88.82%, respectively, demonstrating the efficacy of the GP model. The findings and results offered new insights into the oyster norovirus outbreaks in terms of source, sink, cause, and predictors. The GP model provided an efficient and effective tool for predicting potential oyster norovirus outbreaks and implementing management interventions to prevent or at least reduce norovirus risks to both the human health and the seafood industry. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting Rift Valley Fever Inter-epidemic Activities and Outbreak Patterns: Insights from a Stochastic Host-Vector Model

PubMed Central

Pedro, Sansao A.; Abelman, Shirley; Tonnang, Henri E. Z.

2016-01-01

Rift Valley fever (RVF) outbreaks are recurrent, occurring at irregular intervals of up to 15 years at least in East Africa. Between outbreaks disease inter-epidemic activities exist and occur at low levels and are maintained by female Aedes mcintoshi mosquitoes which transmit the virus to their eggs leading to disease persistence during unfavourable seasons. Here we formulate and analyse a full stochastic host-vector model with two routes of transmission: vertical and horizontal. By applying branching process theory we establish novel relationships between the basic reproduction number, R0, vertical transmission and the invasion and extinction probabilities. Optimum climatic conditions and presence of mosquitoes have not fully explained the irregular oscillatory behaviour of RVF outbreaks. Using our model without seasonality and applying van Kampen system-size expansion techniques, we provide an analytical expression for the spectrum of stochastic fluctuations, revealing how outbreaks multi-year periodicity varies with the vertical transmission. Our theory predicts complex fluctuations with a dominant period of 1 to 10 years which essentially depends on the efficiency of vertical transmission. Our predictions are then compared to temporal patterns of disease outbreaks in Tanzania, Kenya and South Africa. Our analyses show that interaction between nonlinearity, stochasticity and vertical transmission provides a simple but plausible explanation for the irregular oscillatory nature of RVF outbreaks. Therefore, we argue that while rainfall might be the major determinant for the onset and switch-off of an outbreak, the occurrence of a particular outbreak is also a result of a build up phenomena that is correlated to vertical transmission efficiency. PMID:28002417

Predicting Rift Valley Fever Inter-epidemic Activities and Outbreak Patterns: Insights from a Stochastic Host-Vector Model.

PubMed

Pedro, Sansao A; Abelman, Shirley; Tonnang, Henri E Z

2016-12-01

Rift Valley fever (RVF) outbreaks are recurrent, occurring at irregular intervals of up to 15 years at least in East Africa. Between outbreaks disease inter-epidemic activities exist and occur at low levels and are maintained by female Aedes mcintoshi mosquitoes which transmit the virus to their eggs leading to disease persistence during unfavourable seasons. Here we formulate and analyse a full stochastic host-vector model with two routes of transmission: vertical and horizontal. By applying branching process theory we establish novel relationships between the basic reproduction number, R0, vertical transmission and the invasion and extinction probabilities. Optimum climatic conditions and presence of mosquitoes have not fully explained the irregular oscillatory behaviour of RVF outbreaks. Using our model without seasonality and applying van Kampen system-size expansion techniques, we provide an analytical expression for the spectrum of stochastic fluctuations, revealing how outbreaks multi-year periodicity varies with the vertical transmission. Our theory predicts complex fluctuations with a dominant period of 1 to 10 years which essentially depends on the efficiency of vertical transmission. Our predictions are then compared to temporal patterns of disease outbreaks in Tanzania, Kenya and South Africa. Our analyses show that interaction between nonlinearity, stochasticity and vertical transmission provides a simple but plausible explanation for the irregular oscillatory nature of RVF outbreaks. Therefore, we argue that while rainfall might be the major determinant for the onset and switch-off of an outbreak, the occurrence of a particular outbreak is also a result of a build up phenomena that is correlated to vertical transmission efficiency.

Computational analysis of Ebolavirus data: prospects, promises and challenges.

PubMed

Michaelis, Martin; Rossman, Jeremy S; Wass, Mark N

2016-08-15

The ongoing Ebola virus (also known as Zaire ebolavirus, a member of the Ebolavirus family) outbreak in West Africa has so far resulted in >28000 confirmed cases compared with previous Ebolavirus outbreaks that affected a maximum of a few hundred individuals. Hence, Ebolaviruses impose a much greater threat than we may have expected (or hoped). An improved understanding of the virus biology is essential to develop therapeutic and preventive measures and to be better prepared for future outbreaks by members of the Ebolavirus family. Computational investigations can complement wet laboratory research for biosafety level 4 pathogens such as Ebolaviruses for which the wet experimental capacities are limited due to a small number of appropriate containment laboratories. During the current West Africa outbreak, sequence data from many Ebola virus genomes became available providing a rich resource for computational analysis. Here, we consider the studies that have already reported on the computational analysis of these data. A range of properties have been investigated including Ebolavirus evolution and pathogenicity, prediction of micro RNAs and identification of Ebolavirus specific signatures. However, the accuracy of the results remains to be confirmed by wet laboratory experiments. Therefore, communication and exchange between computational and wet laboratory researchers is necessary to make maximum use of computational analyses and to iteratively improve these approaches. © 2016 The Author(s). published by Portland Press Limited on behalf of the Biochemical Society.

Application of Humidity Data for Predictions of Influenza Outbreaks.

NASA Astrophysics Data System (ADS)

Teixeira, J.; Thrastarson, H. T.; Yeo, E.

2016-12-01

Seasonal influenza outbreaks infect millions of people, cause hundreds of thousands of deaths worldwide, and leave an immense economic footprint. Potential forecasting of the timing and intensity of these outbreaks can help mitigation and response efforts (e.g., the management and organization of vaccines, drugs and other resources). Absolute (or specific) humidity has been identified as an important driver of the seasonal behavior of influenza outbreaks in temperate regions. Building upon this result, we incorporate humidity data from both NASA's AIRS (Atmospheric Infra-Red Sounder) instrument and ERA-Interim re-analysis into a SIRS (Susceptible-Infectious-Recovered-Susceptible) type numerical epidemiological model, comprising a prediction system for influenza outbreaks. Data for influenza activity is obtained from sources such as Google Flu Trends and the CDC (Center for Disease Control) and used for comparison and assimilation. The accuracy and limitations of the prediction system are tested with hindcasts of outbreaks in the United States for the years 2005-2015. Our results give support to the hypothesis that local weather conditions drive the seasonality of influenza in temperate regions. The implementation of influenza forecasts that make use of NCEP humidity forecasts is also discussed.

US regional tornado outbreaks and their links to spring ENSO phases and North Atlantic SST variability

NASA Astrophysics Data System (ADS)

Lee, Sang-Ki; Wittenberg, Andrew T.; Enfield, David B.; Weaver, Scott J.; Wang, Chunzai; Atlas, Robert

2016-04-01

Recent violent and widespread tornado outbreaks in the US, such as occurred in the spring of 2011, have caused devastating societal impact with significant loss of life and property. At present, our capacity to predict US tornado and other severe weather risk does not extend beyond seven days. In an effort to advance our capability for developing a skillful long-range outlook for US tornado outbreaks, here we investigate the spring probability patterns of US regional tornado outbreaks during 1950-2014. We show that the four dominant springtime El Niño-Southern Oscillation (ENSO) phases (persistent versus early-terminating El Niño and resurgent versus transitioning La Niña) and the North Atlantic sea surface temperature tripole variability are linked to distinct and significant US regional patterns of outbreak probability. These changes in the probability of outbreaks are shown to be largely consistent with remotely forced regional changes in the large-scale atmospheric processes conducive to tornado outbreaks. An implication of these findings is that the springtime ENSO phases and the North Atlantic SST tripole variability may provide seasonal predictability of US regional tornado outbreaks.

Performance of the OraQuick HCV rapid antibody test for screening exposed patients in a hepatitis C outbreak investigation.

PubMed

Gao, Fengxiang; Talbot, Elizabeth A; Loring, Carol H; Power, Jill J; Dionne-Odom, Jodie; Alroy-Preis, Sharon; Jackson, Patricia; Bean, Christine L

2014-07-01

During a nosocomial hepatitis C outbreak, emergency public clinics employed the OraQuick HCV rapid antibody test on site, and all results were verified by a standard enzyme immunoassay (EIA). Of 1,157 persons, 1,149 (99.3%) exhibited concordant results between the two tests (16 positive, 1,133 negative). The sensitivity, specificity, positive predictive value, and negative predictive value were 94.1%, 99.5%, 72.7%, and 99.9%, respectively. OraQuick performed well as a screening test during an outbreak investigation and could be integrated into future hepatitis C virus (HCV) outbreak testing algorithms. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

Contrasting environments associated with storm prediction center tornado outbreak forecasts using synoptic-scale composite analysis

NASA Astrophysics Data System (ADS)

Bates, Alyssa Victoria

Tornado outbreaks have significant human impact, so it is imperative forecasts of these phenomena are accurate. As a synoptic setup lays the foundation for a forecast, synoptic-scale aspects of Storm Prediction Center (SPC) outbreak forecasts of varying accuracy were assessed. The percentages of the number of tornado outbreaks within SPC 10% tornado probability polygons were calculated. False alarm events were separately considered. The outbreaks were separated into quartiles using a point-in-polygon algorithm. Statistical composite fields were created to represent the synoptic conditions of these groups and facilitate comparison. Overall, temperature advection had the greatest differences between the groups. Additionally, there were significant differences in the jet streak strengths and amounts of vertical wind shear. The events forecasted with low accuracy consisted of the weakest synoptic-scale setups. These results suggest it is possible that events with weak synoptic setups should be regarded as areas of concern by tornado outbreak forecasters.

A systematic review of studies on forecasting the dynamics of influenza outbreaks

PubMed Central

Nsoesie, Elaine O; Brownstein, John S; Ramakrishnan, Naren; Marathe, Madhav V

2014-01-01

Forecasting the dynamics of influenza outbreaks could be useful for decision-making regarding the allocation of public health resources. Reliable forecasts could also aid in the selection and implementation of interventions to reduce morbidity and mortality due to influenza illness. This paper reviews methods for influenza forecasting proposed during previous influenza outbreaks and those evaluated in hindsight. We discuss the various approaches, in addition to the variability in measures of accuracy and precision of predicted measures. PubMed and Google Scholar searches for articles on influenza forecasting retrieved sixteen studies that matched the study criteria. We focused on studies that aimed at forecasting influenza outbreaks at the local, regional, national, or global level. The selected studies spanned a wide range of regions including USA, Sweden, Hong Kong, Japan, Singapore, United Kingdom, Canada, France, and Cuba. The methods were also applied to forecast a single measure or multiple measures. Typical measures predicted included peak timing, peak height, daily/weekly case counts, and outbreak magnitude. Due to differences in measures used to assess accuracy, a single estimate of predictive error for each of the measures was difficult to obtain. However, collectively, the results suggest that these diverse approaches to influenza forecasting are capable of capturing specific outbreak measures with some degree of accuracy given reliable data and correct disease assumptions. Nonetheless, several of these approaches need to be evaluated and their performance quantified in real-time predictions. PMID:24373466

A systematic review of studies on forecasting the dynamics of influenza outbreaks.

PubMed

Nsoesie, Elaine O; Brownstein, John S; Ramakrishnan, Naren; Marathe, Madhav V

2014-05-01

Forecasting the dynamics of influenza outbreaks could be useful for decision-making regarding the allocation of public health resources. Reliable forecasts could also aid in the selection and implementation of interventions to reduce morbidity and mortality due to influenza illness. This paper reviews methods for influenza forecasting proposed during previous influenza outbreaks and those evaluated in hindsight. We discuss the various approaches, in addition to the variability in measures of accuracy and precision of predicted measures. PubMed and Google Scholar searches for articles on influenza forecasting retrieved sixteen studies that matched the study criteria. We focused on studies that aimed at forecasting influenza outbreaks at the local, regional, national, or global level. The selected studies spanned a wide range of regions including USA, Sweden, Hong Kong, Japan, Singapore, United Kingdom, Canada, France, and Cuba. The methods were also applied to forecast a single measure or multiple measures. Typical measures predicted included peak timing, peak height, daily/weekly case counts, and outbreak magnitude. Due to differences in measures used to assess accuracy, a single estimate of predictive error for each of the measures was difficult to obtain. However, collectively, the results suggest that these diverse approaches to influenza forecasting are capable of capturing specific outbreak measures with some degree of accuracy given reliable data and correct disease assumptions. Nonetheless, several of these approaches need to be evaluated and their performance quantified in real-time predictions. © 2013 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.

Asynchrony in host and parasite phenology may decrease disease risk in livestock under climate warming: Nematodirus battus in lambs as a case study.

PubMed

Gethings, Owen J; Rose, Hannah; Mitchell, Siân; Van Dijk, Jan; Morgan, Eric R

2015-09-01

Mismatch in the phenology of trophically linked species as a result of climate warming has been shown to have far-reaching effects on animal communities, but implications for disease have so far received limited attention. This paper presents evidence suggestive of phenological asynchrony in a host-parasite system arising from climate change, with impacts on transmission. Diagnostic laboratory data on outbreaks of infection with the pathogenic nematode Nematodirus battus in sheep flocks in the UK were used to validate region-specific models of the effect of spring temperature on parasite transmission. The hatching of parasite eggs to produce infective larvae is driven by temperature, while the availability of susceptible hosts depends on lambing date, which is relatively insensitive to inter-annual variation in spring temperature. In southern areas and in warmer years, earlier emergence of infective larvae in spring was predicted, with decline through mortality before peak availability of susceptible lambs. Data confirmed model predictions, with fewer outbreaks recorded in those years and regions. Overlap between larval peaks and lamb availability was not reduced in northern areas, which experienced no decreases in the number of reported outbreaks. Results suggest that phenological asynchrony arising from climate warming may affect parasite transmission, with non-linear but predictable impacts on disease burden. Improved understanding of complex responses of host-parasite systems to climate change can contribute to effective adaptation of parasite control strategies.

Dengue Contingency Planning: From Research to Policy and Practice.

PubMed

Runge-Ranzinger, Silvia; Kroeger, Axel; Olliaro, Piero; McCall, Philip J; Sánchez Tejeda, Gustavo; Lloyd, Linda S; Hakim, Lokman; Bowman, Leigh R; Horstick, Olaf; Coelho, Giovanini

2016-09-01

Dengue is an increasingly incident disease across many parts of the world. In response, an evidence-based handbook to translate research into policy and practice was developed. This handbook facilitates contingency planning as well as the development and use of early warning and response systems for dengue fever epidemics, by identifying decision-making processes that contribute to the success or failure of dengue surveillance, as well as triggers that initiate effective responses to incipient outbreaks. Available evidence was evaluated using a step-wise process that included systematic literature reviews, policymaker and stakeholder interviews, a study to assess dengue contingency planning and outbreak management in 10 countries, and a retrospective logistic regression analysis to identify alarm signals for an outbreak warning system using datasets from five dengue endemic countries. Best practices for managing a dengue outbreak are provided for key elements of a dengue contingency plan including timely contingency planning, the importance of a detailed, context-specific dengue contingency plan that clearly distinguishes between routine and outbreak interventions, surveillance systems for outbreak preparedness, outbreak definitions, alert algorithms, managerial capacity, vector control capacity, and clinical management of large caseloads. Additionally, a computer-assisted early warning system, which enables countries to identify and respond to context-specific variables that predict forthcoming dengue outbreaks, has been developed. Most countries do not have comprehensive, detailed contingency plans for dengue outbreaks. Countries tend to rely on intensified vector control as their outbreak response, with minimal focus on integrated management of clinical care, epidemiological, laboratory and vector surveillance, and risk communication. The Technical Handbook for Surveillance, Dengue Outbreak Prediction/ Detection and Outbreak Response seeks to provide countries with evidence-based best practices to justify the declaration of an outbreak and the mobilization of the resources required to implement an effective dengue contingency plan.

The Earth Science Vision

NASA Technical Reports Server (NTRS)

Schoeberl, Mark; Rychekewkitsch, Michael; Andrucyk, Dennis; McConaughy, Gail; Meeson, Blanche; Hildebrand, Peter; Einaudi, Franco (Technical Monitor)

2000-01-01

NASA's Earth Science Enterprise's long range vision is to enable the development of a national proactive environmental predictive capability through targeted scientific research and technological innovation. Proactive environmental prediction means the prediction of environmental events and their secondary consequences. These consequences range from disasters and disease outbreak to improved food production and reduced transportation, energy and insurance costs. The economic advantage of this predictive capability will greatly outweigh the cost of development. Developing this predictive capability requires a greatly improved understanding of the earth system and the interaction of the various components of that system. It also requires a change in our approach to gathering data about the earth and a change in our current methodology in processing that data including its delivery to the customers. And, most importantly, it requires a renewed partnership between NASA and its sister agencies. We identify six application themes that summarize the potential of proactive environmental prediction. We also identify four technology themes that articulate our approach to implementing proactive environmental prediction.

Integrated Application of Random Forest and Artificial Neural Network Algorithms to Predict Viral Contamination in Coastal Waters

NASA Astrophysics Data System (ADS)

Shamkhali Chenar, S.; Deng, Z.

2017-12-01

Pathogenic viruses pose a significant public health threat and economic losses to shellfish industry in the coastal environment. Norovirus is a contagious virus and the leading cause of epidemic gastroenteritis following consumption of oysters harvested from sewage-contaminated waters. While it is challenging to detect noroviruses in coastal waters due to the lack of sensitive and routine diagnostic methods, machine learning techniques are allowing us to prevent or at least reduce the risks by developing effective predictive models. This study attempts to develop an algorithm between historical norovirus outbreak reports and environmental parameters including water temperature, solar radiation, water level, salinity, precipitation, and wind. For this purpose, the Random Forests statistical technique was utilized to select relevant environmental parameters and their various combinations with different time lags controlling the virus distribution in oyster harvesting areas along the Louisiana Coast. An Artificial Neural Networks (ANN) approach was then presented to predict the outbreaks using a final set of input variables. Finally, a sensitivity analysis was conducted to evaluate relative importance and contribution of the input variables to the model output. Findings demonstrated that the developed model was capable of reproducing historical oyster norovirus outbreaks along the Louisiana Coast with the overall accuracy of than 99.83%, demonstrating the efficacy of the model. Moreover, the increase in water temperature, solar radiation, water level, and salinity, and the decrease in wind and rainfall are associated with the reduction in the model-predicted risk of norovirus outbreak according to sensitivity analysis results. In conclusion, the presented machine learning approach provided reliable tools for predicting potential norovirus outbreaks and could be used for early detection of possible outbreaks and reduce the risk of norovirus to public health and the seafood industry.

Outbreaks of Tularemia in a Boreal Forest Region Depends on Mosquito Prevalence

PubMed Central

Rydén, Patrik; Björk, Rafael; Schäfer, Martina L.; Lundström, Jan O.; Petersén, Bodil; Lindblom, Anders; Forsman, Mats; Sjöstedt, Anders

2012-01-01

Background. We aimed to evaluate the potential association of mosquito prevalence in a boreal forest area with transmission of the bacterial disease tularemia to humans, and model the annual variation of disease using local weather data. Methods. A prediction model for mosquito abundance was built using weather and mosquito catch data. Then a negative binomial regression model based on the predicted mosquito abundance and local weather data was built to predict annual numbers of humans contracting tularemia in Dalarna County, Sweden. Results. Three hundred seventy humans were diagnosed with tularemia between 1981 and 2007, 94% of them during 7 summer outbreaks. Disease transmission was concentrated along rivers in the area. The predicted mosquito abundance was correlated (0.41, P < .05) with the annual number of human cases. The predicted mosquito peaks consistently preceded the median onset time of human tularemia (temporal correlation, 0.76; P < .05). Our final predictive model included 5 environmental variables and identified 6 of the 7 outbreaks. Conclusions. This work suggests that a high prevalence of mosquitoes in late summer is a prerequisite for outbreaks of tularemia in a tularemia-endemic boreal forest area of Sweden and that environmental variables can be used as risk indicators. PMID:22124130

Predicting St. Louis encephalitis virus epidemics: lessons from recent, and not so recent, outbreaks.

PubMed

Day, J F

2001-01-01

St. Louis encephalitis virus was first identified as the cause of human disease in North America after a large urban epidemic in St. Louis, Missouri, during the summer of 1933. Since then, numerous outbreaks of St. Louis encephalitis have occurred throughout the continent. In south Florida, a 1990 epidemic lasted from August 1990 through January 1991 and resulted in 226 clinical cases and 11 deaths in 28 counties. This epidemic severely disrupted normal activities throughout the southern half of the state for 5 months and adversely impacted tourism in the affected region. The accurate forecasting of mosquito-borne arboviral epidemics will help minimize their impact on urban and rural population centers. Epidemic predictability would help focus control efforts and public education about epidemic risks, transmission patterns, and elements of personal protection that reduce the probability of arboviral infection. Research associated with arboviral outbreaks has provided an understanding of the strengths and weaknesses associated with epidemic prediction. The purpose of this paper is to review lessons from past arboviral epidemics and determine how these observations might aid our ability to predict and respond to future outbreaks.

Environmental Variability and Fluctuation of Coccidioidomycosis (Valley Fever) In California: Based on a New Framework Involving Fungal Life Cycle

NASA Astrophysics Data System (ADS)

Jia, S.; Okin, G. S.; Shafir, S. C.

2013-12-01

Coccidioidomycosis (valley fever), caused by inhalation of spores from pathogenic fungus includingCoccidiodes immitis (C. immitis) and Coccidioides posadasii (C. posadasii), is a disease endemic to arid regions in the southwest US, as well as parts of Central and South America. With a projected increase of drought in this region, an improved understanding of environmental factors behind the outbreaks of coccidioidomycosis will enable the prediction of coccidioidomycosis in a changing climate regime. Previous research shows the infections correlate with climate conditions including precipitation, temperature, and dust. However, most studies focus only on the environmental conditions of fungus growth, which is the first stage in the fungal life cycle. In contrast, we extend the analysis to the following two stages in the life cycle, arthrospore formation and dispersal, to form a better model to predict the disease outbreaks. Besides climate conditions, we use relative spectral mixture analysis (RSMA) based on MODIS MOD43 nadir BRDF adjusted reflectance (NBAR) data to derive the relative dynamics of green vegetation, non-photosynthetic vegetation and bare soil coverage as better indicators of soil moisture, which is important for arthospore formation and dispersal. After detecting the hotspots of disease outbreaks, we correlate seasonal incidence from 2000 to 2010 with the environmental variables zero to eight seasons before to obtain candidates for stepwise regression. Regression result shows a seasonal difference in the leading explanatory variables. Such difference indicates the different seasonal main influential process from fungal life cycle. C. immitis (fungus responsible for coccidioidomycosis outbreaks in California) growth explains outbreaks in winter and fall better than other two stages in the life cycle, while arthospore formation is more responsible for spring and summer outbreaks. As the driest season, summer has the largest area related with arthospore dispersal. The seasonal difference of main influential process relates to the length of lags between the outbreaks and stages in fungal life cycle. During wet seasons of California including winter and fall, outbreaks are less correlated with the short-lag process such as dispersal of arthospores because of high soil moisture. In contrast, the long-lag process like C.immitis growth is influential on outbreaks in wet seasons. The arthospore formation, especially during the latest dry season (with a lag less than one year), is more responsible for outbreaks in spring and summer, when the influence of C. immitis growth is dampened by time. However, arthospores formed and preserved years ago may introduce uncertainty to the seasonal lag patterns. The long lags also exist in outbreaks related to arthospore formation. By including all three stages of fungal life cycle, we formed a more comprehensive framework in explaining the relationship between environmental conditions and disease outbreaks. Such analysis can be extended to a finer temporal resolution (e.g. per month) to obtain a clearer picture between environmental variability and coccidioidomycosis fluctuation.

Action Tweets Linked to Reduced County-Level HIV Prevalence in the United States: Online Messages and Structural Determinants

PubMed Central

Ireland, Molly E.; Chen, Qijia; Schwartz, H. Andrew; Ungar, Lyle H.; Albarracin, Dolores

2016-01-01

HIV is uncommon in most US counties but travels quickly through vulnerable communities when it strikes. Tracking behavior through social media may provide an unobtrusive, naturalistic means of predicting HIV outbreaks and understanding the behavioral and psychological factors that increase communities'; risk. General action goals, or the motivation to engage in cognitive and motor activity, may support protective health behavior (e.g., using condoms) or encourage activity indiscriminately (e.g., risky sex), resulting in mixed health effects. We explored these opposing hypotheses by regressing county-level HIV prevalence on action language (e.g., work, plan) in over 150 million tweets mapped to US counties. Controlling for demographic and structural predictors of HIV, more active language was associated with lower HIV rates. By leveraging language used on social media to improve existing predictive models of geographic variation in HIV, future targeted HIV-prevention interventions may have a better chance of reaching high-risk communities before outbreaks occur. PMID:26650382

Forecasting fluctuating outbreaks in seasonally driven epidemics

NASA Astrophysics Data System (ADS)

Stone, Lewi

2009-03-01

Seasonality is a driving force that has major impact on the spatio-temporal dynamics of natural systems and their populations. This is especially true for the transmission of common infectious diseases such as influenza, measles, chickenpox, and pertussis. Here we gain new insights into the nonlinear dynamics of recurrent diseases through the analysis of the classical seasonally forced SIR epidemic model. Despite many efforts over the last decades, it has been difficult to gain general analytical insights because of the complex synchronization effects that can evolve between the external forcing and the model's natural oscillations. The analysis advanced here attempts to make progress in this direction by focusing on the dynamics of ``skips'' where we identify and predict years in which the epidemic is absent rather than outbreak years. Skipping events are intrinsic to the forced SIR model when parameterised in the chaotic regime. In fact, it is difficult if not impossible to locate realistic chaotic parameter regimes in which outbreaks occur regularly each year. This contrasts with the well known Rossler oscillator whose outbreaks recur regularly but whose amplitude vary chaotically in time (Uniform Phase Chaotic Amplitude oscillations). The goal of the present study is to develop a ``language of skips'' that makes it possible to predict under what conditions the next outbreak is likely to occur, and how many ``skips'' might be expected after any given outbreak. We identify a new threshold effect and give clear analytical conditions that allow accurate predictions. Moreover, the time of occurrence (i.e., phase) of an outbreak proves to be a useful new parameter that carries important epidemiological information. In forced systems, seasonal changes can prevent late-initiating outbreaks (i.e., having high phase) from running to completion. These principles yield forecasting tools that should have relevance for the study of newly emerging and reemerging diseases.

Decay fungi associated with oaks and other hardwoods in the western United States

Treesearch

Jessie A. Glaeser; Kevin T. Smith

2010-01-01

An assessment of the presence and extent of the wood decay process should be part of any hazard tree analysis. Identification of the fungi responsible for decay improves both the prediction of the consequences of wood decay and the prescription of management options including tree pruning or removal. Until the outbreak of Sudden Oak Death (SOD), foresters in the...

Using Baidu Search Index to Predict Dengue Outbreak in China

NASA Astrophysics Data System (ADS)

Liu, Kangkang; Wang, Tao; Yang, Zhicong; Huang, Xiaodong; Milinovich, Gabriel J.; Lu, Yi; Jing, Qinlong; Xia, Yao; Zhao, Zhengyang; Yang, Yang; Tong, Shilu; Hu, Wenbiao; Lu, Jiahai

2016-12-01

This study identified the possible threshold to predict dengue fever (DF) outbreaks using Baidu Search Index (BSI). Time-series classification and regression tree models based on BSI were used to develop a predictive model for DF outbreak in Guangzhou and Zhongshan, China. In the regression tree models, the mean autochthonous DF incidence rate increased approximately 30-fold in Guangzhou when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 382. When the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 91.8, there was approximately 9-fold increase of the mean autochthonous DF incidence rate in Zhongshan. In the classification tree models, the results showed that when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 99.3, there was 89.28% chance of DF outbreak in Guangzhou, while, in Zhongshan, when the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 68.1, the chance of DF outbreak rose up to 100%. The study indicated that less cost internet-based surveillance systems can be the valuable complement to traditional DF surveillance in China.

Long Range River Discharge Forecasting Using the Gravity Recovery and Climate Experiment (GRACE) Satellite to Predict Conditions for Endemic Cholera

NASA Astrophysics Data System (ADS)

Jutla, A.; Akanda, A. S.; Colwell, R. R.

2014-12-01

Prediction of conditions of an impending disease outbreak remains a challenge but is achievable if the associated and appropriate large scale hydroclimatic process can be estimated in advance. Outbreaks of diarrheal diseases such as cholera, are related to episodic seasonal variability in river discharge in the regions where water and sanitation infrastructure are inadequate and insufficient. However, forecasting river discharge, few months in advance, remains elusive where cholera outbreaks are frequent, probably due to non-availability of geophysical data as well as transboundary water stresses. Here, we show that satellite derived water storage from Gravity Recovery and Climate Experiment Forecasting (GRACE) sensors can provide reliable estimates on river discharge atleast two months in advance over regional scales. Bayesian regression models predicted flooding and drought conditions, a prerequisite for cholera outbreaks, in Bengal Delta with an overall accuracy of 70% for upto 60 days in advance without using any other ancillary ground based data. Forecasting of river discharge will have significant impacts on planning and designing intervention strategies for potential cholera outbreaks in the coastal regions where the disease remain endemic and often fatal.

Spatial Pattern of Attacks of the Invasive Woodwasp Sirex noctilio, at Landscape and Stand Scales.

PubMed

Lantschner, M Victoria; Corley, Juan C

2015-01-01

Invasive insect pests are responsible for important damage to native and plantation forests, when population outbreaks occur. Understanding the spatial pattern of attacks by forest pest populations is essential to improve our understanding of insect population dynamics and for predicting attack risk by invasives or planning pest management strategies. The woodwasp Sirex noctilio is an invasive woodwasp that has become probably the most important pest of pine plantations in the Southern Hemisphere. Our aim was to study the spatial dynamics of S. noctilio populations in Southern Argentina. Specifically we describe: (1) the spatial patterns of S. noctilio outbreaks and their relation with environmental factors at a landscape scale; and (2) characterize the spatial pattern of attacked trees at the stand scale. We surveyed the spatial distribution of S. noctilio outbreaks in three pine plantation landscapes, and we assessed potential associations with topographic variables, habitat characteristics, and distance to other outbreaks. We also looked at the spatial distribution of attacked trees in 20 stands with different levels of infestation, and assessed the relationship of attacks with stand composition and management. We found that the spatial pattern of pine stands with S. noctilio outbreaks at the landscape scale is influenced mainly by the host species present, slope aspect, and distance to other outbreaks. At a stand scale, there is strong aggregation of attacked trees in stands with intermediate infestation levels, and the degree of attacks is influenced by host species and plantation management. We conclude that the pattern of S. noctilio damage at different spatial scales is influenced by a combination of both inherent population dynamics and the underlying patterns of environmental factors. Our results have important implications for the understanding and management of invasive insect outbreaks in forest systems.

Links between type E botulism outbreaks, lake levels, and surface water temperatures in Lake Michigan, 1963-2008

USGS Publications Warehouse

Lafrancois, Brenda Moraska; Riley, Stephen C.; Blehert, David S.; Ballmann, Anne E.

2011-01-01

Relationships between large-scale environmental factors and the incidence of type E avian botulism outbreaks in Lake Michigan were examined from 1963 to 2008. Avian botulism outbreaks most frequently occurred in years with low mean annual water levels, and lake levels were significantly lower in outbreak years than in non-outbreak years. Mean surface water temperatures in northern Lake Michigan during the period when type E outbreaks tend to occur (July through September) were significantly higher in outbreak years than in non-outbreak years. Trends in fish populations did not strongly correlate with botulism outbreaks, although botulism outbreaks in the 1960s coincided with high alewife abundance, and recent botulism outbreaks coincided with rapidly increasing round goby abundance. Botulism outbreaks occurred cyclically, and the frequency of outbreaks did not increase over the period of record. Climate change scenarios for the Great Lakes predict lower water levels and warmer water temperatures. As a consequence, the frequency and magnitude of type E botulism outbreaks in the Great Lakes may increase.

Chapter 3. Genetic variation in Dendroctonus frontalis, within and between populations

Treesearch

Jane Leslie Hayes

1999-01-01

Many species of Dendroctonus, particularly the so-called aggressive species, are notorious outbreak organisms with more or less predictable or characteristic, cyclic patterns of outbreak locally if not regionally. D. frontalis, for example, exhibits an approximately 7-10 year cycle with 2-3 year duration of outbreak. The last...

Phylogeny of Yellow Fever Virus, Uganda, 2016.

PubMed

Hughes, Holly R; Kayiwa, John; Mossel, Eric C; Lutwama, Julius; Staples, J Erin; Lambert, Amy J

2018-08-17

In April 2016, a yellow fever outbreak was detected in Uganda. Removal of contaminating ribosomal RNA in a clinical sample improved the sensitivity of next-generation sequencing. Molecular analyses determined the Uganda yellow fever outbreak was distinct from the concurrent yellow fever outbreak in Angola, improving our understanding of yellow fever epidemiology.

The effects of school closures on influenza outbreaks and pandemics: systematic review of simulation studies.

PubMed

Jackson, Charlotte; Mangtani, Punam; Hawker, Jeremy; Olowokure, Babatunde; Vynnycky, Emilia

2014-01-01

School closure is a potential intervention during an influenza pandemic and has been investigated in many modelling studies. To systematically review the effects of school closure on influenza outbreaks as predicted by simulation studies. We searched Medline and Embase for relevant modelling studies published by the end of October 2012, and handsearched key journals. We summarised the predicted effects of school closure on the peak and cumulative attack rates and the duration of the epidemic. We investigated how these predictions depended on the basic reproduction number, the timing and duration of closure and the assumed effects of school closures on contact patterns. School closures were usually predicted to be most effective if they caused large reductions in contact, if transmissibility was low (e.g. a basic reproduction number <2), and if attack rates were higher in children than in adults. The cumulative attack rate was expected to change less than the peak, but quantitative predictions varied (e.g. reductions in the peak were frequently 20-60% but some studies predicted >90% reductions or even increases under certain assumptions). This partly reflected differences in model assumptions, such as those regarding population contact patterns. Simulation studies suggest that school closure can be a useful control measure during an influenza pandemic, particularly for reducing peak demand on health services. However, it is difficult to accurately quantify the likely benefits. Further studies of the effects of reactive school closures on contact patterns are needed to improve the accuracy of model predictions.

Internet and free press are associated with reduced lags in global outbreak reporting.

PubMed

McAlarnen, Lindsey; Smith, Katherine; Brownstein, John S; Jerde, Christopher

2014-10-30

Global outbreak detection and reporting have generally improved for a variety of infectious diseases and geographic regions in recent decades. Nevertheless, lags in outbreak reporting remain a threat to the global human health and economy. In the time between first occurrence of a novel disease incident and public notification of an outbreak, infected individuals have a greater possibility of traveling and spreading the pathogen to other nations. Shortening outbreak reporting lags has the potential to improve global health by preventing local outbreaks from escalating into global epidemics. Reporting lags between the first record and the first public report of an event were calculated for 318 outbreaks occurring 1996-2009. The influence of freedom of the press, Internet usage, per capita health expenditure, and cell phone subscriptions, on the timeliness of outbreak reporting was evaluated. Freer presses and increasing Internet usage correlate with reduced time between the first record of an outbreak and the public report. Increasing Internet usage reduced the expected reporting lag from more than one month in nations without Internet users to one day in those where 75 of 100 people use the Internet. Advances in technology and the emergence of more open and free governments are associated with to improved global infectious disease surveillance.

Dengue Contingency Planning: From Research to Policy and Practice

PubMed Central

Runge-Ranzinger, Silvia; Kroeger, Axel; Olliaro, Piero; McCall, Philip J.; Sánchez Tejeda, Gustavo; Lloyd, Linda S.; Hakim, Lokman; Bowman, Leigh R.; Horstick, Olaf; Coelho, Giovanini

2016-01-01

Background Dengue is an increasingly incident disease across many parts of the world. In response, an evidence-based handbook to translate research into policy and practice was developed. This handbook facilitates contingency planning as well as the development and use of early warning and response systems for dengue fever epidemics, by identifying decision-making processes that contribute to the success or failure of dengue surveillance, as well as triggers that initiate effective responses to incipient outbreaks. Methodology/Principal findings Available evidence was evaluated using a step-wise process that included systematic literature reviews, policymaker and stakeholder interviews, a study to assess dengue contingency planning and outbreak management in 10 countries, and a retrospective logistic regression analysis to identify alarm signals for an outbreak warning system using datasets from five dengue endemic countries. Best practices for managing a dengue outbreak are provided for key elements of a dengue contingency plan including timely contingency planning, the importance of a detailed, context-specific dengue contingency plan that clearly distinguishes between routine and outbreak interventions, surveillance systems for outbreak preparedness, outbreak definitions, alert algorithms, managerial capacity, vector control capacity, and clinical management of large caseloads. Additionally, a computer-assisted early warning system, which enables countries to identify and respond to context-specific variables that predict forthcoming dengue outbreaks, has been developed. Conclusions/Significance Most countries do not have comprehensive, detailed contingency plans for dengue outbreaks. Countries tend to rely on intensified vector control as their outbreak response, with minimal focus on integrated management of clinical care, epidemiological, laboratory and vector surveillance, and risk communication. The Technical Handbook for Surveillance, Dengue Outbreak Prediction/ Detection and Outbreak Response seeks to provide countries with evidence-based best practices to justify the declaration of an outbreak and the mobilization of the resources required to implement an effective dengue contingency plan. PMID:27653786

Forecasting seasonal outbreaks of influenza.

PubMed

Shaman, Jeffrey; Karspeck, Alicia

2012-12-11

Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003-2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza.

Forecasting seasonal outbreaks of influenza

PubMed Central

Shaman, Jeffrey; Karspeck, Alicia

2012-01-01

Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003–2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza. PMID:23184969

ID-Viewer: a visual analytics architecture for infectious diseases surveillance and response management in Pakistan.

PubMed

Ali, M A; Ahsan, Z; Amin, M; Latif, S; Ayyaz, A; Ayyaz, M N

2016-05-01

Globally, disease surveillance systems are playing a significant role in outbreak detection and response management of Infectious Diseases (IDs). However, in developing countries like Pakistan, epidemic outbreaks are difficult to detect due to scarcity of public health data and absence of automated surveillance systems. Our research is intended to formulate an integrated service-oriented visual analytics architecture for ID surveillance, identify key constituents and set up a baseline for easy reproducibility of such systems in the future. This research focuses on development of ID-Viewer, which is a visual analytics decision support system for ID surveillance. It is a blend of intelligent approaches to make use of real-time streaming data from Emergency Departments (EDs) for early outbreak detection, health care resource allocation and epidemic response management. We have developed a robust service-oriented visual analytics architecture for ID surveillance, which provides automated mechanisms for ID data acquisition, outbreak detection and epidemic response management. Classification of chief-complaints is accomplished using dynamic classification module, which employs neural networks and fuzzy-logic to categorize syndromes. Standard routines by Center for Disease Control (CDC), i.e. c1-c3 (c1-mild, c2-medium and c3-ultra), and spatial scan statistics are employed for detection of temporal and spatio-temporal disease outbreaks respectively. Prediction of imminent disease threats is accomplished using support vector regression for early warnings and response planning. Geographical visual analytics displays are developed that allow interactive visualization of syndromic clusters, monitoring disease spread patterns, and identification of spatio-temporal risk zones. We analysed performance of surveillance framework using ID data for year 2011-2015. Dynamic syndromic classifier is able to classify chief-complaints to appropriate syndromes with high classification accuracy. Outbreak detection methods are able to detect the ID outbreaks in start of epidemic time zones. Prediction model is able to forecast dengue trend for 20 weeks ahead with nominal normalized root mean square error of 0.29. Interactive geo-spatiotemporal displays, i.e. heat-maps, and choropleth are shown in respective sections. The proposed framework will set a standard and provide necessary details for future implementation of such a system for resource-constrained regions. It will improve early outbreak detection attributable to natural and man-made biological threats, monitor spatio-temporal epidemic trends and provide assurance that an outbreak has, or has not occurred. Advanced analytics features will be beneficial in timely organization/formulation of health management policies, disease control activities and efficient health care resource allocation. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Forecasting system predicts presence of sea nettles in Chesapeake Bay

NASA Astrophysics Data System (ADS)

Brown, Christopher W.; Hood, Raleigh R.; Li, Zhen; Decker, Mary Beth; Gross, Thomas F.; Purcell, Jennifer E.; Wang, Harry V.

Outbreaks of noxious biota, which occur in both aquatic and terrestrial systems, can have considerable negative economic impacts. For example, an increasing frequency of harmful algal blooms worldwide has negatively affected the tourism industry in many regions. Such impacts could be mitigated if the conditions that give rise to these outbreaks were known and could be monitored. Recent advances in technology and communications allow us to continuously measure and model many environmental factors that are responsible for outbreaks of certain noxious organisms. A new prototype ecological forecasting system predicts the likelihood of occurrence of the sea nettle (Chrysaora quinquecirrha), a stinging jellyfish, in the Chesapeake Bay.

Predicting susceptibility of white fir during a drought-associated outbreak of the fir engraver, Scolytus centralis, in California

Treesearch

G.T. Ferrell; W.J. Otrosina; C.J. DeMars

1994-01-01

Phenotypic traits were compared with a vigor (growth efficiency) index for accuracy in predicting susceptibility of white fir, Abies concolor (Gord. & Glend.) Lindl., during a drought-associated outbreak of the fir engraver, Scolytus centralis LeC., in the central Sierra Nevada at Lake Tahoe, California.Predictor variables were estimated for 633 firs in six forest...

Faster Detection of Poliomyelitis Outbreaks to Support Polio Eradication

PubMed Central

Chenoweth, Paul; Okayasu, Hiro; Donnelly, Christl A.; Aylward, R. Bruce; Grassly, Nicholas C.

2016-01-01

As the global eradication of poliomyelitis approaches the final stages, prompt detection of new outbreaks is critical to enable a fast and effective outbreak response. Surveillance relies on reporting of acute flaccid paralysis (AFP) cases and laboratory confirmation through isolation of poliovirus from stool. However, delayed sample collection and testing can delay outbreak detection. We investigated whether weekly testing for clusters of AFP by location and time, using the Kulldorff scan statistic, could provide an early warning for outbreaks in 20 countries. A mixed-effects regression model was used to predict background rates of nonpolio AFP at the district level. In Tajikistan and Congo, testing for AFP clusters would have resulted in an outbreak warning 39 and 11 days, respectively, before official confirmation of large outbreaks. This method has relatively high specificity and could be integrated into the current polio information system to support rapid outbreak response activities. PMID:26890053

Faster Detection of Poliomyelitis Outbreaks to Support Polio Eradication.

PubMed

Blake, Isobel M; Chenoweth, Paul; Okayasu, Hiro; Donnelly, Christl A; Aylward, R Bruce; Grassly, Nicholas C

2016-03-01

As the global eradication of poliomyelitis approaches the final stages, prompt detection of new outbreaks is critical to enable a fast and effective outbreak response. Surveillance relies on reporting of acute flaccid paralysis (AFP) cases and laboratory confirmation through isolation of poliovirus from stool. However, delayed sample collection and testing can delay outbreak detection. We investigated whether weekly testing for clusters of AFP by location and time, using the Kulldorff scan statistic, could provide an early warning for outbreaks in 20 countries. A mixed-effects regression model was used to predict background rates of nonpolio AFP at the district level. In Tajikistan and Congo, testing for AFP clusters would have resulted in an outbreak warning 39 and 11 days, respectively, before official confirmation of large outbreaks. This method has relatively high specificity and could be integrated into the current polio information system to support rapid outbreak response activities.

Framework for evaluating public health surveillance systems for early detection of outbreaks: recommendations from the CDC Working Group.

PubMed

Buehler, James W; Hopkins, Richard S; Overhage, J Marc; Sosin, Daniel M; Tong, Van

2004-05-07

The threat of terrorism and high-profile disease outbreaks has drawn attention to public health surveillance systems for early detection of outbreaks. State and local health departments are enhancing existing surveillance systems and developing new systems to better detect outbreaks through public health surveillance. However, information is limited about the usefulness of surveillance systems for outbreak detection or the best ways to support this function. This report supplements previous guidelines for evaluating public health surveillance systems. Use of this framework is intended to improve decision-making regarding the implementation of surveillance for outbreak detection. Use of a standardized evaluation methodology, including description of system design and operation, also will enhance the exchange of information regarding methods to improve early detection of outbreaks. The framework directs particular attention to the measurement of timeliness and validity for outbreak detection. The evaluation framework is designed to support assessment and description of all surveillance approaches to early detection, whether through traditional disease reporting, specialized analytic routines for aberration detection, or surveillance using early indicators of disease outbreaks, such as syndromic surveillance.

Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data

PubMed Central

McGough, Sarah F.; Brownstein, John S.; Hawkins, Jared B.; Santillana, Mauricio

2017-01-01

Background Over 400,000 people across the Americas are thought to have been infected with Zika virus as a consequence of the 2015–2016 Latin American outbreak. Official government-led case count data in Latin America are typically delayed by several weeks, making it difficult to track the disease in a timely manner. Thus, timely disease tracking systems are needed to design and assess interventions to mitigate disease transmission. Methodology/Principal Findings We combined information from Zika-related Google searches, Twitter microblogs, and the HealthMap digital surveillance system with historical Zika suspected case counts to track and predict estimates of suspected weekly Zika cases during the 2015–2016 Latin American outbreak, up to three weeks ahead of the publication of official case data. We evaluated the predictive power of these data and used a dynamic multivariable approach to retrospectively produce predictions of weekly suspected cases for five countries: Colombia, El Salvador, Honduras, Venezuela, and Martinique. Models that combined Google (and Twitter data where available) with autoregressive information showed the best out-of-sample predictive accuracy for 1-week ahead predictions, whereas models that used only Google and Twitter typically performed best for 2- and 3-week ahead predictions. Significance Given the significant delay in the release of official government-reported Zika case counts, we show that these Internet-based data streams can be used as timely and complementary ways to assess the dynamics of the outbreak. PMID:28085877

Association genetics of oleoresin flow in loblolly pine: discovering genes and predicting phenotype for improved resistance to bark beetles and bioenergy potential

Treesearch

Jared W. Westbrook; Marcio F. R. Resende Jr.; Patricio Munoz; Alejandro R. Walker; Jill L. Wegrzyn; C. Dana Nelson; David B. Neale; Matias Kirst; Salvador A. Gezan; Gary F. Peter; John M. Davis

2013-01-01

In the last decade, outbreaks of bark beetles in coniferous forests of North America have caused unprecedented tree mortality and economic losses (Nowak et al., 2008; van Mantgem et al., 2009; Waring et al., 2009), converting forests that were previously atmospheric carbon sinks into carbon sources (Kurz et al., 2008). Native species of bark beetle rapidly kill healthy...

Forecasting disease risk for increased epidemic preparedness in public health

NASA Technical Reports Server (NTRS)

Myers, M. F.; Rogers, D. J.; Cox, J.; Flahault, A.; Hay, S. I.

2000-01-01

Emerging infectious diseases pose a growing threat to human populations. Many of the world's epidemic diseases (particularly those transmitted by intermediate hosts) are known to be highly sensitive to long-term changes in climate and short-term fluctuations in the weather. The application of environmental data to the study of disease offers the capability to demonstrate vector-environment relationships and potentially forecast the risk of disease outbreaks or epidemics. Accurate disease forecasting models would markedly improve epidemic prevention and control capabilities. This chapter examines the potential for epidemic forecasting and discusses the issues associated with the development of global networks for surveillance and prediction. Existing global systems for epidemic preparedness focus on disease surveillance using either expert knowledge or statistical modelling of disease activity and thresholds to identify times and areas of risk. Predictive health information systems would use monitored environmental variables, linked to a disease system, to be observed and provide prior information of outbreaks. The components and varieties of forecasting systems are discussed with selected examples, along with issues relating to further development.

Forecasting Disease Risk for Increased Epidemic Preparedness in Public Health

PubMed Central

Myers, M.F.; Rogers, D.J.; Cox, J.; Flahault, A.; Hay, S.I.

2011-01-01

Emerging infectious diseases pose a growing threat to human populations. Many of the world’s epidemic diseases (particularly those transmitted by intermediate hosts) are known to be highly sensitive to long-term changes in climate and short-term fluctuations in the weather. The application of environmental data to the study of disease offers the capability to demonstrate vector–environment relationships and potentially forecast the risk of disease outbreaks or epidemics. Accurate disease forecasting models would markedly improve epidemic prevention and control capabilities. This chapter examines the potential for epidemic forecasting and discusses the issues associated with the development of global networks for surveillance and prediction. Existing global systems for epidemic preparedness focus on disease surveillance using either expert knowledge or statistical modelling of disease activity and thresholds to identify times and areas of risk. Predictive health information systems would use monitored environmental variables, linked to a disease system, to be observed and provide prior information of outbreaks. The components and varieties of forecasting systems are discussed with selected examples, along with issues relating to further development. PMID:10997211

Performance of Rapid Influenza Diagnostic Testing in Outbreak Settings

PubMed Central

Winter, Anne-Luise; King, Eddie-Chong; Blair, Joanne; Gubbay, Jonathan B.

2014-01-01

Rapid influenza diagnostic tests (RIDTs) may be useful during institutional respiratory disease outbreaks to identify influenza and enable antivirals to be rapidly administered to patients and for the prophylactic treatment of those exposed to the virus but not yet symptomatic. The performance of RIDTs at the outbreak level is not well documented in the literature. This study aimed to evaluate the performance of RIDTs in comparison with that of real-time reverse transcription (rRT)-PCR in the context of institutional respiratory disease outbreaks. This study included outbreak-related respiratory specimens tested for influenza virus at Public Health Ontario Laboratories by both RIDT and rRT-PCR, from 1 September 2010 to 30 April 2013. At the outbreak level, performance testing of RIDTs compared to rRT-PCR for the detection of any influenza virus type demonstrated an overall sensitivity of 76.5%, a specificity of 99.7%, a positive predictive value (PPV) of 99.5%, and a negative predictive value of 85.3%. Because of their high specificity and PPV, even outside of the influenza season, RIDTs can play a role in screening for influenza virus in outbreaks and instituting antiviral therapy in a timely manner when positive. RIDTs can also be useful in remote settings where molecular virology testing is not easily accessible. Suboptimal sensitivity of RIDTs can be addressed by the use of molecular testing. PMID:25320225

Performance of rapid influenza diagnostic testing in outbreak settings.

PubMed

Peci, Adriana; Winter, Anne-Luise; King, Eddie-Chong; Blair, Joanne; Gubbay, Jonathan B

2014-12-01

Rapid influenza diagnostic tests (RIDTs) may be useful during institutional respiratory disease outbreaks to identify influenza and enable antivirals to be rapidly administered to patients and for the prophylactic treatment of those exposed to the virus but not yet symptomatic. The performance of RIDTs at the outbreak level is not well documented in the literature. This study aimed to evaluate the performance of RIDTs in comparison with that of real-time reverse transcription (rRT)-PCR in the context of institutional respiratory disease outbreaks. This study included outbreak-related respiratory specimens tested for influenza virus at Public Health Ontario Laboratories by both RIDT and rRT-PCR, from 1 September 2010 to 30 April 2013. At the outbreak level, performance testing of RIDTs compared to rRT-PCR for the detection of any influenza virus type demonstrated an overall sensitivity of 76.5%, a specificity of 99.7%, a positive predictive value (PPV) of 99.5%, and a negative predictive value of 85.3%. Because of their high specificity and PPV, even outside of the influenza season, RIDTs can play a role in screening for influenza virus in outbreaks and instituting antiviral therapy in a timely manner when positive. RIDTs can also be useful in remote settings where molecular virology testing is not easily accessible. Suboptimal sensitivity of RIDTs can be addressed by the use of molecular testing. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

Strategies for Controlling Non-Transmissible Infection Outbreaks Using a Large Human Movement Data Set

PubMed Central

Hancock, Penelope A.; Rehman, Yasmin; Hall, Ian M.; Edeghere, Obaghe; Danon, Leon; House, Thomas A.; Keeling, Matthew J.

2014-01-01

Prediction and control of the spread of infectious disease in human populations benefits greatly from our growing capacity to quantify human movement behavior. Here we develop a mathematical model for non-transmissible infections contracted from a localized environmental source, informed by a detailed description of movement patterns of the population of Great Britain. The model is applied to outbreaks of Legionnaires' disease, a potentially life-threatening form of pneumonia caused by the bacteria Legionella pneumophilia. We use case-report data from three recent outbreaks that have occurred in Great Britain where the source has already been identified by public health agencies. We first demonstrate that the amount of individual-level heterogeneity incorporated in the movement data greatly influences our ability to predict the source location. The most accurate predictions were obtained using reported travel histories to describe movements of infected individuals, but using detailed simulation models to estimate movement patterns offers an effective fast alternative. Secondly, once the source is identified, we show that our model can be used to accurately determine the population likely to have been exposed to the pathogen, and hence predict the residential locations of infected individuals. The results give rise to an effective control strategy that can be implemented rapidly in response to an outbreak. PMID:25211122

Modeling the geographic distribution of Bacillus anthracis, the causative agent of anthrax disease, for the contiguous United States using predictive ecological [corrected] niche modeling.

PubMed

Blackburn, Jason K; McNyset, Kristina M; Curtis, Andrew; Hugh-Jones, Martin E

2007-12-01

The ecology and distribution of Bacillus anthracis is poorly understood despite continued anthrax outbreaks in wildlife and livestock throughout the United States. Little work is available to define the potential environments that may lead to prolonged spore survival and subsequent outbreaks. This study used the genetic algorithm for rule-set prediction modeling system to model the ecological niche for B. anthracis in the contiguous United States using wildlife and livestock outbreaks and several environmental variables. The modeled niche is defined by a narrow range of normalized difference vegetation index, precipitation, and elevation, with the geographic distribution heavily concentrated in a narrow corridor from southwest Texas northward into the Dakotas and Minnesota. Because disease control programs rely on vaccination and carcass disposal, and vaccination in wildlife remains untenable, understanding the distribution of B. anthracis plays an important role in efforts to prevent/eradicate the disease. Likewise, these results potentially aid in differentiating endemic/natural outbreaks from industrial-contamination related outbreaks or bioterrorist attacks.

An examination of the predictors of blood donors' intentions to donate during two phases of an avian influenza outbreak.

PubMed

Masser, Barbara M; White, Katherine M; Hamilton, Kyra; McKimmie, Blake M

2011-03-01

Data from prior health scares suggest that an avian influenza outbreak will impact on people's intention to donate blood; however, research exploring this is scarce. Using an augmented theory of planned behavior (TPB), incorporating threat perceptions alongside the rational decision-making components of the TPB, the current study sought to identify predictors of blood donors' intentions to donate during two phases of an avian influenza outbreak. Blood donors (n = 172) completed an on-line survey assessing the standard TPB predictors as well as measures of threat perceptions from the health belief model (i.e., perceived susceptibility and severity). Path analyses examined the utility of the augmented TPB to predict donors' intentions to donate during a low- and high-risk phase of an avian influenza outbreak. In both phases, the model provided a good fit to the data explaining 69% (low risk) and 72% (high risk) of the variance in intentions. Attitude, subjective norm, and perceived susceptibility significantly predicted donor intentions in both phases. Within the low-risk phase, sex was an additional significant predictor of intention, while in the high-risk phase, perceived behavioral control was significantly related to intentions. An augmented TPB model can be used to predict donors' intentions to donate blood in a low-risk and a high-risk phase of an outbreak of avian influenza. As such, the results provide important insights into donors' decision-making that can be used by blood agencies to maintain the blood supply in the context of an avian influenza outbreak. © 2010 American Association of Blood Banks.

Dynamic Forecasting of Zika Epidemics Using Google Trends

PubMed Central

Jin, Yuan; Huang, Yong; Lin, Baihan; An, Xiaoping; Feng, Dan; Tong, Yigang

2017-01-01

We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.001). Then, we used the correlation data from Zika-related online search in GTs and ZIKV epidemics between 12 February and 20 October 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the predicted data by ARIMA model, which used the online search data as the external regressor to enhance the forecasting model and assist the historical epidemic data in improving the quality of the predictions, are quite similar to the actual data during ZIKV epidemic early November 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks. PMID:28060809

Dynamic Forecasting of Zika Epidemics Using Google Trends.

PubMed

Teng, Yue; Bi, Dehua; Xie, Guigang; Jin, Yuan; Huang, Yong; Lin, Baihan; An, Xiaoping; Feng, Dan; Tong, Yigang

2017-01-01

We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.001). Then, we used the correlation data from Zika-related online search in GTs and ZIKV epidemics between 12 February and 20 October 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the predicted data by ARIMA model, which used the online search data as the external regressor to enhance the forecasting model and assist the historical epidemic data in improving the quality of the predictions, are quite similar to the actual data during ZIKV epidemic early November 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks.

Risk factors associated with anthrax outbreak in animals in North Dakota, 2005: a retrospective case-control study.

PubMed

Mongoh, Mafany Ndiva; Dyer, Neil W; Stoltenow, Charles L; Khaitsa, Margaret L

2008-01-01

We identified the risk factors associated with the anthrax outbreak Of 2005 in animals in North Dakota. Medical records of the 2005 anthrax outbreak were obtained from the Veterinary Diagnostic Laboratory at North Dakota State University. Additional data were obtained from the North Dakota state veterinarian's office, and supplemental questionnaires were administered to producers. The data obtained included ecological and environmental factors, animal health factors, and management factors. Anthrax occurred from July 1 to October 12, 2005. The cases were located in eastern North Dakota around the Red River Basin. Ransom, LaMoure, and Barnes counties reported most cases (71%). Species affected included cattle, bison, horses, sheep, elk, deer, pigs, and llamas. The predominant symptom was sudden death (38%) followed by bleeding from orifices (17%). Chi-square analysis indicated significant differences between case and control premises on the following variables: death reported on neighboring pasture, vaccination period, dry conditions, wet conditions, antibiotic use, multiple vaccination, and type of predator (coyote). Factors that significantly (p<0.05) predicted anthrax occurrences on the final logistic regression model were vaccination, use of antibiotics during an outbreak, and period of vaccine administration (before or during the outbreak). The characteristics of the anthrax outbreak regarding time and place of occurrence, animals affected, clinical signs reported, and mortality rate were consistent with previous reports of natural anthrax outbreaks in animals. A number of factors that significantly predicted anthrax occurrence in animals in the 2005 outbreak in North Dakota were identified. This information is important in planning appropriate control and prevention measures for anthrax, including recommending the right vaccination and treatment regimens in managing future anthrax outbreaks.

Measuring populations to improve vaccination coverage

NASA Astrophysics Data System (ADS)

Bharti, Nita; Djibo, Ali; Tatem, Andrew J.; Grenfell, Bryan T.; Ferrari, Matthew J.

2016-10-01

In low-income settings, vaccination campaigns supplement routine immunization but often fail to achieve coverage goals due to uncertainty about target population size and distribution. Accurate, updated estimates of target populations are rare but critical; short-term fluctuations can greatly impact population size and susceptibility. We use satellite imagery to quantify population fluctuations and the coverage achieved by a measles outbreak response vaccination campaign in urban Niger and compare campaign estimates to measurements from a post-campaign survey. Vaccine coverage was overestimated because the campaign underestimated resident numbers and seasonal migration further increased the target population. We combine satellite-derived measurements of fluctuations in population distribution with high-resolution measles case reports to develop a dynamic model that illustrates the potential improvement in vaccination campaign coverage if planners account for predictable population fluctuations. Satellite imagery can improve retrospective estimates of vaccination campaign impact and future campaign planning by synchronizing interventions with predictable population fluxes.

Measuring populations to improve vaccination coverage

PubMed Central

Bharti, Nita; Djibo, Ali; Tatem, Andrew J.; Grenfell, Bryan T.; Ferrari, Matthew J.

2016-01-01

In low-income settings, vaccination campaigns supplement routine immunization but often fail to achieve coverage goals due to uncertainty about target population size and distribution. Accurate, updated estimates of target populations are rare but critical; short-term fluctuations can greatly impact population size and susceptibility. We use satellite imagery to quantify population fluctuations and the coverage achieved by a measles outbreak response vaccination campaign in urban Niger and compare campaign estimates to measurements from a post-campaign survey. Vaccine coverage was overestimated because the campaign underestimated resident numbers and seasonal migration further increased the target population. We combine satellite-derived measurements of fluctuations in population distribution with high-resolution measles case reports to develop a dynamic model that illustrates the potential improvement in vaccination campaign coverage if planners account for predictable population fluctuations. Satellite imagery can improve retrospective estimates of vaccination campaign impact and future campaign planning by synchronizing interventions with predictable population fluxes. PMID:27703191

in silico Surveillance: evaluating outbreak detection with simulation models

PubMed Central

2013-01-01

Background Detecting outbreaks is a crucial task for public health officials, yet gaps remain in the systematic evaluation of outbreak detection protocols. The authors’ objectives were to design, implement, and test a flexible methodology for generating detailed synthetic surveillance data that provides realistic geographical and temporal clustering of cases and use to evaluate outbreak detection protocols. Methods A detailed representation of the Boston area was constructed, based on data about individuals, locations, and activity patterns. Influenza-like illness (ILI) transmission was simulated, producing 100 years of in silico ILI data. Six different surveillance systems were designed and developed using gathered cases from the simulated disease data. Performance was measured by inserting test outbreaks into the surveillance streams and analyzing the likelihood and timeliness of detection. Results Detection of outbreaks varied from 21% to 95%. Increased coverage did not linearly improve detection probability for all surveillance systems. Relaxing the decision threshold for signaling outbreaks greatly increased false-positives, improved outbreak detection slightly, and led to earlier outbreak detection. Conclusions Geographical distribution can be more important than coverage level. Detailed simulations of infectious disease transmission can be configured to represent nearly any conceivable scenario. They are a powerful tool for evaluating the performance of surveillance systems and methods used for outbreak detection. PMID:23343523

Data-driven approach of CUSUM algorithm in temporal aberrant event detection using interactive web applications.

PubMed

Li, Ye; Whelan, Michael; Hobbs, Leigh; Fan, Wen Qi; Fung, Cecilia; Wong, Kenny; Marchand-Austin, Alex; Badiani, Tina; Johnson, Ian

2016-06-27

In 2014/2015, Public Health Ontario developed disease-specific, cumulative sum (CUSUM)-based statistical algorithms for detecting aberrant increases in reportable infectious disease incidence in Ontario. The objective of this study was to determine whether the prospective application of these CUSUM algorithms, based on historical patterns, have improved specificity and sensitivity compared to the currently used Early Aberration Reporting System (EARS) algorithm, developed by the US Centers for Disease Control and Prevention. A total of seven algorithms were developed for the following diseases: cyclosporiasis, giardiasis, influenza (one each for type A and type B), mumps, pertussis, invasive pneumococcal disease. Historical data were used as baseline to assess known outbreaks. Regression models were used to model seasonality and CUSUM was applied to the difference between observed and expected counts. An interactive web application was developed allowing program staff to directly interact with data and tune the parameters of CUSUM algorithms using their expertise on the epidemiology of each disease. Using these parameters, a CUSUM detection system was applied prospectively and the results were compared to the outputs generated by EARS. The outcome was the detection of outbreaks, or the start of a known seasonal increase and predicting the peak in activity. The CUSUM algorithms detected provincial outbreaks earlier than the EARS algorithm, identified the start of the influenza season in advance of traditional methods, and had fewer false positive alerts. Additionally, having staff involved in the creation of the algorithms improved their understanding of the algorithms and improved use in practice. Using interactive web-based technology to tune CUSUM improved the sensitivity and specificity of detection algorithms.

Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest

PubMed Central

Eng, Christine L. P.; Tong, Joo Chuan; Tan, Tin Wee

2017-01-01

Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak. PMID:28587080

Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest.

PubMed

Eng, Christine L P; Tong, Joo Chuan; Tan, Tin Wee

2017-05-25

Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak.

Predictive analysis effectiveness in determining the epidemic disease infected area

NASA Astrophysics Data System (ADS)

Ibrahim, Najihah; Akhir, Nur Shazwani Md.; Hassan, Fadratul Hafinaz

2017-10-01

Epidemic disease outbreak had caused nowadays community to raise their great concern over the infectious disease controlling, preventing and handling methods to diminish the disease dissemination percentage and infected area. Backpropagation method was used for the counter measure and prediction analysis of the epidemic disease. The predictive analysis based on the backpropagation method can be determine via machine learning process that promotes the artificial intelligent in pattern recognition, statistics and features selection. This computational learning process will be integrated with data mining by measuring the score output as the classifier to the given set of input features through classification technique. The classification technique is the features selection of the disease dissemination factors that likely have strong interconnection between each other in causing infectious disease outbreaks. The predictive analysis of epidemic disease in determining the infected area was introduced in this preliminary study by using the backpropagation method in observation of other's findings. This study will classify the epidemic disease dissemination factors as the features for weight adjustment on the prediction of epidemic disease outbreaks. Through this preliminary study, the predictive analysis is proven to be effective method in determining the epidemic disease infected area by minimizing the error value through the features classification.

Using Earth Observations to Understand and Predict Infectious Diseases

NASA Technical Reports Server (NTRS)

Soebiyanto, Radina P.; Kiang, Richard

2015-01-01

This presentation discusses the processes from data collection and processing to analysis involved in unraveling patterns between disease outbreaks and the surrounding environment and meteorological conditions. We used these patterns to estimate when and where disease outbreaks will occur. As a case study, we will present our work on assessing the relationship between meteorological conditions and influenza in Central America. Our work represents the discovery, prescriptive and predictive aspects of data analytics.

Spatiotemporal Bayesian networks for malaria prediction.

PubMed

Haddawy, Peter; Hasan, A H M Imrul; Kasantikul, Rangwan; Lawpoolsri, Saranath; Sa-Angchai, Patiwat; Kaewkungwal, Jaranit; Singhasivanon, Pratap

2018-01-01

Targeted intervention and resource allocation are essential for effective malaria control, particularly in remote areas, with predictive models providing important information for decision making. While a diversity of modeling technique have been used to create predictive models of malaria, no work has made use of Bayesian networks. Bayes nets are attractive due to their ability to represent uncertainty, model time lagged and nonlinear relations, and provide explanations. This paper explores the use of Bayesian networks to model malaria, demonstrating the approach by creating village level models with weekly temporal resolution for Tha Song Yang district in northern Thailand. The networks are learned using data on cases and environmental covariates. Three types of networks are explored: networks for numeric prediction, networks for outbreak prediction, and networks that incorporate spatial autocorrelation. Evaluation of the numeric prediction network shows that the Bayes net has prediction accuracy in terms of mean absolute error of about 1.4 cases for 1 week prediction and 1.7 cases for 6 week prediction. The network for outbreak prediction has an ROC AUC above 0.9 for all prediction horizons. Comparison of prediction accuracy of both Bayes nets against several traditional modeling approaches shows the Bayes nets to outperform the other models for longer time horizon prediction of high incidence transmission. To model spread of malaria over space, we elaborate the models with links between the village networks. This results in some very large models which would be far too laborious to build by hand. So we represent the models as collections of probability logic rules and automatically generate the networks. Evaluation of the models shows that the autocorrelation links significantly improve prediction accuracy for some villages in regions of high incidence. We conclude that spatiotemporal Bayesian networks are a highly promising modeling alternative for prediction of malaria and other vector-borne diseases. Copyright © 2017 Elsevier B.V. All rights reserved.

Swimming Associated Disease Outbreaks.

ERIC Educational Resources Information Center

Cabelli, V. J.

1978-01-01

Presents a literature review of recreational waterborne outbreaks and cases of disease, covering publications of 1976-77. This review includes: (1) retrospective and prospective epidemiological studies; (2) predictive models of the risk of recreational waterborn disease. A list of 35 references is also presented. (HM)

Development of a Salmonella screening tool for consumer complaint-based foodborne illness surveillance systems.

PubMed

Li, John; Maclehose, Rich; Smith, Kirk; Kaehler, Dawn; Hedberg, Craig

2011-01-01

Foodborne illness surveillance based on consumer complaints detects outbreaks by finding common exposures among callers, but this process is often difficult. Laboratory testing of ill callers could also help identify potential outbreaks. However, collection of stool samples from all callers is not feasible. Methods to help screen calls for etiology are needed to increase the efficiency of complaint surveillance systems and increase the likelihood of detecting foodborne outbreaks caused by Salmonella. Data from the Minnesota Department of Health foodborne illness surveillance database (2000 to 2008) were analyzed. Complaints with identified etiologies were examined to create a predictive model for Salmonella. Bootstrap methods were used to internally validate the model. Seventy-one percent of complaints in the foodborne illness database with known etiologies were due to norovirus. The predictive model had a good discriminatory ability to identify Salmonella calls. Three cutoffs for the predictive model were tested: one that maximized sensitivity, one that maximized specificity, and one that maximized predictive ability, providing sensitivities and specificities of 32 and 96%, 100 and 54%, and 89 and 72%, respectively. Development of a predictive model for Salmonella could help screen calls for etiology. The cutoff that provided the best predictive ability for Salmonella corresponded to a caller reporting diarrhea and fever with no vomiting, and five or fewer people ill. Screening calls for etiology would help identify complaints for further follow-up and result in identifying Salmonella cases that would otherwise go unconfirmed; in turn, this could lead to the identification of more outbreaks.

Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability

NASA Astrophysics Data System (ADS)

Liu, Sijun; Chen, Jiaping; Wang, Jianming; Wu, Zhuchao; Wu, Weihua; Xu, Zhiwei; Hu, Wenbiao; Xu, Fei; Tong, Shilu; Shen, Hongbing

2017-10-01

Hand, foot, and mouth disease (HFMD) is a significant public health issue in China and an accurate prediction of epidemic can improve the effectiveness of HFMD control. This study aims to develop a weather-based forecasting model for HFMD using the information on climatic variables and HFMD surveillance in Nanjing, China. Daily data on HFMD cases and meteorological variables between 2010 and 2015 were acquired from the Nanjing Center for Disease Control and Prevention, and China Meteorological Data Sharing Service System, respectively. A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed and validated by dividing HFMD infection data into two datasets: the data from 2010 to 2013 were used to construct a model and those from 2014 to 2015 were used to validate it. Moreover, we used weekly prediction for the data between 1 January 2014 and 31 December 2015 and leave-1-week-out prediction was used to validate the performance of model prediction. SARIMA (2,0,0)52 associated with the average temperature at lag of 1 week appeared to be the best model (R 2 = 0.936, BIC = 8.465), which also showed non-significant autocorrelations in the residuals of the model. In the validation of the constructed model, the predicted values matched the observed values reasonably well between 2014 and 2015. There was a high agreement rate between the predicted values and the observed values (sensitivity 80%, specificity 96.63%). This study suggests that the SARIMA model with average temperature could be used as an important tool for early detection and prediction of HFMD outbreaks in Nanjing, China.

Overcoming barriers in evaluating outbreaks of diarrheal disease in resource poor settings: assessment of recurrent outbreaks in Chobe District, Botswana

PubMed Central

2013-01-01

Background Diarrheal illness remains a leading cause of global morbidity and mortality, with the majority of deaths occurring in children <5 years of age. Lack of resources often prohibits the evaluation of outbreak characteristics and limits progress in managing this important disease syndrome, particularly in Africa. Relying only on existing medical staff and hospital resources, we assess the use of a questionnaire survey tool to identify baseline outbreak characteristics during recurrent diarrheal outbreaks in Chobe, Botswana. Methods Using historical surveillance data (2006–2009), the temporal pattern of recurrent diarrheal outbreaks was evaluated among patients <5 years of age presenting to health facilities in Chobe District. Using a questionnaire survey tool, medical staff from selected health facilities assessed patients (all ages) presenting with diarrheal disease during two diarrheal outbreaks (2011–2012). Cluster analysis and classification and regression trees (CART) were used to evaluate patient attributes by outbreak. Results We identified a bimodal, annual pattern of acute diarrhea in children <5 years of age across years (Wilcox test, W = 456.5, p = 0.052). Historical outbreak periods appeared to coincide with major hydrological phenomena (rainfall/flood recession). Across health facilities, a significant percent of patients in the prospective study were in the ≥5 age class (44%, n = 515 and 35%, n = 333 in the dry and wet season outbreaks, respectively). Cluster analysis of questionnaire data identified two main branches associated with patient age (<5 and ≥5 years of age). Patients did not cluster by outbreak or village. CART examination identified sex and hospitalization as being most predictive of patients <5 years and household diarrhea in patients ≥5 years. Water shortages and water quality deficiencies were identified in both outbreaks. Conclusions Diarrhea is a persistent, seasonally occurring disease in Chobe District, Botswana. Lack of variation in outbreak variables suggests the possibility of environmental drivers influencing outbreak dynamics and the potential importance of human-environmental linkages in this region. Public health strategy should be directed at securing improved water service and correcting water quality deficiencies. Public health education should include increased emphasis on sanitation practices when providing care to household members with diarrhea. While global diarrheal disease surveillance is directed at the under-5 age group, this may not be appropriate in areas of high HIV prevalence such as that found in our study area where a large immune-compromised population may warrant increased surveillance across age groups. The approach used in this study provided the first detailed characterization of diarrheal disease outbreaks in the area, an important starting point for immediate intervention and development of working hypotheses for future disease investigations. While data derived from this approach are necessarily limited, they identify critical information on outbreak characteristics in resource poor settings where data gaps continue and disease incidence is high. PMID:23971427

The AFHSC-Division of GEIS Operations Predictive Surveillance Program: a multidisciplinary approach for the early detection and response to disease outbreaks

PubMed Central

2011-01-01

The Armed Forces Health Surveillance Center, Division of Global Emerging Infections Surveillance and Response System Operations (AFHSC-GEIS) initiated a coordinated, multidisciplinary program to link data sets and information derived from eco-climatic remote sensing activities, ecologic niche modeling, arthropod vector, animal disease-host/reservoir, and human disease surveillance for febrile illnesses, into a predictive surveillance program that generates advisories and alerts on emerging infectious disease outbreaks. The program’s ultimate goal is pro-active public health practice through pre-event preparedness, prevention and control, and response decision-making and prioritization. This multidisciplinary program is rooted in over 10 years experience in predictive surveillance for Rift Valley fever outbreaks in Eastern Africa. The AFHSC-GEIS Rift Valley fever project is based on the identification and use of disease-emergence critical detection points as reliable signals for increased outbreak risk. The AFHSC-GEIS predictive surveillance program has formalized the Rift Valley fever project into a structured template for extending predictive surveillance capability to other Department of Defense (DoD)-priority vector- and water-borne, and zoonotic diseases and geographic areas. These include leishmaniasis, malaria, and Crimea-Congo and other viral hemorrhagic fevers in Central Asia and Africa, dengue fever in Asia and the Americas, Japanese encephalitis (JE) and chikungunya fever in Asia, and rickettsial and other tick-borne infections in the U.S., Africa and Asia. PMID:21388561

Model-informed risk assessment for Zika virus outbreaks in the Asia-Pacific regions.

PubMed

Teng, Yue; Bi, Dehua; Xie, Guigang; Jin, Yuan; Huang, Yong; Lin, Baihan; An, Xiaoping; Tong, Yigang; Feng, Dan

2017-05-01

Recently, Zika virus (ZIKV) has been recognized as a significant threat to global public health. The disease was present in large parts of the Americas, the Caribbean, and also the western Pacific area with southern Asia during 2015 and 2016. However, little is known about the factors affecting the transmission of ZIKV. We used Gradient Boosted Regression Tree models to investigate the effects of various potential explanatory variables on the spread of ZIKV, and used current with historical information from a range of sources to assess the risks of future ZIKV outbreaks. Our results indicated that the probability of ZIKV outbreaks increases with vapor pressure, the occurrence of Dengue virus, and population density but decreases as health expenditure, GDP, and numbers of travelers. The predictive results revealed the potential risk countries of ZIKV infection in the Asia-Pacific regions between October 2016 and January 2017. We believe that the high-risk conditions would continue in South Asia and Australia over this period. By integrating information on eco-environmental, social-economical, and ZIKV-related niche factors, this study estimated the probability for locally acquired mosquito-borne ZIKV infections in the Asia-Pacific region and improves the ability to forecast, and possibly even prevent, future outbreaks of ZIKV. Copyright © 2017 The British Infection Association. Published by Elsevier Ltd. All rights reserved.

Timeliness of Nongovernmental versus Governmental Global Outbreak Communications

PubMed Central

Mondor, Luke; Brownstein, John S.; Chan, Emily; Madoff, Lawrence C.; Pollack, Marjorie P.; Buckeridge, David L.

2012-01-01

To compare the timeliness of nongovernmental and governmental communications of infectious disease outbreaks and evaluate trends for each over time, we investigated the time elapsed from the beginning of an outbreak to public reporting of the event. We found that governmental sources improved the timeliness of public reporting of infectious disease outbreaks during the study period. PMID:22709741

IMPROVING WATERBORNE DISEASE OUTBREAK INVESTIGATIONS

EPA Science Inventory

This article summarizes the discussions and conclusions of a workshop held December 7-8, 1998, to consider the inherent limitations and weaknesses of waterborne outbreak investigations and make recommendations for their improvement. In recent years, an increased number of suspec...

Hydroclimatic drivers, Water-borne Diseases, and Population Vulnerability in Bengal Delta

NASA Astrophysics Data System (ADS)

Akanda, A. S.; Jutla, A. S.

2012-04-01

Water-borne diarrheal disease outbreaks in the Bengal Delta region, such as cholera, rotavirus, and dysentery, show distinct seasonal peaks and spatial signatures in their origin and progression. However, the mechanisms behind these seasonal phenomena, especially the role of regional climatic and hydrologic processes behind the disease outbreaks, are not fully understood. Overall diarrheal disease prevalence and the population vulnerability to transmission mechanisms thus remain severely underestimated. Recent findings suggest that diarrheal incidence in the spring is strongly associated with scarcity of freshwater flow volumes, while the abundance of water in monsoon show strong positive correlation with autumn diarrheal burden. The role of large-scale ocean-atmospheric processes that tend to modulate meteorological, hydrological, and environmental conditions over large regions and the effects on the ecological states conducive to the vectors and triggers of diarrheal outbreaks over large geographic regions are not well understood. We take a large scale approach to conduct detailed diagnostic analyses of a range of climate, hydrological, and ecosystem variables to investigate their links to outbreaks, occurrence, and transmission of the most prevalent water-borne diarrheal diseases. We employ satellite remote sensing data products to track coastal ecosystems and plankton processes related to cholera outbreaks. In addition, we investigate the effect of large scale hydroclimatic extremes (e.g., droughts and floods, El Nino) to identify how diarrheal transmission and epidemic outbreaks are most likely to respond to shifts in climatic, hydrologic, and ecological changes over coming decades. We argue that controlling diarrheal disease burden will require an integrated predictive surveillance approach - a combination of prediction and prevention - with recent advances in climate-based predictive capabilities and demonstrated successes in primary and tertiary prevention in endemic regions.

A Study of 279 General Outbreaks of Gastrointestinal Infection in the North-East Region of England

PubMed Central

Tebbutt, Grahame M.; Wilson, Deborah; Holtby, Ian

2009-01-01

All outbreaks of infectious intestinal disease reported to the authorities were entered on a computer database with outbreak control teams being established to investigate larger or more significant incidents. The outbreak database and, when set up, the notes of outbreak team meetings were examined for the 279 outbreaks reported in a three-year period (2003–2005). Faeces specimens submitted as part of an outbreak were examined for microbial pathogens and the results cross-matched to the outbreak number. Almost half of the general outbreaks reported (137) occurred in long-term care facilities for the elderly, 51 outbreaks were recorded in hospitals and 31 occurred in the wider community. In 76 outbreaks no specimen was logged. A microbial cause was confirmed in about one-third of outbreaks, with noroviruses being the most common (19%). Salmonellas accounted for 12 of the 21 community outbreaks linked to social events and all were foodborne. Suggestions for improving notification and surveillance are discussed. PMID:19440398

Evidence for Emergency Vaccination Having Played a Crucial Role to Control the 1965/66 Foot-and-Mouth Disease Outbreak in Switzerland

PubMed Central

Zingg, Dana; Häsler, Stephan; Schuepbach-Regula, Gertraud; Schwermer, Heinzpeter; Dürr, Salome

2015-01-01

Foot-and-mouth disease (FMD) is a highly contagious disease that caused several large outbreaks in Europe in the last century. The last important outbreak in Switzerland took place in 1965/66 and affected more than 900 premises and more than 50,000 animals were slaughtered. Large-scale emergency vaccination of the cattle and pig population has been applied to control the epidemic. In recent years, many studies have used infectious disease models to assess the impact of different disease control measures, including models developed for diseases exotic for the specific region of interest. Often, the absence of real outbreak data makes a validation of such models impossible. This study aimed to evaluate whether a spatial, stochastic simulation model (the Davis Animal Disease Simulation model) can predict the course of a Swiss FMD epidemic based on the available historic input data on population structure, contact rates, epidemiology of the virus, and quality of the vaccine. In addition, the potential outcome of the 1965/66 FMD epidemic without application of vaccination was investigated. Comparing the model outcomes to reality, only the largest 10% of the simulated outbreaks approximated the number of animals being culled. However, the simulation model highly overestimated the number of culled premises. While the outbreak duration could not be well reproduced by the model compared to the 1965/66 epidemic, it was able to accurately estimate the size of the area infected. Without application of vaccination, the model predicted a much higher mean number of culled animals than with vaccination, demonstrating that vaccination was likely crucial in disease control for the Swiss FMD outbreak in 1965/66. The study demonstrated the feasibility to analyze historical outbreak data with modern analytical tools. However, it also confirmed that predicted epidemics from a most carefully parameterized model cannot integrate all eventualities of a real epidemic. Therefore, decision makers need to be aware that infectious disease models are useful tools to support the decision-making process but their results are not equal valuable as real observations and should always be interpreted with caution. PMID:26697436

Modelling the propagation of social response during a disease outbreak.

PubMed

Fast, Shannon M; González, Marta C; Wilson, James M; Markuzon, Natasha

2015-03-06

Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviours from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response. We couple the disease spread and panic spread processes and model them through local interactions between agents. The social contagion process depends on the prevalence of the disease, its perceived risk and a global media signal. We verify the model by analysing the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City and 2003 severe acute respiratory syndrome and 2009 H1N1 outbreaks in Hong Kong, accurately predicting population-level behaviour. This kind of empirically validated model is critical to exploring strategies for public health intervention, increasing our ability to anticipate the response to infectious disease outbreaks. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Modelling the propagation of social response during a disease outbreak

PubMed Central

Fast, Shannon M.; González, Marta C.; Wilson, James M.; Markuzon, Natasha

2015-01-01

Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviours from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response. We couple the disease spread and panic spread processes and model them through local interactions between agents. The social contagion process depends on the prevalence of the disease, its perceived risk and a global media signal. We verify the model by analysing the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City and 2003 severe acute respiratory syndrome and 2009 H1N1 outbreaks in Hong Kong, accurately predicting population-level behaviour. This kind of empirically validated model is critical to exploring strategies for public health intervention, increasing our ability to anticipate the response to infectious disease outbreaks. PMID:25589575

Development of artificial intelligence approach to forecasting oyster norovirus outbreaks along Gulf of Mexico coast.

PubMed

Chenar, Shima Shamkhali; Deng, Zhiqiang

2018-02-01

This paper presents an artificial intelligence-based model, called ANN-2Day model, for forecasting, managing and ultimately eliminating the growing risk of oyster norovirus outbreaks. The ANN-2Day model was developed using Artificial Neural Network (ANN) Toolbox in MATLAB Program and 15-years of epidemiological and environmental data for six independent environmental predictors including water temperature, solar radiation, gage height, salinity, wind, and rainfall. It was found that oyster norovirus outbreaks can be forecasted with two-day lead time using the ANN-2Day model and daily data of the six environmental predictors. Forecasting results of the ANN-2Day model indicated that the model was capable of reproducing 19years of historical oyster norovirus outbreaks along the Northern Gulf of Mexico coast with the positive predictive value of 76.82%, the negative predictive value of 100.00%, the sensitivity of 100.00%, the specificity of 99.84%, and the overall accuracy of 99.83%, respectively, demonstrating the efficacy of the ANN-2Day model in predicting the risk of norovirus outbreaks to human health. The 2-day lead time enables public health agencies and oyster harvesters to plan for management interventions and thus makes it possible to achieve a paradigm shift of their daily management and operation from primarily reacting to epidemic incidents of norovirus infection after they have occurred to eliminating (or at least reducing) the risk of costly incidents. Copyright © 2017 Elsevier Ltd. All rights reserved.

Signature-forecasting and early outbreak detection system

PubMed Central

Naumova, Elena N.; MacNeill, Ian B.

2008-01-01

SUMMARY Daily disease monitoring via a public health surveillance system provides valuable information on population risks. Efficient statistical tools for early detection of rapid changes in the disease incidence are a must for modern surveillance. The need for statistical tools for early detection of outbreaks that are not based on historical information is apparent. A system is discussed for monitoring cases of infections with a view to early detection of outbreaks and to forecasting the extent of detected outbreaks. We propose a set of adaptive algorithms for early outbreak detection that does not rely on extensive historical recording. We also include knowledge of infection disease epidemiology into forecasts. To demonstrate this system we use data from the largest water-borne outbreak of cryptosporidiosis, which occurred in Milwaukee in 1993. Historical data are smoothed using a loess-type smoother. Upon receipt of a new datum, the smoothing is updated and estimates are made of the first two derivatives of the smooth curve, and these are used for near-term forecasting. Recent data and the near-term forecasts are used to compute a color-coded warning index, which quantify the level of concern. The algorithms for computing the warning index have been designed to balance Type I errors (false prediction of an epidemic) and Type II errors (failure to correctly predict an epidemic). If the warning index signals a sufficiently high probability of an epidemic, then a forecast of the possible size of the outbreak is made. This longer term forecast is made by fitting a ‘signature’ curve to the available data. The effectiveness of the forecast depends upon the extent to which the signature curve captures the shape of outbreaks of the infection under consideration. PMID:18716671

Satellite SST-Based Coral Disease Outbreak Predictions for the Hawaiian Archipelago.

PubMed

Caldwell, Jamie M; Heron, Scott F; Eakin, C Mark; Donahue, Megan J

2016-02-01

Predicting wildlife disease risk is essential for effective monitoring and management, especially for geographically expansive ecosystems such as coral reefs in the Hawaiian archipelago. Warming ocean temperature has increased coral disease outbreaks contributing to declines in coral cover worldwide. In this study we investigated seasonal effects of thermal stress on the prevalence of the three most widespread coral diseases in Hawai'i: Montipora white syndrome, Porites growth anomalies and Porites tissue loss syndrome. To predict outbreak likelihood we compared disease prevalence from surveys conducted between 2004 and 2015 from 18 Hawaiian Islands and atolls with biotic (e.g., coral density) and abiotic (satellite-derived sea surface temperature metrics) variables using boosted regression trees. To date, the only coral disease forecast models available were developed for Acropora white syndrome on the Great Barrier Reef (GBR). Given the complexities of disease etiology, differences in host demography and environmental conditions across reef regions, it is important to refine and adapt such models for different diseases and geographic regions of interest. Similar to the Acropora white syndrome models, anomalously warm conditions were important for predicting Montipora white syndrome, possibly due to a relationship between thermal stress and a compromised host immune system. However, coral density and winter conditions were the most important predictors of all three coral diseases in this study, enabling development of a forecasting system that can predict regions of elevated disease risk up to six months before an expected outbreak. Our research indicates satellite-derived systems for forecasting disease outbreaks can be appropriately adapted from the GBR tools and applied for a variety of diseases in a new region. These models can be used to enhance management capacity to prepare for and respond to emerging coral diseases throughout Hawai'i and can be modified for other diseases and regions around the world.

Satellite SST-Based Coral Disease Outbreak Predictions for the Hawaiian Archipelago

PubMed Central

Caldwell, Jamie M.; Heron, Scott F.; Eakin, C. Mark; Donahue, Megan J.

2017-01-01

Predicting wildlife disease risk is essential for effective monitoring and management, especially for geographically expansive ecosystems such as coral reefs in the Hawaiian archipelago. Warming ocean temperature has increased coral disease outbreaks contributing to declines in coral cover worldwide. In this study we investigated seasonal effects of thermal stress on the prevalence of the three most widespread coral diseases in Hawai’i: Montipora white syndrome, Porites growth anomalies and Porites tissue loss syndrome. To predict outbreak likelihood we compared disease prevalence from surveys conducted between 2004 and 2015 from 18 Hawaiian Islands and atolls with biotic (e.g., coral density) and abiotic (satellite-derived sea surface temperature metrics) variables using boosted regression trees. To date, the only coral disease forecast models available were developed for Acropora white syndrome on the Great Barrier Reef (GBR). Given the complexities of disease etiology, differences in host demography and environmental conditions across reef regions, it is important to refine and adapt such models for different diseases and geographic regions of interest. Similar to the Acropora white syndrome models, anomalously warm conditions were important for predicting Montipora white syndrome, possibly due to a relationship between thermal stress and a compromised host immune system. However, coral density and winter conditions were the most important predictors of all three coral diseases in this study, enabling development of a forecasting system that can predict regions of elevated disease risk up to six months before an expected outbreak. Our research indicates satellite-derived systems for forecasting disease outbreaks can be appropriately adapted from the GBR tools and applied for a variety of diseases in a new region. These models can be used to enhance management capacity to prepare for and respond to emerging coral diseases throughout Hawai’i and can be modified for other diseases and regions around the world. PMID:29071133

Ebola virus disease in Africa: epidemiology and nosocomial transmission.

PubMed

Shears, P; O'Dempsey, T J D

2015-05-01

The 2014 Ebola outbreak in West Africa, primarily affecting Guinea, Sierra Leone, and Liberia, has exceeded all previous Ebola outbreaks in the number of cases and in international response. There have been 20 significant outbreaks of Ebola virus disease in Sub-Saharan Africa prior to the 2014 outbreak, the largest being that in Uganda in 2000, with 425 cases and a mortality of 53%. Since the first outbreaks in Sudan and Zaire in 1976, transmission within health facilities has been of major concern, affecting healthcare workers and acting as amplifiers of spread into the community. The lack of resources for infection control and personal protective equipment are the main reasons for nosocomial transmission. Local strategies to improve infection control, and a greater understanding of local community views on the disease, have helped to bring outbreaks under control. Recommendations from previous outbreaks include improved disease surveillance to enable more rapid health responses, the wider availability of personal protective equipment, and greater international preparedness. Copyright © 2015 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

Physical Patterns Associated with 27 April 2011 Tornado Outbreak

NASA Astrophysics Data System (ADS)

Ramos, Fernanda; Salem, Thomas

2012-02-01

The National Weather Service office in Memphis, Tennessee has aimed their efforts to improve severe tornado forecasting. Everything is not known about tornadogenesis, but one thing is: tornadoes tend to form within supercell thunderstorms. Hence, 27 April 2011 and 25 May 2011 were days when a Tornado Outbreak was expected to arise. Although 22 tornadoes struck the region on 27 April 2011, only 1 impacted the area on 25 May 2011. In order to understand both events, comparisons of their physical features were made. These parameters were studied using the Weather Event Simulator system and the NOAA/NWS Storm Prediction database. This research concentrated on the Surface Frontal Analysis, NAM40 700mb Dew-Points, NAM80 250mb Wind Speed and NAM20 500mb Vorticity images as well as 0-6 km Shear, MUCAPE and VGP mesoscale patterns. As result of this research a Dry-Line ahead of a Cold Front, Dew-points 5C and higher, and high Vorticity values^ were synoptic patterns that influenced to the formation of supercell tornadoes. Finally, MUCAPE and VGP favored the possibility of tornadoes occurrence on 25 May 2011, but shear was the factor that made 27 April 2011 a day for a Tornado Outbreak weather event.

Norovirus outbreaks: a systematic review of commonly implicated transmission routes and vehicles

PubMed Central

Bitler, E. J.; Matthews, J. E.; Dickey, B. W.; Eisenberg, J. N. S.; Leon, J. S.

2013-01-01

Summary Causal mechanisms of norovirus outbreaks are often not revealed. Understanding the transmission route (e.g., foodborne, waterborne, or environmental) and vehicle (e.g., shellfish or recreational water) of a norovirus outbreak, however, is of great public health importance; this information can facilitate interventions for an ongoing outbreak and regulatory action to limit future outbreaks. Towards this goal, we conducted a systematic review to examine whether published outbreak information was associated with the implicated transmission route or vehicle. Genogroup distribution was associated with transmission route and food vehicle, but attack rate and the presence of GII.4 strain were not associated with transmission route, food vehicle, or water vehicle. Attack rate, genogroup distribution, and GII.4 strain distribution also varied by other outbreak characteristics (e.g. setting, season, and hemisphere). These relationships suggest that different genogroups exploit different environmental conditions and thereby can be used to predict the likelihood of various transmission routes or vehicles. PMID:23433247

Are staff management practices and inspection risk ratings associated with foodborne disease outbreaks in the catering industry in England and Wales?

PubMed

Jones, Sarah L; Parry, Sharon M; O'Brien, Sarah J; Palmer, Stephen R

2008-03-01

Despite structured enforcement of food hygiene requirements known to prevent foodborne disease outbreaks, catering businesses continue to be the most common setting for outbreaks in the United Kingdom. In a matched case control study of catering businesses, 148 businesses associated with outbreaks were compared with 148 control businesses. Hazard analysis critical control point systems and/or formal food hygiene training qualifications were not protective. Food hygiene inspection scores were not useful in predicting which catering businesses were associated with outbreaks. Businesses associated with outbreaks were more likely to be larger small and medium-sized enterprises (SMEs) or to serve Chinese cuisine and less likely to have the owner or manager working in the kitchen, but when size of the SME was taken into account these two differences were no longer significant. In larger businesses, case businesses were more likely to be hotels and were more commonly associated with viral foodborne outbreaks, but there was no explanation within the data for this association.

Emergence of new norovirus variants on spring cruise ships and prediction of winter epidemics.

PubMed

Verhoef, Linda; Depoortere, Evelyn; Boxman, Ingeborg; Duizer, Erwin; van Duynhoven, Yvonne; Harris, John; Johnsen, Christina; Kroneman, Annelies; Le Guyader, Soizick; Lim, Wilina; Maunula, Leena; Meldal, Hege; Ratcliff, Rod; Reuter, Gábor; Schreier, Eckart; Siebenga, Joukje; Vainio, Kirsti; Varela, Carmen; Vennema, Harry; Koopmans, Marion

2008-02-01

In June 2006, reported outbreaks of norovirus on cruise ships suddenly increased; 43 outbreaks occurred on 13 vessels. All outbreaks investigated manifested person-to-person transmission. Detection of a point source was impossible because of limited investigation of initial outbreaks and data sharing. The most probable explanation for these outbreaks is increased norovirus activity in the community, which coincided with the emergence of 2 new GGII.4 variant strains in Europe and the Pacific. As in 2002, a new GGII.4 variant detected in the spring and summer corresponded with high norovirus activity in the subsequent winter. Because outbreaks on cruise ships are likely to occur when new variants circulate, an active reporting system could function as an early warning system. Internationally accepted guidelines are needed for reporting, investigating, and controlling norovirus illness on cruise ships in Europe.

Emergence of New Norovirus Variants on Spring Cruise Ships and Prediction of Winter Epidemics

PubMed Central

Depoortere, Evelyn; Boxman, Ingeborg; Duizer, Erwin; van Duynhoven, Yvonne; Harris, John; Johnsen, Christina; Kroneman, Annelies; Le Guyader, Soizick; Lim, Wilina; Maunula, Leena; Meldal, Hege; Ratcliff, Rod; Reuter, Gábor; Schreier, Eckart; Siebenga, Joukje; Vainio, Kirsti; Varela, Carmen; Vennema, Harry; Koopmans, Marion

2008-01-01

In June 2006, reported outbreaks of norovirus on cruise ships suddenly increased; 43 outbreaks occurred on 13 vessels. All outbreaks investigated manifested person-to-person transmission. Detection of a point source was impossible because of limited investigation of initial outbreaks and data sharing. The most probable explanation for these outbreaks is increased norovirus activity in the community, which coincided with the emergence of 2 new GGII.4 variant strains in Europe and the Pacific. As in 2002, a new GGII.4 variant detected in the spring and summer corresponded with high norovirus activity in the subsequent winter. Because outbreaks on cruise ships are likely to occur when new variants circulate, an active reporting system could function as an early warning system. Internationally accepted guidelines are needed for reporting, investigating, and controlling norovirus illness on cruise ships in Europe. PMID:18258116

Nigerian response to the 2014 Ebola viral disease outbreak: lessons and cautions

PubMed Central

Oleribe, Obinna Ositadimma; Crossey, Mary Margaret Elizabeth; Taylor-Robinson, Simon David

2015-01-01

The Ebola virus disease outbreak that initially hit Guinea, Liberia and Senegal in 2014 was projected to affect Nigeria very badly when the first case was reported in July 2014. However, the outbreak was effectively and swiftly contained with only eight deaths out of 20 cases, confounding even the most optimistic predictions of the disease modelers. A combination of health worker and public education, a coordinated field epidemiology and laboratory training program (with prior experience in disease outbreak control in other diseases) and effective set-up of emergency operations centers were some of the measures that helped to confound the critics and contain what would have been an otherwise deadly outbreak in a densely populated country with a highly mobile population. This article highlights the measures taken in Nigeria and looks to the translatable lessons learnt for future disease outbreaks, whether that be from the Ebola virus or other infectious agents. PMID:26740841

Cryptosporidiosis-associated mortality following a massive waterborne outbreak in Milwaukee, Wisconsin.

PubMed

Hoxie, N J; Davis, J P; Vergeront, J M; Nashold, R D; Blair, K A

1997-12-01

This study estimated the magnitude of cryptosporidiosis-associated mortality in the Milwaukee vicinity for 2 years following a massive waterborne outbreak. Death certificates were reviewed. During approximately 2 years before the outbreak, cryptosporidiosis was listed as an underlying or contributing cause of death on the death certificates of four Milwaukee-vicinity residents. In the approximately 2 years after the outbreak, this number was 54, of whom 85% had acquired immunodeficiency syndrome (AIDS) listed as the underlying cause of death. In the first 6 months after the outbreak, the number of death certificates indicating AIDS, but not cryptosporidiosis, as a cause of death was 19 (95% confidence interval = 12.26) higher than preoutbreak trends would have predicted. Waterborne outbreaks of cryptosporidium infection can result in significant mortality, particularly among immunocompromised populations. Any discussion of policies to ensure safe drinking water must consider the potential fatal consequences of waterborne cryptosporidiosis among immunocompromised populations.

Anomalous High Rainfall and Soil Saturation as Combined Risk Indicator of Rift Valley Fever Outbreaks, South Africa, 2008-2011.

PubMed

Williams, Roy; Malherbe, Johan; Weepener, Harold; Majiwa, Phelix; Swanepoel, Robert

2016-12-01

Rift Valley fever (RVF), a zoonotic vectorborne viral disease, causes loss of life among humans and livestock and an adverse effect on the economy of affected countries. Vaccination is the most effective way to protect livestock; however, during protracted interepidemic periods, farmers discontinue vaccination, which leads to loss of herd immunity and heavy losses of livestock when subsequent outbreaks occur. Retrospective analysis of the 2008-2011 RVF epidemics in South Africa revealed a pattern of continuous and widespread seasonal rainfall causing substantial soil saturation followed by explicit rainfall events that flooded dambos (seasonally flooded depressions), triggering outbreaks of disease. Incorporation of rainfall and soil saturation data into a prediction model for major outbreaks of RVF resulted in the correctly identified risk in nearly 90% of instances at least 1 month before outbreaks occurred; all indications are that irrigation is of major importance in the remaining 10% of outbreaks.

[Implementation of vanA and vanB genes by PCR technique research interest in system (Xpert vanA/vanB CepheidR) closed in a laboratory of microbiology in managing an outbreak to Enterococcus faecium resistant glycopeptide (EfRG)].

PubMed

Dekeyser, S; Beclin, E; Descamps, D

2011-04-01

The closed system PCR for the rapid detection of vanA and vanB genes (Xpert vanA/vanB Cepheid(®)) was evaluated in our laboratory, to improve the rapidity of the response and thus the management of patients and isolation measures during two GRE outbreaks. From March to December2009, 565 samples were analysed by PCR associated to bacterial culture initially for all samples for 2months (n = 75), and thereafter for PCR-positive samples only. In this study, sensitivity and negative predictive values of the PCR were 100%. Specificity was evaluated in the presence and absence of outbreak: 69.3 and 76.8% respectively. The variability of false positive rates between units were lower in nonepidemic than during epidemic phase. The global false positive rate was 23.9%. This easy-to-use technology provides rapid results… four samples are tested in 1h versus 72h for culture. Despite its reagent cost, it represents an important hospital diagnostic tool: improvement of the management of cohorting areas and patient transfer between units, adaptation of isolation measures and treatments. However, culture remains necessary to confirm any positive result obtained by PCR and for epidemiological surveillance. Copyright © 2010 Elsevier Masson SAS. All rights reserved.

Auditing the management of vaccine-preventable disease outbreaks: the need for a tool.

PubMed

Torner, Nuria; Carnicer-Pont, Dolors; Castilla, Jesus; Cayla, Joan; Godoy, Pere; Dominguez, Angela

2011-01-13

Public health activities, especially infectious disease control, depend on effective teamwork. We present the results of a pilot audit questionnaire aimed at assessing the quality of public health services in the management of VPD outbreaks. Audit questionnaire with three main areas indicators (structure, process and results) was developed. Guidelines were set and each indicator was assessed by three auditors. Differences in indicator scores according to median size of outbreaks were determined by ANOVA (significance at p≤0.05). Of 154 outbreaks; eighteen indicators had a satisfactory mean score, indicator "updated guidelines" and "timely reporting" had a poor mean score (2.84±106 and 2.44±1.67, respectively). Statistically significant differences were found according to outbreak size, in the indicators "availability of guidelines/protocol updated less than 3 years ago" (p = 0.03) and "days needed for outbreak control" (p = 0.04). Improving availability of updated guidelines, enhancing timely reporting and adequate recording of control procedures taken is needed to allow for management assessment and improvement.

Mapping as a tool for predicting the risk of anthrax outbreaks in Northern Region of Ghana.

PubMed

Nsoh, Ayamdooh Evans; Kenu, Ernest; Forson, Eric Kofi; Afari, Edwin; Sackey, Samuel; Nyarko, Kofi Mensah; Yebuah, Nathaniel

2016-01-01

Anthrax is a febrile soil-born infectious disease that can affect all warm-blooded animals including man. Outbreaks of anthrax have been reported in northern region of Ghana but no concerted effort has been made to implement risk-based surveillance systems to document outbreaks so as to implement policies to address the disease. We generated predictive maps using soil pH, temperature and rainfall as predictor variables to identify hotspot areas for the outbreaks. A 10-year secondary data records on soil pH, temperature and rainfall were used to create climate-based risk maps using ArcGIS 10.2. The monthly mean values of rainfall and temperature for ten years were calculated and anthrax related evidence based constant raster values were created as weights for the three factors. All maps were generated using the Kriging interpolation method. There were 43 confirmed outbreaks. The deaths involved were 131 cattle, 44 sheep, 15 goats, 562 pigs with 6 human deaths and 22 developed cutaneous anthrax. We found three strata of well delineated distribution pattern indicating levels of risk due to suitability of area for anthrax spore survival. The likelihood of outbreaks occurrence and reoccurrence was higher in Strata I, Strata II and strata III respectively in descending order, due to the suitability of soil pH, temperature and rainfall for the survival and dispersal of B. anthracis spore. The eastern corridor of Northern region is a Hots spot area. Policy makers can develop risk based surveillance system and focus on this area to mitigate anthrax outbreaks and reoccurrence.

Characterizing Open Water Bodies and Their Color Properties Through Optical Remote Sensing to Identify Areas of Vector-Borne Disease Risk

NASA Astrophysics Data System (ADS)

Podest, E.; De La Torre Juarez, M.; McDonald, K. C.; Jensen, K.; Ceccato, P.

2014-12-01

Predicting the risk of vector-borne disease outbreaks is a required step towards their control and eradication. Satellite observations can provide needed data to support agency decisions with respect to deployment of preventative measures and control resources. The coverage and persistence of open water is one of the primary indicators of conditions suitable for mosquito breeding habitats. This is currently a poorly measured variable due to its spatial and temporal variability across landscapes, especially in remote areas. Here we develop a methodology for monitoring these conditions through optical remote sensing images from Landsat. We pansharpen the images and apply a decision tree classification approach using Random Forests to generate 15 meter resolution maps of open water. In addition, since some mosquitos breed in clear water while others in turbid water, we classify water bodies according to their water color properties and we validate the results using field knowledge. We focus in East Africa where we assses the usefulness of these products to improve prediction of malaria outbreaks. Portions of this work were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Impact of foot-and-mouth disease on milk production on a large-scale dairy farm in Kenya.

PubMed

Lyons, Nicholas A; Alexander, Neal; Stärk, Katharina D C; Dulu, Thomas D; Sumption, Keith J; James, Andrew D; Rushton, Jonathan; Fine, Paul E M

2015-06-15

The economic impact of foot-and-mouth disease (FMD) has been poorly characterised particularly in endemic settings where such knowledge is important for decision-making on disease control with limited resources. In order to address this, a study was designed using individual animal data from a large-scale dairy farm in Kenya to estimate the impact of an FMD outbreak due to serotype SAT2 virus on milk yield. Daily milk yields from 218 mainly European-breed cattle that were lactating during the 29-day outbreak period were considered in the analysis. At the herd level, the average daily yields decreased from around 20 to 13kg per cow, recovering approximately 2 months after the commencement of the outbreak. Generalised estimating equations (GEE) and an autoregressive correlation matrix were used to compare yields of reported clinical FMD cases and non-cases. No difference was found between reported clinical and non-clinical cases suggesting inaccurate case recording, poor sensitivity of the case definition and subclinical infections being present. To further investigate the impact of FMD, yields were predicted for each individual animal based on historic data from the same herd using a similar GEE approach. For cattle lactating during the outbreak, comparisons were made between actual and predicted yields from the commencement of the outbreak to 305 days lactation using a linear regression model. Animals produced significantly less than predicted if in parity 2 or greater and between 0 and 50 days in milk (DIM) at the start of the outbreak period. The maximum effect was seen among animals in parity ≥4 and between 0 and 50 DIM at the start of the outbreak, producing on average 688.7kg (95%CI 395.5, 981.8) less milk than predicted for their remaining lactation, representing an average 15% reduction in the 305 day production for these animals. Generalisation of the results requires caution as the majority of Kenyan milk is produced in smallholder farms. However, such farms use similar genetics and feeding practices to the study farm, and such systems are increasingly important in the supply of milk globally. These results make an important and unique contribution to the evidence base on FMD impact among dairy cattle in an endemic setting. Copyright © 2015 Elsevier B.V. All rights reserved.

The use and role of predictive systems in disease management.

PubMed

Gent, David H; Mahaffee, Walter F; McRoberts, Neil; Pfender, William F

2013-01-01

Disease predictive systems are intended to be management aids. With a few exceptions, these systems typically do not have direct sustained use by growers. Rather, their impact is mostly pedagogic and indirect, improving recommendations from farm advisers and shaping management concepts. The degree to which a system is consulted depends on the amount of perceived new, actionable information that is consistent with the objectives of the user. Often this involves avoiding risks associated with costly disease outbreaks. Adoption is sensitive to the correspondence between the information a system delivers and the information needed to manage a particular pathosystem at an acceptable financial risk; details of the approach used to predict disease risk are less important. The continuing challenge for researchers is to construct tools relevant to farmers and their advisers that improve upon their current management skill. This goal requires an appreciation of growers' decision calculus in managing disease problems and, more broadly, their overall farm enterprise management.

Forecasting the spatial transmission of influenza in the United States.

PubMed

Pei, Sen; Kandula, Sasikiran; Yang, Wan; Shaman, Jeffrey

2018-03-13

Recurrent outbreaks of seasonal and pandemic influenza create a need for forecasts of the geographic spread of this pathogen. Although it is well established that the spatial progression of infection is largely attributable to human mobility, difficulty obtaining real-time information on human movement has limited its incorporation into existing infectious disease forecasting techniques. In this study, we develop and validate an ensemble forecast system for predicting the spatiotemporal spread of influenza that uses readily accessible human mobility data and a metapopulation model. In retrospective state-level forecasts for 35 US states, the system accurately predicts local influenza outbreak onset,-i.e., spatial spread, defined as the week that local incidence increases above a baseline threshold-up to 6 wk in advance of this event. In addition, the metapopulation prediction system forecasts influenza outbreak onset, peak timing, and peak intensity more accurately than isolated location-specific forecasts. The proposed framework could be applied to emergent respiratory viruses and, with appropriate modifications, other infectious diseases.

Disease prevention versus data privacy: using landcover maps to inform spatial epidemic models.

PubMed

Tildesley, Michael J; Ryan, Sadie J

2012-01-01

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock.

Disease Prevention versus Data Privacy: Using Landcover Maps to Inform Spatial Epidemic Models

PubMed Central

Tildesley, Michael J.; Ryan, Sadie J.

2012-01-01

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock. PMID:23133352

Evaluation and comparison of statistical methods for early temporal detection of outbreaks: A simulation-based study

PubMed Central

Le Strat, Yann

2017-01-01

The objective of this paper is to evaluate a panel of statistical algorithms for temporal outbreak detection. Based on a large dataset of simulated weekly surveillance time series, we performed a systematic assessment of 21 statistical algorithms, 19 implemented in the R package surveillance and two other methods. We estimated false positive rate (FPR), probability of detection (POD), probability of detection during the first week, sensitivity, specificity, negative and positive predictive values and F1-measure for each detection method. Then, to identify the factors associated with these performance measures, we ran multivariate Poisson regression models adjusted for the characteristics of the simulated time series (trend, seasonality, dispersion, outbreak sizes, etc.). The FPR ranged from 0.7% to 59.9% and the POD from 43.3% to 88.7%. Some methods had a very high specificity, up to 99.4%, but a low sensitivity. Methods with a high sensitivity (up to 79.5%) had a low specificity. All methods had a high negative predictive value, over 94%, while positive predictive values ranged from 6.5% to 68.4%. Multivariate Poisson regression models showed that performance measures were strongly influenced by the characteristics of time series. Past or current outbreak size and duration strongly influenced detection performances. PMID:28715489

Predicting county-level southern pine beetle outbreaks from neighborhood patterns

USDA-ARS?s Scientific Manuscript database

The southern pine beetle (Dendroctonus frontalis, Coleoptera: Curculionidae) is the most destructive insect in southern forests. States have kept county-level records on the locations of beetle outbreaks for the past forty-eight years. In this study, we seek to determine how accurately patterns of c...

Data modeling for detection of epidemic outbreak

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.; Jaenisch, Kristina L.; Conn, Michael S.; Faucheux, Jeffrey P.

2005-05-01

Data Modeling is successfully applied to outbreak detection using epidemicological time series data. With proper selection of features, same day detection was demonstrated. Predictive Data Models are derived from the features in the form of integro-differential equations or their solution. These models are used as real-time change detectors. Data Modeling enables change detection using only nominal (no-outbreak) examples for training. Modeling naturally occurring dynamics due to assignable causes such as flu season enables distinction to be made of chemical and biological (chem-bio) causes.

A climate-based prediction model in the high-risk clusters of the Mekong Delta region, Vietnam: towards improving dengue prevention and control.

PubMed

Phung, Dung; Talukder, Mohammad Radwanur Rahman; Rutherford, Shannon; Chu, Cordia

2016-10-01

To develop a prediction score scheme useful for prevention practitioners and authorities to implement dengue preparedness and controls in the Mekong Delta region (MDR). We applied a spatial scan statistic to identify high-risk dengue clusters in the MDR and used generalised linear-distributed lag models to examine climate-dengue associations using dengue case records and meteorological data from 2003 to 2013. The significant predictors were collapsed into categorical scales, and the β-coefficients of predictors were converted to prediction scores. The score scheme was validated for predicting dengue outbreaks using ROC analysis. The north-eastern MDR was identified as the high-risk cluster. A 1 °C increase in temperature at lag 1-4 and 5-8 weeks increased the dengue risk 11% (95% CI, 9-13) and 7% (95% CI, 6-8), respectively. A 1% rise in humidity increased dengue risk 0.9% (95% CI, 0.2-1.4) at lag 1-4 and 0.8% (95% CI, 0.2-1.4) at lag 5-8 weeks. Similarly, a 1-mm increase in rainfall increased dengue risk 0.1% (95% CI, 0.05-0.16) at lag 1-4 and 0.11% (95% CI, 0.07-0.16) at lag 5-8 weeks. The predicted scores performed with high accuracy in diagnosing the dengue outbreaks (96.3%). This study demonstrates the potential usefulness of a dengue prediction score scheme derived from complex statistical models for high-risk dengue clusters. We recommend a further study to examine the possibility of incorporating such a score scheme into the dengue early warning system in similar climate settings. © 2016 John Wiley & Sons Ltd.

Evaluation of Syndromic Surveillance Systems in 6 US State and Local Health Departments.

PubMed

Thomas, Mathew J; Yoon, Paula W; Collins, James M; Davidson, Arthur J; Mac Kenzie, William R

Evaluating public health surveillance systems is critical to ensuring that conditions of public health importance are appropriately monitored. Our objectives were to qualitatively evaluate 6 state and local health departments that were early adopters of syndromic surveillance in order to (1) understand the characteristics and current uses, (2) identify the most and least useful syndromes to monitor, (3) gauge the utility for early warning and outbreak detection, and (4) assess how syndromic surveillance impacted their daily decision making. We adapted evaluation guidelines from the Centers for Disease Control and Prevention and gathered input from the Centers for Disease Control and Prevention subject matter experts in public health surveillance to develop a questionnaire. We interviewed staff members from a convenience sample of 6 local and state health departments with syndromic surveillance programs that had been in operation for more than 10 years. Three of the 6 interviewees provided an example of using syndromic surveillance to identify an outbreak (ie, cluster of foodborne illness in 1 jurisdiction) or detect a surge in cases for seasonal conditions (eg, influenza in 2 jurisdictions) prior to traditional, disease-specific systems. Although all interviewees noted that syndromic surveillance has not been routinely useful or efficient for early outbreak detection or case finding in their jurisdictions, all agreed that the information can be used to improve their understanding of dynamic disease control environments and conditions (eg, situational awareness) in their communities. In the jurisdictions studied, syndromic surveillance may be useful for monitoring the spread and intensity of large outbreaks of disease, especially influenza; enhancing public health awareness of mass gatherings and natural disasters; and assessing new, otherwise unmonitored conditions when real-time alternatives are unavailable. Future studies should explore opportunities to strengthen syndromic surveillance by including broader access to and enhanced analysis of text-related data from electronic health records. Health departments may accelerate the development and use of syndromic surveillance systems, including the improvement of the predictive value and strengthening the early outbreak detection capability of these systems. These efforts support getting the right information to the right people at the right time, which is the overarching goal of CDC's Surveillance Strategy.

Selection tool for foodborne norovirus outbreaks.

PubMed

Verhoef, Linda P B; Kroneman, Annelies; van Duynhoven, Yvonne; Boshuizen, Hendriek; van Pelt, Wilfrid; Koopmans, Marion

2009-01-01

Detection of pathogens in the food chain is limited mainly to bacteria, and the globalization of the food industry enables international viral foodborne outbreaks to occur. Outbreaks from 2002 through 2006 recorded in a European norovirus surveillance database were investigated for virologic and epidemiologic indicators of food relatedness. The resulting validated multivariate logistic regression model comparing foodborne (n = 224) and person-to-person (n = 654) outbreaks was used to create a practical web-based tool that can be limited to epidemiologic parameters for nongenotyping countries. Non-genogroup-II.4 outbreaks, higher numbers of cases, and outbreaks in restaurants or households characterized (sensitivity = 0.80, specificity = 0.86) foodborne outbreaks and reduced the percentage of outbreaks requiring source-tracing to 31%. The selection tool enabled prospectively focused follow-up. Use of this tool is likely to improve data quality and strain typing in current surveillance systems, which is necessary for identification of potential international foodborne outbreaks.

Nowcasting the spread of chikungunya virus in the Americas.

PubMed

Johansson, Michael A; Powers, Ann M; Pesik, Nicki; Cohen, Nicole J; Staples, J Erin

2014-01-01

In December 2013, the first locally-acquired chikungunya virus (CHIKV) infections in the Americas were reported in the Caribbean. As of May 16, 55,992 cases had been reported and the outbreak was still spreading. Identification of newly affected locations is paramount to intervention activities, but challenging due to limitations of current data on the outbreak and on CHIKV transmission. We developed models to make probabilistic predictions of spread based on current data considering these limitations. Branching process models capturing travel patterns, local infection prevalence, climate dependent transmission factors, and associated uncertainty estimates were developed to predict probable locations for the arrival of CHIKV-infected travelers and for the initiation of local transmission. Many international cities and areas close to where transmission has already occurred were likely to have received infected travelers. Of the ten locations predicted to be the most likely locations for introduced CHIKV transmission in the first four months of the outbreak, eight had reported local cases by the end of April. Eight additional locations were likely to have had introduction leading to local transmission in April, but with substantial uncertainty. Branching process models can characterize the risk of CHIKV introduction and spread during the ongoing outbreak. Local transmission of CHIKV is currently likely in several Caribbean locations and possible, though uncertain, for other locations in the continental United States, Central America, and South America. This modeling framework may also be useful for other outbreaks where the risk of pathogen spread over heterogeneous transportation networks must be rapidly assessed on the basis of limited information.

The dynamics of transmission and the dynamics of networks.

PubMed

Farine, Damien

2017-05-01

A toy example depicted here highlighting the results of a study in this issue of the Journal of Animal Ecology that investigates the impact of network dynamics on potential disease outbreaks. Infections (stars) that spread by contact only (left) reduce the predicted outbreak size compared to situations where individuals can become infected by moving through areas that previously contained infected individuals (right). This is potentially important in species where individuals, or in this case groups, have overlapping ranges (as depicted on the top right). Incorporating network dynamics that maintain information about the ordering of contacts (central blocks; including the ordering of spatial overlap as noted by the arrows that highlight the blue group arriving after the red group in top-right of the figure) is important for capturing how a disease might not have the opportunity to spread to all individuals. By contrast, a static or 'average' network (lower blocks) does not capture any of these dynamics. Interestingly, although static networks generally predict larger outbreak sizes, the authors find that in cases when transmission probability is low, this prediction can switch as a result of changes in the estimated intensity of contacts among individuals. [Colour figure can be viewed at wileyonlinelibrary.com]. Springer, A., Kappeler, P.M. & Nunn, C.L. (2017) Dynamic vs. static social networks in models of parasite transmission: Predicting Cryptosporidium spread in wild lemurs. Journal of Animal Ecology, 86, 419-433. The spread of disease or information through networks can be affected by several factors. Whether and how these factors are accounted for can fundamentally change the predicted impact of a spreading epidemic. Springer, Kappeler & Nunn () investigate the role of different modes of transmission and network dynamics on the predicted size of a disease outbreak across several groups of Verreaux's sifakas, a group-living species of lemur. While some factors, such as seasonality, led to consistent differences in the structure of social networks, using dynamic vs. static representations of networks generated differences in the predicted outbreak size of an emergent disease. These findings highlight some of the challenges associated with studying disease dynamics in animal populations, and the importance of continuing efforts to develop the network tools needed to study disease spread. © 2017 The Author. Journal of Animal Ecology © 2017 British Ecological Society.

Models for short term malaria prediction in Sri Lanka

PubMed Central

Briët, Olivier JT; Vounatsou, Penelope; Gunawardena, Dissanayake M; Galappaththy, Gawrie NL; Amerasinghe, Priyanie H

2008-01-01

Background Malaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control. Methods Exponentially weighted moving average models, autoregressive integrated moving average (ARIMA) models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA) models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal) ARIMA models. Results The best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal) ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons. Conclusion Heterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts) for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed. PMID:18460204

Rapid detection of foodborne botulism outbreaks facilitated by epidemiological linking of cases: implications for food defense and public health response.

PubMed

Newkirk, Ryan W; Hedberg, Craig W

2012-02-01

The main objective of this study was to develop an understanding of the descriptive epidemiology of foodborne botulism in the context of outbreak detection and food defense. This study used 1993-2008 data from the Centers for Disease Control and Prevention (CDC) Annual Summaries of Notifiable Diseases, 2003-2006 data from the Bacterial Foodborne and Diarrheal Disease National Case Surveillance Annual Reports, and 1993-2008 data from the Annual Listing of Foodborne Disease Outbreaks. Published outbreak investigation reports were identified through a PubMed search of MEDLINE citations for botulism outbreaks. Fifty-eight foodborne botulism outbreaks were reported to CDC between 1993 and 2008. Four hundred sixteen foodborne botulism cases were documented; 205 (49%) were associated with outbreaks. Familial connections and co-hospitalization of initial presenting cases were common in large outbreaks (>5 cases). In these outbreaks, the time from earliest exposure to outbreak recognition varied dramatically (range, 48-216 h). The identification of epidemiologic linkages between foodborne botulism cases is a critical part of diagnostic evaluation and outbreak detection. Investigation of an intentionally contaminated food item with a long shelf life and widespread distribution may be delayed until an astute physician suspects foodborne botulism; suspicion of foodborne botulism occurs more frequently when more than one case is hospitalized concurrently. In an effort to augment national botulism surveillance and antitoxin release systems and to improve food defense and public health preparedness efforts, medical organizations and Homeland Security officials should emphasize the education and training of medical personnel to improve foodborne botulism diagnostic capabilities to recognize single foodborne botulism cases and to look for epidemiologic linkages between suspected cases.

Evaluation of a statewide foodborne illness complaint surveillance system in Minnesota, 2000 through 2006.

PubMed

Li, John; Smith, Kirk; Kaehler, Dawn; Everstine, Karen; Rounds, Josh; Hedberg, Craig

2010-11-01

Foodborne outbreaks are detected by recognition of similar illnesses among persons with a common exposure or by identification of case clusters through pathogen-specific surveillance. PulseNet USA has created a national framework for pathogen-specific surveillance, but no comparable effort has been made to improve surveillance of consumer complaints of suspected foodborne illness. The purpose of this study was to characterize the complaint surveillance system in Minnesota and to evaluate its use for detecting outbreaks. Minnesota Department of Health foodborne illness surveillance data from 2000 through 2006 were analyzed for this study. During this period, consumer complaint surveillance led to detection of 79% of confirmed foodborne outbreaks. Most norovirus infection outbreaks were detected through complaints. Complaint surveillance also directly led or contributed to detection of 25% of salmonellosis outbreaks. Eighty-one percent of complainants did not seek medical attention. The number of ill persons in a complainant's party was significantly associated with a complaint ultimately resulting in identification of a foodborne outbreak. Outbreak confirmation was related to a complainant's ability to identify a common exposure and was likely related to the process by which the Minnesota Department of Health chooses complaints to investigate. A significant difference (P < 0.001) was found in incubation periods between complaints that were outbreak associated (median, 27 h) and those that were not outbreak associated (median, 6 h). Complaint systems can be used to detect outbreaks caused by a variety of pathogens. Case detection for foodborne disease surveillance in Minnesota happens through a multitude of mechanisms. The ability to integrate these mechanisms and carry out rapid investigations leads to improved outbreak detection.

Support vector machine applied to predict the zoonotic potential of E. coli O157 cattle isolates

USDA-ARS?s Scientific Manuscript database

Methods based on sequence data analysis facilitate the tracking of disease outbreaks, allow relationships between strains to be reconstructed and virulence factors to be identified. However, these methods are used postfactum after an outbreak has happened. Here, we show that support vector machine a...

Global Distribution of Outbreaks of Water-Associated Infectious Diseases

PubMed Central

Yang, Kun; LeJeune, Jeffrey; Alsdorf, Doug; Lu, Bo; Shum, C. K.; Liang, Song

2012-01-01

Background Water plays an important role in the transmission of many infectious diseases, which pose a great burden on global public health. However, the global distribution of these water-associated infectious diseases and underlying factors remain largely unexplored. Methods and Findings Based on the Global Infectious Disease and Epidemiology Network (GIDEON), a global database including water-associated pathogens and diseases was developed. In this study, reported outbreak events associated with corresponding water-associated infectious diseases from 1991 to 2008 were extracted from the database. The location of each reported outbreak event was identified and geocoded into a GIS database. Also collected in the GIS database included geo-referenced socio-environmental information including population density (2000), annual accumulated temperature, surface water area, and average annual precipitation. Poisson models with Bayesian inference were developed to explore the association between these socio-environmental factors and distribution of the reported outbreak events. Based on model predictions a global relative risk map was generated. A total of 1,428 reported outbreak events were retrieved from the database. The analysis suggested that outbreaks of water-associated diseases are significantly correlated with socio-environmental factors. Population density is a significant risk factor for all categories of reported outbreaks of water-associated diseases; water-related diseases (e.g., vector-borne diseases) are associated with accumulated temperature; water-washed diseases (e.g., conjunctivitis) are inversely related to surface water area; both water-borne and water-related diseases are inversely related to average annual rainfall. Based on the model predictions, “hotspots” of risks for all categories of water-associated diseases were explored. Conclusions At the global scale, water-associated infectious diseases are significantly correlated with socio-environmental factors, impacting all regions which are affected disproportionately by different categories of water-associated infectious diseases. PMID:22348158

Detecting and Responding to a Dengue Outbreak: Evaluation of Existing Strategies in Country Outbreak Response Planning

PubMed Central

Kroeger, Axel; Runge-Ranzinger, Silvia; O'Dempsey, Tim

2013-01-01

Background. Dengue outbreaks are occurring with increasing frequency and intensity. Evidence-based epidemic preparedness and effective response are now a matter of urgency. Therefore, we have analysed national and municipal dengue outbreak response plans. Methods. Thirteen country plans from Asia, Latin America and Australia, and one international plan were obtained from the World Health Organization. The information was transferred to a data analysis matrix where information was extracted according to predefined and emerging themes and analysed for scope, inconsistencies, omissions, and usefulness. Findings. Outbreak response planning currently has a considerable number of flaws. Outbreak governance was weak with a lack of clarity of stakeholder roles. Late timing of responses due to poor surveillance, a lack of combining routine data with additional alerts, and lack of triggers for initiating the response weakened the functionality of plans. Frequently an outbreak was not defined, and early response mechanisms based on alert signals were neglected. There was a distinct lack of consideration of contextual influences which can affect how an outbreak detection and response is managed. Conclusion. A model contingency plan for dengue outbreak prediction, detection, and response may help national disease control authorities to develop their own more detailed and functional context specific plans. PMID:24222774

Whole genome sequencing may not be adequate to determine genome relatedness for surveillance and outbreak investigation of foodborne pathogens

USDA-ARS?s Scientific Manuscript database

Introduction: Advances in genomic technologies have improve the speed and precision of foodborne disease outbreak detection and response. For the past two decades, pulsed field gel electrophoresis (PFGE) has been the method of choice for surveillance and outbreak investigation with foodborne pathoge...

Mitigating effects of vaccination on influenza outbreaks given constraints in stockpile size and daily administration capacity

PubMed Central

2011-01-01

Background Influenza viruses are a major cause of morbidity and mortality worldwide. Vaccination remains a powerful tool for preventing or mitigating influenza outbreaks. Yet, vaccine supplies and daily administration capacities are limited, even in developed countries. Understanding how such constraints can alter the mitigating effects of vaccination is a crucial part of influenza preparedness plans. Mathematical models provide tools for government and medical officials to assess the impact of different vaccination strategies and plan accordingly. However, many existing models of vaccination employ several questionable assumptions, including a rate of vaccination proportional to the population at each point in time. Methods We present a SIR-like model that explicitly takes into account vaccine supply and the number of vaccines administered per day and places data-informed limits on these parameters. We refer to this as the non-proportional model of vaccination and compare it to the proportional scheme typically found in the literature. Results The proportional and non-proportional models behave similarly for a few different vaccination scenarios. However, there are parameter regimes involving the vaccination campaign duration and daily supply limit for which the non-proportional model predicts smaller epidemics that peak later, but may last longer, than those of the proportional model. We also use the non-proportional model to predict the mitigating effects of variably timed vaccination campaigns for different levels of vaccination coverage, using specific constraints on daily administration capacity. Conclusions The non-proportional model of vaccination is a theoretical improvement that provides more accurate predictions of the mitigating effects of vaccination on influenza outbreaks than the proportional model. In addition, parameters such as vaccine supply and daily administration limit can be easily adjusted to simulate conditions in developed and developing nations with a wide variety of financial and medical resources. Finally, the model can be used by government and medical officials to create customized pandemic preparedness plans based on the supply and administration constraints of specific communities. PMID:21806800

Outbreaks of infectious intestinal disease associated with person to person spread in hotels and restaurants.

PubMed

McDonnell, R J; Wall, P G; Adak, G K; Evans, H S; Cowden, J M; Caul, E O

1995-09-15

Twenty-eight outbreaks of infectious intestinal disease, reported as being transmitted mainly by the person to person route, were identified in association with retail catering premises, such as hotels, restaurants, and public houses, in England and Wales between 1992 and 1994. Five thousand and forty-eight people were at risk in these outbreaks and 1234 were affected. Most of the outbreaks (over 90%) occurred in hotels. Small round structured viruses were the most commonly detected pathogens. Diarrhoea and vomiting were common symptoms and most of the outbreaks occurred in the summer months. Control measures to contain infectious individuals and improved hygiene measures are necessary to contain such outbreaks.

Superensemble forecasts of dengue outbreaks

PubMed Central

Kandula, Sasikiran; Shaman, Jeffrey

2016-01-01

In recent years, a number of systems capable of predicting future infectious disease incidence have been developed. As more of these systems are operationalized, it is important that the forecasts generated by these different approaches be formally reconciled so that individual forecast error and bias are reduced. Here we present a first example of such multi-system, or superensemble, forecast. We develop three distinct systems for predicting dengue, which are applied retrospectively to forecast outbreak characteristics in San Juan, Puerto Rico. We then use Bayesian averaging methods to combine the predictions from these systems and create superensemble forecasts. We demonstrate that on average, the superensemble approach produces more accurate forecasts than those made from any of the individual forecasting systems. PMID:27733698

El Niño Southern Oscillation and Leptospirosis Outbreaks in New Caledonia

PubMed Central

Weinberger, Daniel; Baroux, Noémie; Grangeon, Jean-Paul; Ko, Albert I.; Goarant, Cyrille

2014-01-01

Leptospirosis is an important cause of seasonal outbreaks in New Caledonia and the tropics. Using time series derived from high-quality laboratory-based surveillance from 2000–2012, we evaluated whether climatic factors, including El Niño Southern Oscillation (ENSO) and meteorological conditions allow for the prediction of leptospirosis outbreaks in New Caledonia. We found that La Niña periods are associated with high rainfall, and both of these factors were in turn, temporally associated with outbreaks of leptospirosis. The sea surface temperature in El Niño Box 4 allowed forecasting of leptospirosis outbreaks four months into the future, a time lag allowing public health authorities to increase preparedness. To our knowledge, our observations in New Caledonia are the first demonstration that ENSO has a strong association with leptospirosis. This association should be tested in other regions in the South Pacific, Asia or Latin America where ENSO may drive climate variability and the risk for leptospirosis outbreaks. PMID:24743322

Anomalous High Rainfall and Soil Saturation as Combined Risk Indicator of Rift Valley Fever Outbreaks, South Africa, 2008–2011

PubMed Central

Malherbe, Johan; Weepener, Harold; Majiwa, Phelix; Swanepoel, Robert

2016-01-01

Rift Valley fever (RVF), a zoonotic vectorborne viral disease, causes loss of life among humans and livestock and an adverse effect on the economy of affected countries. Vaccination is the most effective way to protect livestock; however, during protracted interepidemic periods, farmers discontinue vaccination, which leads to loss of herd immunity and heavy losses of livestock when subsequent outbreaks occur. Retrospective analysis of the 2008–2011 RVF epidemics in South Africa revealed a pattern of continuous and widespread seasonal rainfall causing substantial soil saturation followed by explicit rainfall events that flooded dambos (seasonally flooded depressions), triggering outbreaks of disease. Incorporation of rainfall and soil saturation data into a prediction model for major outbreaks of RVF resulted in the correctly identified risk in nearly 90% of instances at least 1 month before outbreaks occurred; all indications are that irrigation is of major importance in the remaining 10% of outbreaks. PMID:27403563

Cryptosporidiosis-associated mortality following a massive waterborne outbreak in Milwaukee, Wisconsin.

PubMed Central

Hoxie, N J; Davis, J P; Vergeront, J M; Nashold, R D; Blair, K A

1997-01-01

OBJECTIVES: This study estimated the magnitude of cryptosporidiosis-associated mortality in the Milwaukee vicinity for 2 years following a massive waterborne outbreak. METHODS: Death certificates were reviewed. RESULTS: During approximately 2 years before the outbreak, cryptosporidiosis was listed as an underlying or contributing cause of death on the death certificates of four Milwaukee-vicinity residents. In the approximately 2 years after the outbreak, this number was 54, of whom 85% had acquired immunodeficiency syndrome (AIDS) listed as the underlying cause of death. In the first 6 months after the outbreak, the number of death certificates indicating AIDS, but not cryptosporidiosis, as a cause of death was 19 (95% confidence interval = 12.26) higher than preoutbreak trends would have predicted. CONCLUSIONS: Waterborne outbreaks of cryptosporidium infection can result in significant mortality, particularly among immunocompromised populations. Any discussion of policies to ensure safe drinking water must consider the potential fatal consequences of waterborne cryptosporidiosis among immunocompromised populations. Images FIGURE 2 PMID:9431298

Food- and waterborne disease outbreaks in Australian long-term care facilities, 2001-2008.

PubMed

Kirk, Martyn D; Lalor, Karin; Raupach, Jane; Combs, Barry; Stafford, Russell; Hall, Gillian V; Becker, Niels

2011-01-01

Abstract Food- or waterborne diseases in long-term care facilities (LTCF) can result in serious outcomes, including deaths, and they are potentially preventable. We analyzed data collected by OzFoodNet on food- and waterborne disease outbreaks occurring in LTCF in Australia from 2001 to 2008. We compared outbreaks by the number of persons affected, etiology, and implicated vehicle. During 8 years of surveillance, 5.9% (55/936) of all food- and waterborne outbreaks in Australia occurred in LTCF. These LTCF outbreaks affected a total of 909 people, with 66 hospitalized and 23 deaths. The annual incidence of food- or waterborne outbreaks was 1.9 (95% confidence intervals 1.0-3.7) per 1000 facilities. Salmonella caused 17 outbreaks, Clostridium perfringens 14 outbreaks, Campylobacter 8 outbreaks, and norovirus 1 outbreak. Residents were at higher risk of death during outbreaks of salmonellosis than for all other outbreaks combined (relative risk 7.8, 95% confidence intervals 1.8-33.8). Of 15 outbreaks of unknown etiology, 11 were suspected to be due to C. perfringens intoxication. Food vehicles were only identified in 27% (14/52) of outbreaks, with six outbreak investigations implicating pureed foods. Dishes containing raw eggs were implicated as the cause of four outbreaks. Three outbreaks of suspected waterborne disease were attributed to rainwater collected from facility roofs. To prevent disease outbreaks, facilities need to improve handling of pureed foods, avoid feeding residents raw or undercooked eggs, and ensure that rainwater tanks have a scheduled maintenance and disinfection program.

The 2016 outbreak on Jupiter's North Temperate Belt and jet from ground-based and Juno imaging

NASA Astrophysics Data System (ADS)

Rogers, J. H.; Orton, G. S.; Eichstädt, G.; Vedovato, M.; Caplinger, M.; Momary, T. W.; Hansen, C. J.

2017-09-01

A new outbreak of convective plumes on the peak of Jupiter's fastest jet, which had been predicted the previous year, began in autumn, 2016. It was observed just after solar conjunction by the NASA Infrared Telescope Facility, by JunoCam, and by amateur astronomers. It unfolded in essentially the same way as previous such outbreaks, leading to revival of the North Temperate Belt with a notably red component. The maturation of this belt was monitored at high resolution by JunoCam.

First reported chikungunya fever outbreak in the republic of Congo, 2011.

PubMed

Moyen, Nanikaly; Thiberville, Simon-Djamel; Pastorino, Boris; Nougairede, Antoine; Thirion, Laurence; Mombouli, Jean-Vivien; Dimi, Yannick; Leparc-Goffart, Isabelle; Capobianchi, Maria Rosaria; Lepfoundzou, Amelia Dzia; de Lamballerie, Xavier

2014-01-01

Chikungunya is an Aedes -borne disease characterised by febrile arthralgia and responsible for massive outbreaks. We present a prospective clinical cohort study and a retrospective serological study relating to a CHIK outbreak, in the Republic of Congo in 2011. We analysed 317 suspected cases, of which 308 (97.2%) lived in the city of Brazzaville (66.6% in the South area). Amongst them, 37 (11.7%) were CHIKV+ve patients (i.e., biologically confirmed by a real-time RT-PCR assay), of whom 36 (97.3%) had fever, 22 (66.7%) myalgia and 32 (86.5%) arthralgia. All tested negative for dengue. The distribution of incident cases within Brazzaville districts was compared with CHIKV seroprevalence before the outbreak (34.4% in 517 blood donors), providing evidence for previous circulation of CHIKV. We applied a CHIK clinical score to 126 patients recruited within the two first day of illness (including 28 CHIKV+ves (22.2%)) with sensitivity (78.6%) and specificity (72.4%) values comparing with those of the referent study in Reunion Island. The negative predictive value was high (92%), but the positive predictive value (45%) indicate poor potential contribution to medical practice to identify CHIKV+ve patients in low prevalence outbreaks. However, the score allowed a slightly more accurate follow-up of the evolution of the outbreak than the criterion "fever+arthralgia". The complete sequencing of a Congolase isolate (Brazza_MRS1) demonstrated belonging to the East/Central/South African lineage and was further used for producing a robust genome-scale CHIKV phylogenetic analysis. We describe the first Chikungunya outbreak declared in the Republic of Congo. The seroprevalence study conducted amongst blood donors before outbreak provided evidence for previous CHIKV circulation. We suggest that a more systematic survey of the entomological situation and of arbovirus circulation is necessary in Central Africa for better understanding the environmental, microbiological and sociological determinants of emergence.

First Reported Chikungunya Fever Outbreak in the Republic of Congo, 2011

PubMed Central

Pastorino, Boris; Nougairede, Antoine; Thirion, Laurence; Mombouli, Jean-Vivien; Dimi, Yannick; Leparc-Goffart, Isabelle; Capobianchi, Maria Rosaria; Lepfoundzou, Amelia Dzia; de Lamballerie, Xavier

2014-01-01

Background Chikungunya is an Aedes -borne disease characterised by febrile arthralgia and responsible for massive outbreaks. We present a prospective clinical cohort study and a retrospective serological study relating to a CHIK outbreak, in the Republic of Congo in 2011. Methodology and Findings We analysed 317 suspected cases, of which 308 (97.2%) lived in the city of Brazzaville (66.6% in the South area). Amongst them, 37 (11.7%) were CHIKV+ve patients (i.e., biologically confirmed by a real-time RT-PCR assay), of whom 36 (97.3%) had fever, 22 (66.7%) myalgia and 32 (86.5%) arthralgia. All tested negative for dengue. The distribution of incident cases within Brazzaville districts was compared with CHIKV seroprevalence before the outbreak (34.4% in 517 blood donors), providing evidence for previous circulation of CHIKV. We applied a CHIK clinical score to 126 patients recruited within the two first day of illness (including 28 CHIKV+ves (22.2%)) with sensitivity (78.6%) and specificity (72.4%) values comparing with those of the referent study in Reunion Island. The negative predictive value was high (92%), but the positive predictive value (45%) indicate poor potential contribution to medical practice to identify CHIKV+ve patients in low prevalence outbreaks. However, the score allowed a slightly more accurate follow-up of the evolution of the outbreak than the criterion "fever+arthralgia". The complete sequencing of a Congolase isolate (Brazza_MRS1) demonstrated belonging to the East/Central/South African lineage and was further used for producing a robust genome-scale CHIKV phylogenetic analysis. Conclusions/Significance We describe the first Chikungunya outbreak declared in the Republic of Congo. The seroprevalence study conducted amongst blood donors before outbreak provided evidence for previous CHIKV circulation. We suggest that a more systematic survey of the entomological situation and of arbovirus circulation is necessary in Central Africa for better understanding the environmental, microbiological and sociological determinants of emergence. PMID:25541718

Wetland environmental conditions associated with the risk of avian cholera outbreaks and the abundance of Pasteurella multocida

USGS Publications Warehouse

Blanchong, Julie A.; Samuel, Michael D.; Goldberg, Diana R.; Shadduck, Daniel J.; Creekmore, L.H.

2006-01-01

Avian cholera is a significant infectious disease affecting waterfowl across North America and occurs worldwide among various avian species. Despite the importance of this disease, little is known about the factors that cause avian cholera outbreaks and what management strategies might be used to reduce disease mortality. Previous studies indicated that wetland water conditions may affect survival and transmission of Pasteurella multocida, the agent that causes avian cholera. These studies hypothesized that water conditions affect the likelihood that avian cholera outbreaks will occur in specific wetlands. To test these predictions, we collected data from avian cholera outbreak and non-outbreak (control) wetlands throughout North America (wintera??spring 1995a??1996 to 1998a??1999) to evaluate whether water conditions were associated with outbreaks. Conditional logistic regression analysis on paired outbreak and non-outbreak wetlands indicated no significant association between water conditions and the risk of avian cholera outbreaks. For wetlands where avian cholera outbreaks occurred, linear regression showed that increased eutrophic nutrient concentrations (Potassium [K], nitrate [NO3], phosphorus [P], and phosphate [PO3]) were positively related to the abundance of P. multocida recovered from water and sediment samples. Wetland protein concentration and an El Ni??o event were also associated with P. multocida abundance. Our results indicate that wetland water conditions are not strongly associated with the risk of avian cholera outbreaks; however, some variables may play a role in the abundance of P. multocida bacteria and might be important in reducing the severity of avian cholera outbreaks.

Investigating Coral Disease Spread Across the Hawaiian Archipelago

NASA Astrophysics Data System (ADS)

Sziklay, Jamie

Coral diseases negatively impact reef ecosystems and they are increasing worldwide; yet, we have a limited understanding of the factors that influence disease risk and transmission. My dissertation research investigated coral disease spread for several common coral diseases in the Hawaiian archipelago to understand how host-pathogenenvironment interactions vary across different spatial scales and how we can use that information to improve management strategies. At broad spatial scales, I developed forecasting models to predict outbreak risk based on depth, coral density and temperature anomalies from remotely sensed data (chapter 1). In this chapter, I determined that host density, total coral density, depth and winter temperature variation were important predictors of disease prevalence for several coral diseases. Expanding on the predictive models, I also found that colony size, wave energy, water quality, fish abundance and nearby human population size altered disease risk (chapter 2). Most of the model variation occurred at the scale of sites and coastline, indicating that local coral composition and water quality were key determinants of disease risk. At the reef scale, I investigated factors that influence disease transmission among individuals using a tissue loss disease outbreak in Kane'ohe Bay, O'ahu, Hawai'i as a case study (chapter 3). I determined that host size, proximity to infected neighbors and numbers of infected neighbors were associated with disease risk. Disease transmission events were very localized (within 15 m) and rates changed dramatically over the course of the outbreak: the transmission rate initially increased quickly during the outbreak and then decreased steadily until the outbreak ended. At the colony scale, I investigated disease progression between polyps within individual coral colonies using confocal microscopy (chapter 4). Here, I determined that fragmented florescent pigment distributions appeared adjacent to the disease front of infected coral and had fewer intact polyps than in healthy coral fragments. These results suggested that disease progression within colonies affected with chronic and acute Montipora white syndromes are highly localized rather than systemic and their bacterial pathogens directly attack the coral tissue rather than zooxanthellae. Overall, my dissertation research indicates that watershed condition and coral community configuration can facilitate and/or inhibit coral disease spread, and that disease transmission may be more spatially constrained than previously thought.

Automated real time constant-specificity surveillance for disease outbreaks.

PubMed

Wieland, Shannon C; Brownstein, John S; Berger, Bonnie; Mandl, Kenneth D

2007-06-13

For real time surveillance, detection of abnormal disease patterns is based on a difference between patterns observed, and those predicted by models of historical data. The usefulness of outbreak detection strategies depends on their specificity; the false alarm rate affects the interpretation of alarms. We evaluate the specificity of five traditional models: autoregressive, Serfling, trimmed seasonal, wavelet-based, and generalized linear. We apply each to 12 years of emergency department visits for respiratory infection syndromes at a pediatric hospital, finding that the specificity of the five models was almost always a non-constant function of the day of the week, month, and year of the study (p < 0.05). We develop an outbreak detection method, called the expectation-variance model, based on generalized additive modeling to achieve a constant specificity by accounting for not only the expected number of visits, but also the variance of the number of visits. The expectation-variance model achieves constant specificity on all three time scales, as well as earlier detection and improved sensitivity compared to traditional methods in most circumstances. Modeling the variance of visit patterns enables real-time detection with known, constant specificity at all times. With constant specificity, public health practitioners can better interpret the alarms and better evaluate the cost-effectiveness of surveillance systems.

Predicting and Mitigating Outbreaks of Vector-Borne Disease Utilizing Satellite Remote Sensing Technology and Models

NASA Technical Reports Server (NTRS)

Estes, Sue M.

2009-01-01

The Public Health application area focuses on Earth science applications to public health and safety, particularly regarding infectious disease, emergency preparedness and response, and environmental health issues. The application explores issues of toxic and pathogenic exposure, as well as natural and man-made hazards and their effects, for risk characterization/mitigation and improvements to health and safety. The program elements of the NASA Applied Sciences Program are: Agricultural Efficiency, Air Quality, Climate, Disaster Management, Ecological Forecasting, Water Resources, Weather, and Public Health.

Scabies outbreaks in residential care homes: factors associated with late recognition, burden and impact. A mixed methods study in England.

PubMed

Hewitt, K A; Nalabanda, A; Cassell, J A

2015-05-01

Scabies is an important public health problem in residential care homes. Delayed diagnosis contributes to outbreaks, which may be prolonged and difficult to control. We investigated factors influencing outbreak recognition, diagnosis and treatment, and staff experiences of outbreak control, identifying areas for intervention. We carried out a semi-structured survey of managers, affected residents and staff of seven care homes reporting suspected scabies outbreaks in southern England over a 6-month period. Attack rates ranged from 2% to 50%, and most cases had dementia (37/39, 95%). Cases were diagnosed clinically by GPs (59%) or home staff (41%), none by dermatologists. Most outbreaks were attributable to avoidably late diagnosis of the index case. Participants reported considerable challenges in managing scabies outbreaks, including late diagnosis and recognition of outbreaks; logistically difficult mass treatment; distressing treatment processes and high costs. This study demonstrates the need for improved support for care homes in detecting and managing these outbreaks.

A model to predict when a cholera outbreak might hit the Congo

NASA Astrophysics Data System (ADS)

Schultz, Colin

2014-09-01

In 2011, as many as 600,000 people in 58 countries contracted cholera, with thousands succumbing to the disease. In most countries, cholera is rare. In others, like the Democratic Republic of the Congo, cholera is an endemic threat, always lurking in the background waiting for the right set of conditions to spark an outbreak.

A Review of Hypothesized Determinants Associated with Bighorn Sheep (Ovis canadensis) Die-Offs

PubMed Central

Miller, David S.; Hoberg, Eric; Weiser, Glen; Aune, Keith; Atkinson, Mark; Kimberling, Cleon

2012-01-01

Multiple determinants have been hypothesized to cause or favor disease outbreaks among free-ranging bighorn sheep (Ovis canadensis) populations. This paper considered direct and indirect causes of mortality, as well as potential interactions among proposed environmental, host, and agent determinants of disease. A clear, invariant relationship between a single agent and field outbreaks has not yet been documented, in part due to methodological limitations and practical challenges associated with developing rigorous study designs. Therefore, although there is a need to develop predictive models for outbreaks and validated mitigation strategies, uncertainty remains as to whether outbreaks are due to endemic or recently introduced agents. Consequently, absence of established and universal explanations for outbreaks contributes to conflict among wildlife and livestock stakeholders over land use and management practices. This example illustrates the challenge of developing comprehensive models for understanding and managing wildlife diseases in complex biological and sociological environments. PMID:22567546

Stochastic Epidemic Outbreaks, or Why Epidemics Behave Like Lasers

NASA Astrophysics Data System (ADS)

Schwartz, Ira; Billings, Lora; Bollt, Erik; Carr, Thomas

2004-03-01

Many diseases, such childhood diseases, dengue fever, and West Nile virus, appear to oscillate randomly as a function of seasonal environmental or social changes. Such oscillations appear to have a chaotic bursting character, although it is still uncertain how much is due to random fluctuations. Such bursting in the presence of noise is also observed in driven lasers. In this talk, I will show how noise can excite random outbreaks in simple models of seasonally driven outbreaks, as well as lasers. The models for both population dynamics will be shown to share the same class of underlying topology, which plays a major role in the cause of observed stochastic bursting. New tools for predicting stcohastic outbreaks will be presented.

Predictive and Reactive Distribution of Vaccines and Antivirals during Cross-Regional Pandemic Outbreaks

PubMed Central

Uribe-Sánchez, Andrés; Savachkin, Alex

2011-01-01

As recently pointed out by the Institute of Medicine, the existing pandemic mitigation models lack the dynamic decision support capability. We develop a large-scale simulation-driven optimization model for generating dynamic predictive distribution of vaccines and antivirals over a network of regional pandemic outbreaks. The model incorporates measures of morbidity, mortality, and social distancing, translated into the cost of lost productivity and medical expenses. The performance of the strategy is compared to that of the reactive myopic policy, using a sample outbreak in Fla, USA, with an affected population of over four millions. The comparison is implemented at different levels of vaccine and antiviral availability and administration capacity. Sensitivity analysis is performed to assess the impact of variability of some critical factors on policy performance. The model is intended to support public health policy making for effective distribution of limited mitigation resources. PMID:23074658

[Practical lessons from the risk management of an outbreak of acute gastroenteritis in a public school].

PubMed

Morillo-García, Aurea; Sillero-Sánchez, Rocío; Aldana-Espinal, Josefa María; Nieto-Cervera, Pilar

2005-01-01

We present our reflections on the management of an acute gastroenteritis outbreak in a public school, which caused a public health crisis, and the conclusions drawn from this experience. The methodology of strengths, weaknesses, opportunities, and threats (SWOT) analysis was used. This article describes the epidemiology of the incident and the policy decisions made, but focuses on operational aspects of outbreak management. The experience of the outbreak control team, liaison with other organizations, and data management are discussed. The difficulties encountered by the outbreak team related to delay in declaring in the outbreak, lack of training in some of the entities involved, and incorrect use of the surveillance circuits. Current protocols and specific action plans for the management of outbreaks should be improved through self-evaluation and updating of resources and knowledge.

Modeling the environmental suitability of anthrax in Ghana and estimating populations at risk: Implications for vaccination and control.

PubMed

Kracalik, Ian T; Kenu, Ernest; Ayamdooh, Evans Nsoh; Allegye-Cudjoe, Emmanuel; Polkuu, Paul Nokuma; Frimpong, Joseph Asamoah; Nyarko, Kofi Mensah; Bower, William A; Traxler, Rita; Blackburn, Jason K

2017-10-01

Anthrax is hyper-endemic in West Africa. Despite the effectiveness of livestock vaccines in controlling anthrax, underreporting, logistics, and limited resources makes implementing vaccination campaigns difficult. To better understand the geographic limits of anthrax, elucidate environmental factors related to its occurrence, and identify human and livestock populations at risk, we developed predictive models of the environmental suitability of anthrax in Ghana. We obtained data on the location and date of livestock anthrax from veterinary and outbreak response records in Ghana during 2005-2016, as well as livestock vaccination registers and population estimates of characteristically high-risk groups. To predict the environmental suitability of anthrax, we used an ensemble of random forest (RF) models built using a combination of climatic and environmental factors. From 2005 through the first six months of 2016, there were 67 anthrax outbreaks (851 cases) in livestock; outbreaks showed a seasonal peak during February through April and primarily involved cattle. There was a median of 19,709 vaccine doses [range: 0-175 thousand] administered annually. Results from the RF model suggest a marked ecological divide separating the broad areas of environmental suitability in northern Ghana from the southern part of the country. Increasing alkaline soil pH was associated with a higher probability of anthrax occurrence. We estimated 2.2 (95% CI: 2.0, 2.5) million livestock and 805 (95% CI: 519, 890) thousand low income rural livestock keepers were located in anthrax risk areas. Based on our estimates, the current anthrax vaccination efforts in Ghana cover a fraction of the livestock potentially at risk, thus control efforts should be focused on improving vaccine coverage among high risk groups.

Modeling the environmental suitability of anthrax in Ghana and estimating populations at risk: Implications for vaccination and control

PubMed Central

Allegye-Cudjoe, Emmanuel; Polkuu, Paul Nokuma; Frimpong, Joseph Asamoah; Nyarko, Kofi Mensah; Bower, William A.; Traxler, Rita

2017-01-01

Anthrax is hyper-endemic in West Africa. Despite the effectiveness of livestock vaccines in controlling anthrax, underreporting, logistics, and limited resources makes implementing vaccination campaigns difficult. To better understand the geographic limits of anthrax, elucidate environmental factors related to its occurrence, and identify human and livestock populations at risk, we developed predictive models of the environmental suitability of anthrax in Ghana. We obtained data on the location and date of livestock anthrax from veterinary and outbreak response records in Ghana during 2005–2016, as well as livestock vaccination registers and population estimates of characteristically high-risk groups. To predict the environmental suitability of anthrax, we used an ensemble of random forest (RF) models built using a combination of climatic and environmental factors. From 2005 through the first six months of 2016, there were 67 anthrax outbreaks (851 cases) in livestock; outbreaks showed a seasonal peak during February through April and primarily involved cattle. There was a median of 19,709 vaccine doses [range: 0–175 thousand] administered annually. Results from the RF model suggest a marked ecological divide separating the broad areas of environmental suitability in northern Ghana from the southern part of the country. Increasing alkaline soil pH was associated with a higher probability of anthrax occurrence. We estimated 2.2 (95% CI: 2.0, 2.5) million livestock and 805 (95% CI: 519, 890) thousand low income rural livestock keepers were located in anthrax risk areas. Based on our estimates, the current anthrax vaccination efforts in Ghana cover a fraction of the livestock potentially at risk, thus control efforts should be focused on improving vaccine coverage among high risk groups. PMID:29028799

An exploration of how perceptions of the risk of avian influenza in poultry relate to urbanization in Vietnam.

PubMed

Finucane, Melissa L; Nghiem, Tuyen; Saksena, Sumeet; Nguyen, Lam; Fox, Jefferson; Spencer, James H; Thau, Trinh Dinh

2014-01-01

This research examined how perceptions of outbreaks of highly pathogenic avian influenza (HPAI) subtype H5N1 in poultry are related to urbanization. Via in-depth interviews with village leaders, household farmers, and large farm operators in modern, transitional, and traditional communes in the north of Vietnam, we explored behaviors, attitudes, cultural values, and traditions that might amplify or attenuate HPAI outbreaks. We also explored conceptualizations of urbanization and its impacts on animal husbandry and disease outbreaks. Qualitative theme analyses identified the key impacts, factors related to HPAI outbreaks, and disease prevention and management strategies. The analyses also highlighted how urbanization improves some aspects of life (e.g., food security, family wealth and health, more employment opportunities, and improved infrastructure), but simultaneously poses significant challenges for poultry farming and disease management. Awareness of qualitative aspects of HPAI risk perceptions and behaviors and how they vary with urbanization processes may help to improve the prevention and management of emerging infectious diseases.

Quantifying Reporting Timeliness to Improve Outbreak Control

PubMed Central

Swaan, Corien; van Steenbergen, Jim; Kretzschmar, Mirjam

2015-01-01

The extent to which reporting delays should be reduced to gain substantial improvement in outbreak control is unclear. We developed a model to quantitatively assess reporting timeliness. Using reporting speed data for 6 infectious diseases in the notification system in the Netherlands, we calculated the proportion of infections produced by index and secondary cases until the index case is reported. We assumed interventions that immediately stop transmission. Reporting delays render useful only those interventions that stop transmission from index and secondary cases. We found that current reporting delays are adequate for hepatitis A and B control. However, reporting delays should be reduced by a few days to improve measles and mumps control, by at least 10 days to improve shigellosis control, and by at least 5 weeks to substantially improve pertussis control. Our method provides quantitative insight into the required reporting delay reductions needed to achieve outbreak control and other transmission prevention goals. PMID:25625374

Salmonella enterica Pulsed-Field Gel Electrophoresis Clusters, Minnesota, USA, 2001–2007

PubMed Central

Hedberg, Craig W.; Meyer, Stephanie; Boxrud, David J.; Smith, Kirk E.

2010-01-01

We determined characteristics of Salmonella enterica pulsed-field gel electrophoresis clusters that predict their being solved (i.e., that result in identification of a confirmed outbreak). Clusters were investigated by the Minnesota Department of Health by using a dynamic iterative model. During 2001–2007, a total of 43 (12.5%) of 344 clusters were solved. Clusters of >4 isolates were more likely to be solved than clusters of 2 isolates. Clusters in which the first 3 case isolates were received at the Minnesota Department of Health within 7 days were more likely to be solved than were clusters in which the first 3 case isolates were received over a period >14 days. If resources do not permit investigation of all S. enterica pulsed-field gel electrophoresis clusters, investigation of clusters of >4 cases and clusters in which the first 3 case isolates were received at a public health laboratory within 7 days may improve outbreak investigations. PMID:21029524

Results of routine restaurant inspections can predict outbreaks of foodborne illness: the Seattle-King County experience.

PubMed Central

Irwin, K; Ballard, J; Grendon, J; Kobayashi, J

1989-01-01

To analyze the association between the results of routine inspections and foodborne outbreaks in restaurants, we conducted a matched case-control study using available data from Seattle-King County, Washington. Case restaurants were facilities with a reported foodborne outbreak between January 1, 1986 and March 31, 1987 (N = 28). Two control restaurants with no reported outbreaks during this period were matched to each case restaurant on county health district and date of routine inspection (N = 56). Data from the routine inspection that preceded the outbreak (for case restaurants) or the date-matched routine inspection (for control restaurants) were abstracted from computerized inspection records. Case restaurants had a significantly lower mean inspection score (83.8 on a 0 to 100 point scale) than control restaurants (90.9). Restaurants with poor inspection scores and violations of proper temperature controls of potentially hazardous foods were, respectively, five and ten times more likely to have outbreaks than restaurants with better results. Although this study demonstrates that Seattle-King County's routine inspection form can successfully identify restaurants at increased risk of foodborne outbreaks, it also illustrates that more emphasis on regulation and education is needed to prevent outbreaks in restaurants with poor inspection results. PMID:2705592

Individualistic values are related to an increase in the outbreaks of infectious diseases and zoonotic diseases.

PubMed

Morand, Serge; Walther, Bruno A

2018-03-01

Collectivist versus individualistic values are important attributes of intercultural variation. Collectivist values favour in-group members over out-group members and may have evolved to protect in-group members against pathogen transmission. As predicted by the pathogen stress theory of cultural values, more collectivist countries are associated with a higher historical pathogen burden. However, if lifestyles of collectivist countries indeed function as a social defence which decreases pathogen transmission, then these countries should also have experienced fewer disease outbreaks in recent times. We tested this novel hypothesis by correlating the values of collectivism-individualism for 66 countries against their historical pathogen burden, recent number of infectious disease outbreaks and zoonotic disease outbreaks and emerging infectious disease events, and four potentially confounding variables. We confirmed the previously established negative relationship between individualism and historical pathogen burden with new data. While we did not find a correlation for emerging infectious disease events, we found significant positive correlations between individualism and the number of infectious disease outbreaks and zoonotic disease outbreaks. Therefore, one possible cost for individualistic cultures may be their higher susceptibility to disease outbreaks. We support further studies into the exact protective behaviours and mechanisms of collectivist societies which may inhibit disease outbreaks.

DoD-GEIS Rift Valley Fever Monitoring and Prediction System as a Tool for Defense and US Diplomacy

NASA Technical Reports Server (NTRS)

Anyamba, Assaf; Tucker, Compton J.; Linthicum, Kenneth J.; Witt, Clara J.; Gaydos, Joel C.; Russell, Kevin L.

2011-01-01

Over the last 10 years the Armed Forces Health Surveillance Center's Global Emerging Infections Surveillance and Response System (GEIS) partnering with NASA'S Goddard Space Flight Center and USDA's USDA-Center for Medical, Agricultural & Veterinary Entomology established and have operated the Rift Valley fever Monitoring and Prediction System to monitor, predict and assess the risk of Rift Valley fever outbreaks and other vector-borne diseases over Africa and the Middle East. This system is built on legacy DoD basic research conducted by Walter Reed Army Institute of Research overseas laboratory (US Army Medical Research Unit-Kenya) and the operational satellite environmental monitoring by NASA GSFC. Over the last 10 years of operation the system has predicted outbreaks of Rift Valley fever in the Horn of Africa, Sudan, South Africa and Mauritania. The ability to predict an outbreak several months before it occurs provides early warning to protect deployed forces, enhance public health in concerned countries and is a valuable tool use.d by the State Department in US Diplomacy. At the international level the system has been used by the Food and Agricultural Organization (FAD) and the World Health Organization (WHO) to support their monitoring, surveillance and response programs in the livestock sector and human health. This project is a successful testament of leveraging resources of different federal agencies to achieve objectives of force health protection, health and diplomacy.

The Use of Ambient Humidity Conditions to Improve Influenza Forecast

NASA Astrophysics Data System (ADS)

Shaman, J. L.; Kandula, S.; Yang, W.; Karspeck, A. R.

2017-12-01

Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing. These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast and provide further evidence that humidity modulates rates of influenza transmission.

Rapid response to Ebola outbreaks in remote areas - Liberia, July-November 2014.

PubMed

Kateh, Francis; Nagbe, Thomas; Kieta, Abraham; Barskey, Albert; Gasasira, Alex Ntale; Driscoll, Anne; Tucker, Anthony; Christie, Athalia; Karmo, Ben; Scott, Colleen; Bowah, Collin; Barradas, Danielle; Blackley, David; Dweh, Emmanuel; Warren, Felicia; Mahoney, Frank; Kassay, Gabriel; Calvert, Geoffrey M; Castro, Georgina; Logan, Gorbee; Appiah, Grace; Kirking, Hannah; Koon, Hawa; Papowitz, Heather; Walke, Henry; Cole, Isaac B; Montgomery, Joel; Neatherlin, John; Tappero, Jordan W; Hagan, Jose E; Forrester, Joseph; Woodring, Joseph; Mott, Joshua; Attfield, Kathleen; DeCock, Kevin; Lindblade, Kim A; Powell, Krista; Yeoman, Kristin; Adams, Laura; Broyles, Laura N; Slutsker, Laurence; Larway, Lawrence; Belcher, Lisa; Cooper, Lorraine; Santos, Marjorie; Westercamp, Matthew; Weinberg, Meghan Pearce; Massoudi, Mehran; Dea, Monica; Patel, Monita; Hennessey, Morgan; Fomba, Moses; Lubogo, Mutaawe; Maxwell, Nikki; Moonan, Patrick; Arzoaquoi, Sampson; Gee, Samuel; Zayzay, Samuel; Pillai, Satish; Williams, Seymour; Zarecki, Shauna Mettee; Yett, Sheldon; James, Stephen; Grube, Steven; Gupta, Sundeep; Nelson, Thelma; Malibiche, Theophil; Frank, Wilmont; Smith, Wilmot; Nyenswah, Tolbert

2015-02-27

West Africa is experiencing its first epidemic of Ebola virus disease (Ebola). As of February 9, Liberia has reported 8,864 Ebola cases, of which 3,147 were laboratory-confirmed. Beginning in August 2014, the Liberia Ministry of Health and Social Welfare (MOHSW), supported by CDC, the World Health Organization (WHO), and others, began systematically investigating and responding to Ebola outbreaks in remote areas. Because many of these areas lacked mobile telephone service, easy road access, and basic infrastructure, flexible and targeted interventions often were required. Development of a national strategy for the Rapid Isolation and Treatment of Ebola (RITE) began in early October. The strategy focuses on enhancing capacity of county health teams (CHT) to investigate outbreaks in remote areas and lead tailored responses through effective and efficient coordination of technical and operational assistance from the MOHSW central level and international partners. To measure improvements in response indicators and outcomes over time, data from investigations of 12 of 15 outbreaks in remote areas with illness onset dates of index cases during July 16-November 20, 2014, were analyzed. The times to initial outbreak alerts and durations of the outbreaks declined over that period while the proportions of patients who were isolated and treated increased. At the same time, the case-fatality rate in each outbreak declined. Implementation of strategies, such as RITE, to rapidly respond to rural outbreaks of Ebola through coordinated and tailored responses can successfully reduce transmission and improve outcomes.

Surveillance and Outbreak Response Management System (SORMAS) to support the control of the Ebola virus disease outbreak in West Africa.

PubMed

Fähnrich, C; Denecke, K; Adeoye, O O; Benzler, J; Claus, H; Kirchner, G; Mall, S; Richter, R; Schapranow, M P; Schwarz, N; Tom-Aba, D; Uflacker, M; Poggensee, G; Krause, G

2015-03-26

In the context of controlling the current outbreak of Ebola virus disease (EVD), the World Health Organization claimed that 'critical determinant of epidemic size appears to be the speed of implementation of rigorous control measures', i.e. immediate follow-up of contact persons during 21 days after exposure, isolation and treatment of cases, decontamination, and safe burials. We developed the Surveillance and Outbreak Response Management System (SORMAS) to improve efficiency and timeliness of these measures. We used the Design Thinking methodology to systematically analyse experiences from field workers and the Ebola Emergency Operations Centre (EOC) after successful control of the EVD outbreak in Nigeria. We developed a process model with seven personas representing the procedures of EVD outbreak control. The SORMAS system architecture combines latest In-Memory Database (IMDB) technology via SAP HANA (in-memory, relational database management system), enabling interactive data analyses, and established SAP cloud tools, such as SAP Afaria (a mobile device management software). The user interface consists of specific front-ends for smartphones and tablet devices, which are independent from physical configurations. SORMAS allows real-time, bidirectional information exchange between field workers and the EOC, ensures supervision of contact follow-up, automated status reports, and GPS tracking. SORMAS may become a platform for outbreak management and improved routine surveillance of any infectious disease. Furthermore, the SORMAS process model may serve as framework for EVD outbreak modeling.

Estimating the Burden of Disease Associated with Outbreaks Reported to the U.S. Waterborne Disease Outbreak Surveillance System: Identifying Limitations and Improvements (Final Report)

EPA Science Inventory

This report demonstrates how data from the Waterborne Disease Outbreak Surveillance System (WBDOSS) can be used to estimate disease burden and presents results using 30 years of data. This systematic analysis does not attempt to provide an estimate of the actual incidence and b...

Outbreak of Ciprofloxacin-Resistant Shigella sonnei Associated with Travel to Vietnam, Republic of Korea.

PubMed

Kim, Jin Seok; Kim, Jae Joon; Kim, Soo Jin; Jeon, Se-Eun; Seo, Ki Yeon; Choi, Jun-Kil; Kim, Nan-Ok; Hong, Sahyun; Chung, Gyung Tae; Yoo, Cheon-Kwon; Kim, Young-Taek; Cheun, Hyeng Il; Bae, Geun-Ryang; Yeo, Yeong-Hee; Ha, Gang-Ja; Choi, Mi-Suk; Kang, Shin-Jung; Kim, Junyoung

2015-07-01

We investigated an October 2014 outbreak of illness caused by Shigella sonnei in a daycare center in the Republic of Korea (South Korea). The outbreak strain was resistant to extended-spectrum cephalosporins and fluoroquinolones and was traced to a child who had traveled to Vietnam. Improved hygiene and infection control practices are needed for prevention of shigellosis.

Typhoid fever acquired in the United States, 1999–2010: epidemiology, microbiology, and use of a space–time scan statistic for outbreak detection

PubMed Central

IMANISHI, M.; NEWTON, A. E.; VIEIRA, A. R.; GONZALEZ-AVILES, G.; KENDALL SCOTT, M. E.; MANIKONDA, K.; MAXWELL, T. N.; HALPIN, J. L.; FREEMAN, M. M.; MEDALLA, F.; AYERS, T. L.; DERADO, G.; MAHON, B. E.; MINTZ, E. D.

2016-01-01

SUMMARY Although rare, typhoid fever cases acquired in the United States continue to be reported. Detection and investigation of outbreaks in these domestically acquired cases offer opportunities to identify chronic carriers. We searched surveillance and laboratory databases for domestically acquired typhoid fever cases, used a space–time scan statistic to identify clusters, and classified clusters as outbreaks or non-outbreaks. From 1999 to 2010, domestically acquired cases accounted for 18% of 3373 reported typhoid fever cases; their isolates were less often multidrug-resistant (2% vs. 15%) compared to isolates from travel-associated cases. We identified 28 outbreaks and two possible outbreaks within 45 space–time clusters of ⩾2 domestically acquired cases, including three outbreaks involving ⩾2 molecular subtypes. The approach detected seven of the ten outbreaks published in the literature or reported to CDC. Although this approach did not definitively identify any previously unrecognized outbreaks, it showed the potential to detect outbreaks of typhoid fever that may escape detection by routine analysis of surveillance data. Sixteen outbreaks had been linked to a carrier. Every case of typhoid fever acquired in a non-endemic country warrants thorough investigation. Space–time scan statistics, together with shoe-leather epidemiology and molecular subtyping, may improve outbreak detection. PMID:25427666

Typhoid fever acquired in the United States, 1999-2010: epidemiology, microbiology, and use of a space-time scan statistic for outbreak detection.

PubMed

Imanishi, M; Newton, A E; Vieira, A R; Gonzalez-Aviles, G; Kendall Scott, M E; Manikonda, K; Maxwell, T N; Halpin, J L; Freeman, M M; Medalla, F; Ayers, T L; Derado, G; Mahon, B E; Mintz, E D

2015-08-01

Although rare, typhoid fever cases acquired in the United States continue to be reported. Detection and investigation of outbreaks in these domestically acquired cases offer opportunities to identify chronic carriers. We searched surveillance and laboratory databases for domestically acquired typhoid fever cases, used a space-time scan statistic to identify clusters, and classified clusters as outbreaks or non-outbreaks. From 1999 to 2010, domestically acquired cases accounted for 18% of 3373 reported typhoid fever cases; their isolates were less often multidrug-resistant (2% vs. 15%) compared to isolates from travel-associated cases. We identified 28 outbreaks and two possible outbreaks within 45 space-time clusters of ⩾2 domestically acquired cases, including three outbreaks involving ⩾2 molecular subtypes. The approach detected seven of the ten outbreaks published in the literature or reported to CDC. Although this approach did not definitively identify any previously unrecognized outbreaks, it showed the potential to detect outbreaks of typhoid fever that may escape detection by routine analysis of surveillance data. Sixteen outbreaks had been linked to a carrier. Every case of typhoid fever acquired in a non-endemic country warrants thorough investigation. Space-time scan statistics, together with shoe-leather epidemiology and molecular subtyping, may improve outbreak detection.

Geographic variation in forest composition and precipitation predict the synchrony of forest insect outbreaks

Treesearch

Kyle J. Haynes; Andrew M. Liebhold; Ottar N. Bjørnstad; Andrew J. Allstadt; Randall S. Morin

2018-01-01

Evaluating the causes of spatial synchrony in population dynamics in nature is notoriously difficult due to a lack of data and appropriate statistical methods. Here, we use a recently developed method, a multivariate extension of the local indicators of spatial autocorrelation statistic, to map geographic variation in the synchrony of gypsy moth outbreaks. Regression...

Remote Sensing Contributions to Prediction and Risk Assessment of Natural Diasters Caused by Large Scale Rift Valley fever Outbreaks

USDA-ARS?s Scientific Manuscript database

Remotely sensed vegetation measurements for the last 30 years combined with other climate data sets such as rainfall and sea surface temperatures have come to play an important role in the study of the ecology of vector-borne diseases. We show that episodic outbreaks of Rift Valley fever are influen...

Algorithms for detecting and predicting influenza outbreaks: metanarrative review of prospective evaluations

PubMed Central

Spreco, A; Timpka, T

2016-01-01

Objectives Reliable monitoring of influenza seasons and pandemic outbreaks is essential for response planning, but compilations of reports on detection and prediction algorithm performance in influenza control practice are largely missing. The aim of this study is to perform a metanarrative review of prospective evaluations of influenza outbreak detection and prediction algorithms restricted settings where authentic surveillance data have been used. Design The study was performed as a metanarrative review. An electronic literature search was performed, papers selected and qualitative and semiquantitative content analyses were conducted. For data extraction and interpretations, researcher triangulation was used for quality assurance. Results Eight prospective evaluations were found that used authentic surveillance data: three studies evaluating detection and five studies evaluating prediction. The methodological perspectives and experiences from the evaluations were found to have been reported in narrative formats representing biodefence informatics and health policy research, respectively. The biodefence informatics narrative having an emphasis on verification of technically and mathematically sound algorithms constituted a large part of the reporting. Four evaluations were reported as health policy research narratives, thus formulated in a manner that allows the results to qualify as policy evidence. Conclusions Awareness of the narrative format in which results are reported is essential when interpreting algorithm evaluations from an infectious disease control practice perspective. PMID:27154479

The importance of waterborne disease outbreak surveillance in the United States.

PubMed

Craun, Gunther Franz

2012-01-01

Analyses of the causes of disease outbreaks associated with contaminated drinking water in the United States have helped inform prevention efforts at the national, state, and local levels. This article describes the changing nature of disease outbreaks in public water systems during 1971-2008 and discusses the importance of a collaborative waterborne outbreak surveillance system established in 1971. Increasing reports of outbreaks throughout the early 1980s emphasized that microbial contaminants remained a health-risk challenge for suppliers of drinking water. Outbreak investigations identified the responsible etiologic agents and deficiencies in the treatment and distribution of drinking water, especially the high risk associated with unfiltered surface water systems. Surveillance information was important in establishing an effective research program that guided government regulations and industry actions to improve drinking water quality. Recent surveillance statistics suggest that prevention efforts based on these research findings have been effective in reducing outbreak risks especially for surface water systems.

Molecular epidemiology of "Norwalk-like viruses" in outbreaks of gastroenteritis in the United States.

PubMed

Fankhauser, R L; Noel, J S; Monroe, S S; Ando, T; Glass, R I

1998-12-01

Fecal specimens from 90 outbreaks of nonbacterial gastroenteritis reported to 33 state health departments from January 1996 to June 1997 were examined to determine the importance of and to characterize "Norwalk-like viruses" (NLVs) in these outbreaks. NLVs were detected by reverse transcription-polymerase chain reaction in specimens from 86 (96%) of 90 outbreaks. Outbreaks were most frequent in nursing homes and hospitals (43%), followed by restaurants or events with catered meals (26%); consumption of contaminated food was the most commonly identified mode of transmission (37%). Nucleotide sequence analysis showed great diversity between strains but also provided evidence indicating the emergence of a common, predominant strain. The application of improved molecular techniques to detect NLVs demonstrates that most outbreaks of nonbacterial gastroenteritis in the United States appear to be associated with these viruses and that sequence analysis is a robust tool to help link or differentiate these outbreaks.

Predicting factors for malaria re-introduction: an applied model in an elimination setting to prevent malaria outbreaks.

PubMed

Ranjbar, Mansour; Shoghli, Alireza; Kolifarhood, Goodarz; Tabatabaei, Seyed Mehdi; Amlashi, Morteza; Mohammadi, Mahdi

2016-03-02

Malaria re-introduction is a challenge in elimination settings. To prevent re-introduction, receptivity, vulnerability, and health system capacity of foci should be monitored using appropriate tools. This study aimed to design an applicable model to monitor predicting factors of re-introduction of malaria in highly prone areas. This exploratory, descriptive study was conducted in a pre-elimination setting with a high-risk of malaria transmission re-introduction. By using nominal group technique and literature review, a list of predicting indicators for malaria re-introduction and outbreak was defined. Accordingly, a checklist was developed and completed in the field for foci affected by re-introduction and for cleared-up foci as a control group, for a period of 12 weeks before re-introduction and for the same period in the previous year. Using field data and analytic hierarchical process (AHP), each variable and its sub-categories were weighted, and by calculating geometric means for each sub-category, score of corresponding cells of interaction matrices, lower and upper threshold of different risks strata, including low and mild risk of re-introduction and moderate and high risk of malaria outbreaks, were determined. The developed predictive model was calibrated through resampling with different sets of explanatory variables using R software. Sensitivity and specificity of the model were calculated based on new samples. Twenty explanatory predictive variables of malaria re-introduction were identified and a predictive model was developed. Unpermitted immigrants from endemic neighbouring countries were determined as a pivotal factor (AHP score: 0.181). Moreover, quality of population movement (0.114), following malaria transmission season (0.088), average daily minimum temperature in the previous 8 weeks (0.062), an outdoor resting shelter for vectors (0.045), and rainfall (0.042) were determined. Positive and negative predictive values of the model were 81.8 and 100 %, respectively. This study introduced a new, simple, yet reliable model to forecast malaria re-introduction and outbreaks eight weeks in advance in pre-elimination and elimination settings. The model incorporates comprehensive deterministic factors that can easily be measured in the field, thereby facilitating preventive measures.

Crisis prevention and management during SARS outbreak, Singapore.

PubMed

Quah, Stella R; Hin-Peng, Lee

2004-02-01

We discuss crisis prevention and management during the first 3 months of the severe acute respiratory syndrome (SARS) outbreak in Singapore. Four public health issues were considered: prevention measures, self-health evaluation, SARS knowledge, and appraisal of crisis management. We conducted telephone interviews with a representative sample of 1,201 adults, > or = 21 years of age. We found that sex, age, and attitude (anxiety and perception of open communication with authorities) were associated with practicing preventive measures. Analysis of Singapore's outbreak improves our understanding of the social dimensions of infectious disease outbreaks.

Generalized reproduction numbers and the prediction of patterns in waterborne disease

PubMed Central

Gatto, Marino; Mari, Lorenzo; Bertuzzo, Enrico; Casagrandi, Renato; Righetto, Lorenzo; Rodriguez-Iturbe, Ignacio; Rinaldo, Andrea

2012-01-01

Understanding, predicting, and controlling outbreaks of waterborne diseases are crucial goals of public health policies, but pose challenging problems because infection patterns are influenced by spatial structure and temporal asynchrony. Although explicit spatial modeling is made possible by widespread data mapping of hydrology, transportation infrastructure, population distribution, and sanitation, the precise condition under which a waterborne disease epidemic can start in a spatially explicit setting is still lacking. Here we show that the requirement that all the local reproduction numbers be larger than unity is neither necessary nor sufficient for outbreaks to occur when local settlements are connected by networks of primary and secondary infection mechanisms. To determine onset conditions, we derive general analytical expressions for a reproduction matrix , explicitly accounting for spatial distributions of human settlements and pathogen transmission via hydrological and human mobility networks. At disease onset, a generalized reproduction number (the dominant eigenvalue of ) must be larger than unity. We also show that geographical outbreak patterns in complex environments are linked to the dominant eigenvector and to spectral properties of . Tests against data and computations for the 2010 Haiti and 2000 KwaZulu-Natal cholera outbreaks, as well as against computations for metapopulation networks, demonstrate that eigenvectors of provide a synthetic and effective tool for predicting the disease course in space and time. Networked connectivity models, describing the interplay between hydrology, epidemiology, and social behavior sustaining human mobility, thus prove to be key tools for emergency management of waterborne infections. PMID:23150538

Building-level analyses to prospectively detect influenza outbreaks in long-term care facilities: New York City, 2013-2014.

PubMed

Levin-Rector, Alison; Nivin, Beth; Yeung, Alice; Fine, Annie D; Greene, Sharon K

2015-08-01

Timely outbreak detection is necessary to successfully control influenza in long-term care facilities (LTCFs) and other institutions. To supplement nosocomial outbreak reports, calls from infection control staff, and active laboratory surveillance, the New York City (NYC) Department of Health and Mental Hygiene implemented an automated building-level analysis to proactively identify LTCFs with laboratory-confirmed influenza activity. Geocoded addresses of LTCFs in NYC were compared with geocoded residential addresses for all case-patients with laboratory-confirmed influenza reported through passive surveillance. An automated daily analysis used the geocoded building identification number, approximate text matching, and key-word searches to identify influenza in residents of LTCFs for review and follow-up by surveillance coordinators. Our aim was to determine whether the building analysis improved prospective outbreak detection during the 2013-2014 influenza season. Of 119 outbreaks identified in LTCFs, 109 (92%) were ever detected by the building analysis, and 55 (46%) were first detected by the building analysis. Of the 5,953 LTCF staff and residents who received antiviral prophylaxis during the 2013-2014 season, 929 (16%) were at LTCFs where outbreaks were initially detected by the building analysis. A novel building-level analysis improved influenza outbreak identification in LTCFs in NYC, prompting timely infection control measures. Copyright © 2015 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Roosting behaviour and habitat selection of Pteropus giganteus reveals potential links to Nipah virus epidemiology

PubMed Central

Hahn, Micah B.; Epstein, Jonathan H.; Gurley, Emily S.; Islam, Mohammad S.; Luby, Stephen P.; Daszak, Peter; Patz, Jonathan A.

2014-01-01

Summary 1. Flying foxes Pteropus spp. play a key role in forest regeneration as seed dispersers and are also the reservoir of many viruses, including Nipah virus in Bangladesh. Little is known about their habitat requirements, particularly in South Asia. Identifying Pteropus habitat preferences could assist in understanding the risk of zoonotic disease transmission broadly, and in Bangladesh, could help explain the spatial distribution of human Nipah virus cases. 2. We analysed characteristics of Pteropus giganteus roosts and constructed an ecological niche model to identify suitable habitat in Bangladesh. We also assessed the distribution of suitable habitat in relation to the location of human Nipah virus cases. 3. Compared to non-roost trees, P. giganteus roost trees are taller with larger diameters, and are more frequently canopy trees. Colony size was larger in densely forested regions and smaller in flood-affected areas. Roosts were located in areas with lower annual precipitation and higher human population density than non-roost sites. 4. We predicted that 2–17% of Bangladesh's land area is suitable roosting habitat. Nipah virus outbreak villages were 2.6 times more likely to be located in areas predicted as highly suitable habitat for P. giganteus compared to non-outbreak villages. 5. Synthesis and applications. Habitat suitability modelling may help identify previously undocumented Nipah outbreak locations and improve our understanding of Nipah virus ecology by highlighting regions where there is suitable bat habitat but no reported human Nipah virus. Conservation and public health education is a key component of P. giganteus management in Bangladesh due to the general misunderstanding and fear of bats that are a reservoir of Nipah virus. Affiliation between Old World fruit bats (Pteropodidae) and people is common throughout their range, and in order to conserve these keystone bat species and prevent emergence of zoonotic viruses, it is imperative that we continue to improve our understanding of Pteropus resource requirements and routes of virus transmission from bats to people. Results presented here can be utilized to develop land management strategies and conservation policies that simultaneously protect fruit bats and public health. PMID:24778457

Roosting behaviour and habitat selection of Pteropus giganteus reveals potential links to Nipah virus epidemiology.

PubMed

Hahn, Micah B; Epstein, Jonathan H; Gurley, Emily S; Islam, Mohammad S; Luby, Stephen P; Daszak, Peter; Patz, Jonathan A

2014-04-01

1. Flying foxes Pteropus spp. play a key role in forest regeneration as seed dispersers and are also the reservoir of many viruses, including Nipah virus in Bangladesh. Little is known about their habitat requirements, particularly in South Asia. Identifying Pteropus habitat preferences could assist in understanding the risk of zoonotic disease transmission broadly, and in Bangladesh, could help explain the spatial distribution of human Nipah virus cases. 2. We analysed characteristics of Pteropus giganteus roosts and constructed an ecological niche model to identify suitable habitat in Bangladesh. We also assessed the distribution of suitable habitat in relation to the location of human Nipah virus cases. 3. Compared to non-roost trees, P. giganteus roost trees are taller with larger diameters, and are more frequently canopy trees. Colony size was larger in densely forested regions and smaller in flood-affected areas. Roosts were located in areas with lower annual precipitation and higher human population density than non-roost sites. 4. We predicted that 2-17% of Bangladesh's land area is suitable roosting habitat. Nipah virus outbreak villages were 2.6 times more likely to be located in areas predicted as highly suitable habitat for P. giganteus compared to non-outbreak villages. 5. Synthesis and applications . Habitat suitability modelling may help identify previously undocumented Nipah outbreak locations and improve our understanding of Nipah virus ecology by highlighting regions where there is suitable bat habitat but no reported human Nipah virus. Conservation and public health education is a key component of P. giganteus management in Bangladesh due to the general misunderstanding and fear of bats that are a reservoir of Nipah virus. Affiliation between Old World fruit bats ( Pteropodidae ) and people is common throughout their range, and in order to conserve these keystone bat species and prevent emergence of zoonotic viruses, it is imperative that we continue to improve our understanding of Pteropus resource requirements and routes of virus transmission from bats to people. Results presented here can be utilized to develop land management strategies and conservation policies that simultaneously protect fruit bats and public health.

Outbreaks of influenza-like illness in long-term care facilities in Winnipeg, Canada.

PubMed

Mahmud, Salaheddin M; Thompson, Laura H; Nowicki, Deborah L; Plourde, Pierre J

2013-11-01

Outbreaks of influenza-like illness (ILI) are common in long-term care facilities (LTCFs) and result in significant morbidity and mortality among residents. We describe patterns of reported ILI outbreaks in LTCFs in Winnipeg, Canada, and examine LTCF and outbreak characteristics that influence the clinical outcomes of these outbreaks. We analyzed the electronic records of all ILI outbreaks reported by LTCFs in Winnipeg from 2003 to 2011. Outbreak duration, ILI attack rates among staff and residents, and residents' death rates were calculated by presumed viral etiology, staff vaccination rates, type of influenza chemoprophylaxis used, and time to notification to public health. Of a total of 154 reported outbreaks, most (N=80) were attributed to influenza, and these outbreaks tended to have higher attack and death rates among LTCF residents compared with outbreaks caused by other respiratory viruses (12) or those of unknown etiology (62). About 92% of residents and 38% of staff of the average LTCFs were vaccinated. Chemoprophylaxis was used in 57·5% of influenza outbreaks. Regardless of presumed viral etiology, outbreaks reported within 3 days of onset ended sooner and had lower attack and mortality rates among residents. Influenza-like illness outbreaks still occur among highly immunized LTCF residents, so in addition to vaccination of staff and residents, it is important to maintain competent infection control practices. Early identification and notification to public health authorities and possibly early initiation of control measures could improve clinical outcomes of ILI outbreaks. © 2012 John Wiley & Sons Ltd.

Assessment of the response to cholera outbreaks in two districts in Ghana.

PubMed

Ohene, Sally-Ann; Klenyuie, Wisdom; Sarpeh, Mark

2016-11-02

Despite recurring outbreaks of cholera in Ghana, very little has been reported on assessments of outbreak response activities undertaken in affected areas. This study assessed the response activities undertaken in two districts, Akatsi District in Volta Region and Komenda-Edina-Eguafo-Abirem (KEEA) Municipal in Central Region during the 2012 cholera epidemic in Ghana. We conducted a retrospective assessment of the events, strengths and weaknesses of the cholera outbreak response activities in the two districts making use of the WHO cholera evaluation tool. Information sources included surveillance and facility records, reports and interviews with relevant health personnel involved in the outbreak response from both district health directorates and health facilities. We collected data on age, sex, area of residence, date of reporting to health facility of cholera cases, district population data and information on the outbreak response activities and performed descriptive analyses of the outbreak data by person, time and place. The cholera outbreak in Akatsi was explosive with a high attack rate (AR) of 374/100,000 and case fatality rate (CFR) of 1.2 % while that in KEEA was on a relatively smaller scale AR of 23/100,000 but with a high case fatality rate of 18.8 %. For both districts, we identified multiple strengths in the response to the outbreak including timely notification of the district health officials which triggered prompt investigation of the suspected outbreak facilitating confirmation of cholera and initiation of public health response activities. Others were coordination of the activities by multi-sectoral committees, instituting water, sanitation and hygiene measures and appropriate case management at health facilities. We also found areas that needed improvement in both districts including incomplete surveillance data, sub-optimal community based surveillance considering the late reporting and the deaths in the community and the inadequate community knowledge about cholera preventive measures. The assessment of the cholera outbreak response in the two districts highlighted strengths in the epidemic control activities. There was however need to strengthen preparedness especially in the area of improving community surveillance and awareness about cholera prevention and the importance of seeking prompt treatment in health facilities in the event of an outbreak.

Retrospective analysis of institutional scabies outbreaks from 1984 to 2013: lessons learned and moving forward.

PubMed

Mounsey, K E; Murray, H C; King, M; Oprescu, F

2016-08-01

Scabies outbreaks can be disruptive in institutional settings, and are associated with considerable but under-researched morbidity, especially in vulnerable populations. In this paper, we describe key findings from a retrospective review of scabies outbreaks reported in the literature over the past 30 years. We undertook this review to gain insights into the impact of institutional outbreaks, the burden in terms of attack rates, economic costs, treatment trends, the types of index cases and outbreak progression. We found 84 reports over 30 years, with outbreaks most frequently reported in aged care facilities (n = 40) and hospitals (n = 33). On average, scabies outbreaks persisted for 3 months, and the median attack rate was 38%. While 1% lindane was once the most commonly employed acaricide, 5% permethrin and oral ivermectin are increasingly used. Crusted scabies represented the index case for 83% of outbreaks, and scabies was misdiagnosed in 43% outbreaks. The frequency of reported scabies outbreaks has not declined consistently over time suggesting the disease is still highly problematic. We contend that more research and practice emphasis must be paid to improve diagnostic methods, surveillance and control, health staff education and management of crusted scabies to prevent the development of scabies outbreaks in institutional settings.

Model-based prediction of nephropathia epidemica outbreaks based on climatological and vegetation data and bank vole population dynamics.

PubMed

Haredasht, S Amirpour; Taylor, C J; Maes, P; Verstraeten, W W; Clement, J; Barrios, M; Lagrou, K; Van Ranst, M; Coppin, P; Berckmans, D; Aerts, J-M

2013-11-01

Wildlife-originated zoonotic diseases in general are a major contributor to emerging infectious diseases. Hantaviruses more specifically cause thousands of human disease cases annually worldwide, while understanding and predicting human hantavirus epidemics pose numerous unsolved challenges. Nephropathia epidemica (NE) is a human infection caused by Puumala virus, which is naturally carried and shed by bank voles (Myodes glareolus). The objective of this study was to develop a method that allows model-based predicting 3 months ahead of the occurrence of NE epidemics. Two data sets were utilized to develop and test the models. These data sets were concerned with NE cases in Finland and Belgium. In this study, we selected the most relevant inputs from all the available data for use in a dynamic linear regression (DLR) model. The number of NE cases in Finland were modelled using data from 1996 to 2008. The NE cases were predicted based on the time series data of average monthly air temperature (°C) and bank voles' trapping index using a DLR model. The bank voles' trapping index data were interpolated using a related dynamic harmonic regression model (DHR). Here, the DLR and DHR models used time-varying parameters. Both the DHR and DLR models were based on a unified state-space estimation framework. For the Belgium case, no time series of the bank voles' population dynamics were available. Several studies, however, have suggested that the population of bank voles is related to the variation in seed production of beech and oak trees in Northern Europe. Therefore, the NE occurrence pattern in Belgium was predicted based on a DLR model by using remotely sensed phenology parameters of broad-leaved forests, together with the oak and beech seed categories and average monthly air temperature (°C) using data from 2001 to 2009. Our results suggest that even without any knowledge about hantavirus dynamics in the host population, the time variation in NE outbreaks in Finland could be predicted 3 months ahead with a 34% mean relative prediction error (MRPE). This took into account solely the population dynamics of the carrier species (bank voles). The time series analysis also revealed that climate change, as represented by the vegetation index, changes in forest phenology derived from satellite images and directly measured air temperature, may affect the mechanics of NE transmission. NE outbreaks in Belgium were predicted 3 months ahead with a 40% MRPE, based only on the climatological and vegetation data, in this case, without any knowledge of the bank vole's population dynamics. In this research, we demonstrated that NE outbreaks can be predicted using climate and vegetation data or the bank vole's population dynamics, by using dynamic data-based models with time-varying parameters. Such a predictive modelling approach might be used as a step towards the development of new tools for the prevention of future NE outbreaks. © 2012 Blackwell Verlag GmbH.

Characteristics of Clusters of Salmonella and Escherichia coli O157 Detected by Pulsed-Field Gel Electrophoresis that Predict Identification of Outbreaks.

PubMed

Jones, Timothy F; Sashti, Nupur; Ingram, Amanda; Phan, Quyen; Booth, Hillary; Rounds, Joshua; Nicholson, Cyndy S; Cosgrove, Shaun; Crocker, Kia; Gould, L Hannah

2016-12-01

Molecular subtyping of pathogens is critical for foodborne disease outbreak detection and investigation. Many clusters initially identified by pulsed-field gel electrophoresis (PFGE) are not confirmed as point-source outbreaks. We evaluated characteristics of clusters that can help prioritize investigations to maximize effective use of limited resources. A multiagency collaboration (FoodNet) collected data on Salmonella and Escherichia coli O157 clusters for 3 years. Cluster size, timing, extent, and nature of epidemiologic investigations were analyzed to determine associations with whether the cluster was identified as a confirmed outbreak. During the 3-year study period, 948 PFGE clusters were identified; 849 (90%) were Salmonella and 99 (10%) were E. coli O157. Of those, 192 (20%) were ultimately identified as outbreaks (154 [18%] of Salmonella and 38 [38%] of E. coli O157 clusters). Successful investigation was significantly associated with larger cluster size, more rapid submission of isolates (e.g., for Salmonella, 6 days for outbreaks vs. 8 days for nonoutbreaks) and PFGE result reporting to investigators (16 days vs. 29 days, respectively), and performance of analytic studies (completed in 33% of Salmonella outbreaks vs. 1% of nonoutbreaks) and environmental investigations (40% and 1%, respectively). Intervals between first and second cases in a cluster did not differ significantly between outbreaks and nonoutbreaks. Molecular subtyping of pathogens is a rapidly advancing technology, and successfully identifying outbreaks will vary by pathogen and methods used. Understanding criteria for successfully investigating outbreaks is critical for efficiently using limited resources.

Devising a method towards development of early warning tool for detection of malaria outbreak.

PubMed

Verma, Preeti; Sarkar, Soma; Singh, Poonam; Dhiman, Ramesh C

2017-11-01

Uncertainty often arises in differentiating seasonal variation from outbreaks of malaria. The present study was aimed to generalize the theoretical structure of sine curve for detecting an outbreak so that a tool for early warning of malaria may be developed. A 'case/mean-ratio scale' system was devised for labelling the outbreak in respect of two diverse districts of Assam and Rajasthan. A curve-based method of analysis was developed for determining outbreak and using the properties of sine curve. It could be used as an early warning tool for Plasmodium falciparum malaria outbreaks. In the present method of analysis, the critical C max (peak value of sine curve) value of seasonally adjusted curve for P. falciparum malaria outbreak was 2.3 for Karbi Anglong and 2.2 for Jaisalmer districts. On case/mean-ratio scale, the C max value of malaria curve between C max and 3.5, the outbreak could be labelled as minor while >3.5 may be labelled as major. In epidemic years, with mean of case/mean ratio of ≥1.00 and root mean square (RMS) ≥1.504 of case/mean ratio, outbreaks can be predicted 1-2 months in advance. The present study showed that in P. falciparum cases in Karbi Anglong (Assam) and Jaisalmer (Rajasthan) districts, the rise in C max value of curve was always followed by rise in average/RMS or both and hence could be used as an early warning tool. The present method provides better detection of outbreaks than the conventional method of mean plus two standard deviation (mean+2 SD). The identified tools are simple and may be adopted for preparedness of malaria outbreaks.

Mortality of spruce and fir in Maine in 1976-78 due to the spruce budworm outbreak

Treesearch

Donald W. Seegrist; Stanford L. Arner

1982-01-01

The spruce budworm population in Maine's spruce-fir forests has been at epidemic levels since the early 1970's. Spruce-fir mortality in 1976-78 is compared with predictions of what mortality would have been had the natural mortality rates remained at the levels experienced before the budworm outbreak. It appears that mortality of spruce and fir has increased...

Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore.

PubMed

Earnest, Arul; Chen, Mark I; Ng, Donald; Sin, Leo Yee

2005-05-11

The main objective of this study is to apply autoregressive integrated moving average (ARIMA) models to make real-time predictions on the number of beds occupied in Tan Tock Seng Hospital, during the recent SARS outbreak. This is a retrospective study design. Hospital admission and occupancy data for isolation beds was collected from Tan Tock Seng hospital for the period 14th March 2003 to 31st May 2003. The main outcome measure was daily number of isolation beds occupied by SARS patients. Among the covariates considered were daily number of people screened, daily number of people admitted (including observation, suspect and probable cases) and days from the most recent significant event discovery. We utilized the following strategy for the analysis. Firstly, we split the outbreak data into two. Data from 14th March to 21st April 2003 was used for model development. We used structural ARIMA models in an attempt to model the number of beds occupied. Estimation is via the maximum likelihood method using the Kalman filter. For the ARIMA model parameters, we considered the simplest parsimonious lowest order model. We found that the ARIMA (1,0,3) model was able to describe and predict the number of beds occupied during the SARS outbreak well. The mean absolute percentage error (MAPE) for the training set and validation set were 5.7% and 8.6% respectively, which we found was reasonable for use in the hospital setting. Furthermore, the model also provided three-day forecasts of the number of beds required. Total number of admissions and probable cases admitted on the previous day were also found to be independent prognostic factors of bed occupancy. ARIMA models provide useful tools for administrators and clinicians in planning for real-time bed capacity during an outbreak of an infectious disease such as SARS. The model could well be used in planning for bed-capacity during outbreaks of other infectious diseases as well.

Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model

NASA Astrophysics Data System (ADS)

Neufeld, K. N.; Keinath, A. P.; Gugino, B. K.; McGrath, M. T.; Sikora, E. J.; Miller, S. A.; Ivey, M. L.; Langston, D. B.; Dutta, B.; Keever, T.; Sims, A.; Ojiambo, P. S.

2017-11-01

Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good performance in predicting the risk of cucurbit downy mildew outbreak in the eastern United States.

Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model.

PubMed

Neufeld, K N; Keinath, A P; Gugino, B K; McGrath, M T; Sikora, E J; Miller, S A; Ivey, M L; Langston, D B; Dutta, B; Keever, T; Sims, A; Ojiambo, P S

2018-04-01

Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good performance in predicting the risk of cucurbit downy mildew outbreak in the eastern United States.

Increased efficiency in the second-hand tire trade provides opportunity for dengue control.

PubMed

Pliego Pliego, Emilene; Velázquez-Castro, Jorge; Eichhorn, Markus P; Fraguela Collar, Andrés

2018-01-21

Dengue fever is increasing in geographical range, spread by invasion of its vector mosquitoes. The trade in second-hand tires has been implicated as a factor in this process because they act as mobile reservoirs of mosquito eggs and larvae. Regional transportation of tires can create linkages between rural areas with dengue and disease-free urban areas, potentially giving rise to outbreaks even in areas with strong local control measures. In this work we sought to model the dynamics of mosquito transportation via the tire trade, in particular to predict its role in causing unexpected dengue outbreaks through vertical transmission of the virus across generations of mosquitoes. We also aimed to identify strategies for regulating the trade in second-hand tires, improving disease control. We created a mathematical model which captures the dynamics of dengue between rural and urban areas, taking into account the movement and storage time of tires, and mosquito diapause. We simulate a series of scenarios in which a mosquito population is introduced to a dengue-free area via movement of tires, either as single or multiple events, increasing the likelihood of a dengue outbreak. A persistent disease state can be induced regardless of whether urban conditions for an outbreak are met, and an existing endemic state can be enhanced by vector input. Finally we assess the potential for regulation of tire processing as a means of reducing the transmission of dengue fever using a specific case study from Puerto Rico. Our work demonstrates the importance of the second-hand tire trade in modulating the spread of dengue fever across regions, in particular its role in introducing dengue to disease-free areas. We propose that reduction of tire storage time and control of their movement can play a crucial role in containing dengue outbreaks. Copyright © 2017 Elsevier Ltd. All rights reserved.

Acceptance of Vaccinations in Pandemic Outbreaks: A Discrete Choice Experiment

PubMed Central

Determann, Domino; Korfage, Ida J.; Lambooij, Mattijs S.; Bliemer, Michiel; Richardus, Jan Hendrik; Steyerberg, Ewout W.; de Bekker-Grob, Esther W.

2014-01-01

Background Preventive measures are essential to limit the spread of new viruses; their uptake is key to their success. However, the vaccination uptake in pandemic outbreaks is often low. We aim to elicit how disease and vaccination characteristics determine preferences of the general public for new pandemic vaccinations. Methods In an internet-based discrete choice experiment (DCE) a representative sample of 536 participants (49% participation rate) from the Dutch population was asked for their preference for vaccination programs in hypothetical communicable disease outbreaks. We used scenarios based on two disease characteristics (susceptibility to and severity of the disease) and five vaccination program characteristics (effectiveness, safety, advice regarding vaccination, media attention, and out-of-pocket costs). The DCE design was based on a literature review, expert interviews and focus group discussions. A panel latent class logit model was used to estimate which trade-offs individuals were willing to make. Results All above mentioned characteristics proved to influence respondents’ preferences for vaccination. Preference heterogeneity was substantial. Females who stated that they were never in favor of vaccination made different trade-offs than males who stated that they were (possibly) willing to get vaccinated. As expected, respondents preferred and were willing to pay more for more effective vaccines, especially if the outbreak was more serious (€6–€39 for a 10% more effective vaccine). Changes in effectiveness, out-of-pocket costs and in the body that advises the vaccine all substantially influenced the predicted uptake. Conclusions We conclude that various disease and vaccination program characteristics influence respondents’ preferences for pandemic vaccination programs. Agencies responsible for preventive measures during pandemics can use the knowledge that out-of-pocket costs and the way advice is given affect vaccination uptake to improve their plans for future pandemic outbreaks. The preference heterogeneity shows that information regarding vaccination needs to be targeted differently depending on gender and willingness to get vaccinated. PMID:25057914

Acceptance of vaccinations in pandemic outbreaks: a discrete choice experiment.

PubMed

Determann, Domino; Korfage, Ida J; Lambooij, Mattijs S; Bliemer, Michiel; Richardus, Jan Hendrik; Steyerberg, Ewout W; de Bekker-Grob, Esther W

2014-01-01

Preventive measures are essential to limit the spread of new viruses; their uptake is key to their success. However, the vaccination uptake in pandemic outbreaks is often low. We aim to elicit how disease and vaccination characteristics determine preferences of the general public for new pandemic vaccinations. In an internet-based discrete choice experiment (DCE) a representative sample of 536 participants (49% participation rate) from the Dutch population was asked for their preference for vaccination programs in hypothetical communicable disease outbreaks. We used scenarios based on two disease characteristics (susceptibility to and severity of the disease) and five vaccination program characteristics (effectiveness, safety, advice regarding vaccination, media attention, and out-of-pocket costs). The DCE design was based on a literature review, expert interviews and focus group discussions. A panel latent class logit model was used to estimate which trade-offs individuals were willing to make. All above mentioned characteristics proved to influence respondents' preferences for vaccination. Preference heterogeneity was substantial. Females who stated that they were never in favor of vaccination made different trade-offs than males who stated that they were (possibly) willing to get vaccinated. As expected, respondents preferred and were willing to pay more for more effective vaccines, especially if the outbreak was more serious (€6-€39 for a 10% more effective vaccine). Changes in effectiveness, out-of-pocket costs and in the body that advises the vaccine all substantially influenced the predicted uptake. We conclude that various disease and vaccination program characteristics influence respondents' preferences for pandemic vaccination programs. Agencies responsible for preventive measures during pandemics can use the knowledge that out-of-pocket costs and the way advice is given affect vaccination uptake to improve their plans for future pandemic outbreaks. The preference heterogeneity shows that information regarding vaccination needs to be targeted differently depending on gender and willingness to get vaccinated.

Sentinel surveillance for human enterovirus 71 in Sarawak, Malaysia: lessons from the first 7 years.

PubMed

Podin, Yuwana; Gias, Edna L M; Ong, Flora; Leong, Yee-Wei; Yee, Siew-Fung; Yusof, Mohd Apandi; Perera, David; Teo, Bibiana; Wee, Thian-Yew; Yao, Sik-Chi; Yao, Sik-King; Kiyu, Andrew; Arif, Mohd Taha; Cardosa, Mary Jane

2006-07-07

A major outbreak of human enterovirus 71-associated hand, foot and mouth disease in Sarawak in 1997 marked the beginning of a series of outbreaks in the Asia Pacific region. Some of these outbreaks had unusually high numbers of fatalities and this generated much fear and anxiety in the region. We established a sentinel surveillance programme for hand, foot and mouth disease in Sarawak, Malaysia, in March 1998, and the observations of the first 7 years are described here. Virus isolation, serotyping and genotyping were performed on throat, rectal, vesicle and other swabs. During this period Sarawak had two outbreaks of human enterovirus 71, in 2000 and 2003. The predominant strains circulating in the outbreaks of 1997, 2000 and 2003 were all from genogroup B, but the strains isolated during each outbreak were genetically distinct from each other. Human enterovirus 71 outbreaks occurred in a cyclical pattern every three years and Coxsackievirus A16 co-circulated with human enterovirus 71. Although vesicles were most likely to yield an isolate, this sample was not generally available from most cases and obtaining throat swabs was thus found to be the most efficient way to obtain virological information. Knowledge of the epidemiology of human enterovirus 71 transmission will allow public health personnel to predict when outbreaks might occur and to plan interventions in an effective manner in order to reduce the burden of disease.

Using Google Trends and ambient temperature to predict seasonal influenza outbreaks.

PubMed

Zhang, Yuzhou; Bambrick, Hilary; Mengersen, Kerrie; Tong, Shilu; Hu, Wenbiao

2018-05-16

The discovery of the dynamics of seasonal and non-seasonal influenza outbreaks remains a great challenge. Previous internet-based surveillance studies built purely on internet or climate data do have potential error. We collected influenza notifications, temperature and Google Trends (GT) data between January 1st, 2011 and December 31st, 2016. We performed time-series cross correlation analysis and temporal risk analysis to discover the characteristics of influenza epidemics in the period. Then, the seasonal autoregressive integrated moving average (SARIMA) model and regression tree model were developed to track influenza epidemics using GT and climate data. Influenza infection was significantly corrected with GT at lag of 1-7 weeks in Brisbane and Gold Coast, and temperature at lag of 1-10 weeks for the two study settings. SARIMA models with GT and temperature data had better predictive performance. We identified autoregression (AR) for influenza was the most important determinant for influenza occurrence in both Brisbane and Gold Coast. Our results suggested internet search metrics in conjunction with temperature can be used to predict influenza outbreaks, which can be considered as a pre-requisite for constructing early warning systems using search and temperature data. Copyright © 2018 Elsevier Ltd. All rights reserved.

Social media in Ebola outbreak.

PubMed

Hossain, L; Kam, D; Kong, F; Wigand, R T; Bossomaier, T

2016-07-01

The West African 2014 Ebola outbreak has highlighted the need for a better information network. Hybrid information networks, an integration of both hierarchical and formalized command control-driven and community-based, or ad hoc emerging networks, could assist in improving public health responses. By filling the missing gaps with social media use, the public health response could be more proactive rather than reactive in responding to such an outbreak of global concern. This article provides a review of the current social media use specifically in this outbreak by systematically collecting data from ProQuest Newsstand, Dow Jones Factiva, Program for Monitoring Emerging Diseases (ProMED) as well as Google Trends. The period studied is from 19 March 2014 (first request for information on ProMED) to 15 October 2014, a total of 31 weeks. The term 'Ebola' was used in the search for media reports. The outcome of the review shows positive results for social media use in effective surveillance response mechanisms - for improving the detection, preparedness and response of the outbreak - as a complement to traditional, filed, work-based surveillance approach.

A Qualitative Inquiry About Pruno, an Illicit Alcoholic Beverage Linked to Botulism Outbreaks in United States Prisons.

PubMed

Walters, Maroya Spalding; Sreenivasan, Nandini; Person, Bobbie; Shew, Mark; Wheeler, Daniel; Hall, Julia; Bogdanow, Linda; Leniek, Karyn; Rao, Agam

2015-11-01

Since 2011, 3 outbreaks of botulism in US prisons have been attributed to pruno, which is an alcoholic beverage made by inmates. Following 1 outbreak, we conducted a qualitative inquiry to understand pruno brewing and its social context to inform outbreak prevention measures. We interviewed staff, inmates, and parolees from 1 prison about pruno production methods, the social aspects of pruno, and strategies for communicating the association between botulism and pruno. Twenty-seven inmates and parolees and 13 staff completed interviews. Pruno is fermented from water, fruit, sugar, and miscellaneous ingredients. Knowledge of pruno making was widespread among inmates; staff were familiar with only the most common ingredients and supplies inmates described. Staff and inmates described inconsistent consequences for pruno possession and suggested using graphic health messages from organizations external to the prison to communicate the risk of botulism from pruno. Pruno making was frequent in this prison. Improved staff recognition of pruno ingredients and supplies might improve detection of brewing activities in this and other prisons. Consistent consequences and clear messages about the association between pruno and botulism might prevent outbreaks.

Analysis and Modeling of Influenza Outbreaks as Driven by Weather

NASA Astrophysics Data System (ADS)

Thrastarson, H. T.; Teixeira, J.; Serman, E. A.; Parekh, A.; Yeo, E.

2017-12-01

Seasonal influenza outbreaks are a major source of illness, mortality and economic burden worldwide. Attributing what drives the seasonality of the outbreaks is still an unsettled problem. But in temperate regions absolute humidity conditions are a strong candidate (Shaman et al., 2010) and some studies have associated temperature conditions with influenza outbreaks. We use humidity and temperature data from NASA's AIRS (Atmospheric Infra-Red Sounder) instrument as well as data for influenza incidence in the US and South Africa to explore the connection between weather and influenza seasonality at different spatial scales. We also incorporate influenza surveillance data, satellite data and humidity forecasts into a numerical epidemiological prediction system. Our results give support for the role of local weather conditions as drivers of the seasonality of influenza in temperate regions. This can have implications for public health efforts where forecasting of the timing and intensity of influenza outbreaks has a great potential role (e.g., aiding management and organization of vaccines, drugs and other resources).

Cholera forecast for Dhaka, Bangladesh, with the 2015-2016 El Niño: Lessons learned

PubMed Central

Martinez, Pamela P.; Reiner, Robert C.; Cash, Benjamin A.; Rodó, Xavier; Shahjahan Mondal, Mohammad; Roy, Manojit; Yunus, Mohammad; Faruque, A. S. G.; Huq, Sayeeda; King, Aaron A.; Pascual, Mercedes

2017-01-01

A substantial body of work supports a teleconnection between the El Niño-Southern Oscillation (ENSO) and cholera incidence in Bangladesh. In particular, high positive anomalies during the winter (Dec-Feb) in sea surface temperatures (SST) in the tropical Pacific have been shown to exacerbate the seasonal outbreak of cholera following the monsoons from August to November. Climate studies have indicated a role of regional precipitation over Bangladesh in mediating this long-distance effect. Motivated by this previous evidence, we took advantage of the strong 2015–2016 El Niño event to evaluate the predictability of cholera dynamics for the city in recent times based on two transmission models that incorporate SST anomalies and are fitted to the earlier surveillance records starting in 1995. We implemented a mechanistic temporal model that incorporates both epidemiological processes and the effect of ENSO, as well as a previously published statistical model that resolves space at the level of districts (thanas). Prediction accuracy was evaluated with “out-of-fit” data from the same surveillance efforts (post 2008 and 2010 for the two models respectively), by comparing the total number of cholera cases observed for the season to those predicted by model simulations eight to twelve months ahead, starting in January each year. Although forecasts were accurate for the low cholera risk observed for the years preceding the 2015–2016 El Niño, the models also predicted a high probability of observing a large outbreak in fall 2016. Observed cholera cases up to Oct 2016 did not show evidence of an anomalous season. We discuss these predictions in the context of regional and local climate conditions, which show that despite positive regional rainfall anomalies, rainfall and inundation in Dhaka remained low. Possible explanations for these patterns are given together with future implications for cholera dynamics and directions to improve their prediction for the city. PMID:28253325

Cholera forecast for Dhaka, Bangladesh, with the 2015-2016 El Niño: Lessons learned.

PubMed

Martinez, Pamela P; Reiner, Robert C; Cash, Benjamin A; Rodó, Xavier; Shahjahan Mondal, Mohammad; Roy, Manojit; Yunus, Mohammad; Faruque, A S G; Huq, Sayeeda; King, Aaron A; Pascual, Mercedes

2017-01-01

A substantial body of work supports a teleconnection between the El Niño-Southern Oscillation (ENSO) and cholera incidence in Bangladesh. In particular, high positive anomalies during the winter (Dec-Feb) in sea surface temperatures (SST) in the tropical Pacific have been shown to exacerbate the seasonal outbreak of cholera following the monsoons from August to November. Climate studies have indicated a role of regional precipitation over Bangladesh in mediating this long-distance effect. Motivated by this previous evidence, we took advantage of the strong 2015-2016 El Niño event to evaluate the predictability of cholera dynamics for the city in recent times based on two transmission models that incorporate SST anomalies and are fitted to the earlier surveillance records starting in 1995. We implemented a mechanistic temporal model that incorporates both epidemiological processes and the effect of ENSO, as well as a previously published statistical model that resolves space at the level of districts (thanas). Prediction accuracy was evaluated with "out-of-fit" data from the same surveillance efforts (post 2008 and 2010 for the two models respectively), by comparing the total number of cholera cases observed for the season to those predicted by model simulations eight to twelve months ahead, starting in January each year. Although forecasts were accurate for the low cholera risk observed for the years preceding the 2015-2016 El Niño, the models also predicted a high probability of observing a large outbreak in fall 2016. Observed cholera cases up to Oct 2016 did not show evidence of an anomalous season. We discuss these predictions in the context of regional and local climate conditions, which show that despite positive regional rainfall anomalies, rainfall and inundation in Dhaka remained low. Possible explanations for these patterns are given together with future implications for cholera dynamics and directions to improve their prediction for the city.

Organomercury poisoning in Iraq: History prior to the 1971-72 outbreak

PubMed Central

Al-Damluji, S. F.

1976-01-01

Mercury has been used medically in the Middle East since time immemorial. Organomercury compounds were first used as seed dressings in Iraq in 1955. During the years 1955-59, 200 cases of poisoning occurred. A more clearly defined outbreak occurred in 1960, involving approximately 1000 hospital admissions. Twelve patients from two families affected in this outbreak were re-examined in 1973, and showed considerable improvement. PMID:788949

Spatial diffusion of influenza outbreak-related climate factors in Chiang Mai Province, Thailand.

PubMed

Nakapan, Supachai; Tripathi, Nitin Kumar; Tipdecho, Taravudh; Souris, Marc

2012-10-24

Influenza is one of the most important leading causes of respiratory illness in the countries located in the tropical areas of South East Asia and Thailand. In this study the climate factors associated with influenza incidence in Chiang Mai Province, Northern Thailand, were investigated. Identification of factors responsible for influenza outbreaks and the mapping of potential risk areas in Chiang Mai are long overdue. This work examines the association between yearly climate patterns between 2001 and 2008 and influenza outbreaks in the Chiang Mai Province. The climatic factors included the amount of rainfall, percent of rainy days, relative humidity, maximum, minimum temperatures and temperature difference. The study develops a statistical analysis to quantitatively assess the relationship between climate and influenza outbreaks and then evaluate its suitability for predicting influenza outbreaks. A multiple linear regression technique was used to fit the statistical model. The Inverse Distance Weighted (IDW) interpolation and Geographic Information System (GIS) techniques were used in mapping the spatial diffusion of influenza risk zones. The results show that there is a significance correlation between influenza outbreaks and climate factors for the majority of the studied area. A statistical analysis was conducted to assess the validity of the model comparing model outputs and actual outbreaks.

Water and sediment characteristics associated with avian botulism outbreaks in wetlands

USGS Publications Warehouse

Rocke, Tonie E.; Samuel, Michael D.

1999-01-01

Avian botulism kills thousands of waterbirds annually throughout North America, but management efforts to reduce its effects have been hindered because environmental conditions that promote outbreaks are poorly understood. We measured sediment and water variables in 32 pairs of wetlands with and without a current outbreak of avian botulism. Wetlands with botulism outbreaks had greater percent organic matter (POM) in the sediment (P = 0.088) and lower redox potential in the water (P = 0.096) than paired control wetlands. We also found that pH, redox potential, temperature, and salinity measured just above the sediment-water interface were associated (P ≤ 0.05) with the risk of botulism outbreaks in wetlands, but relations were complex, involving nonlinear and multivariate associations. Regression models indicated that the risk of botulism outbreaks increased when water pH was between 7.5 and 9.0, redox potential was negative, and water temperature was >20°C. Risk declined when redox potential increased (>100), water temperature decreased (10-15°C), pH was 9.0, or salinity was low (<2.0 ppt). Our predictive models could allow managers to assess potential effects of wetland management practices on the risk of botulism outbreaks and to develop and evaluate alternative management strategies to reduce losses from avian botulism.

West Nile virus outbreak in Phoenix, Arizona--2010: entomological observations and epidemiological correlations.

PubMed

Colborn, James M; Smith, Kirk A; Townsend, John; Damian, Dan; Nasci, Roger S; Mutebi, John-Paul

2013-06-01

In 2010, Arizona experienced an unusually early and severe outbreak of West Nile virus (WNV) centered in the southeast section of Maricopa County. Entomological data were collected before and during the outbreak, from May 25 through July 31, 2010, using the CO2-baited light trap monitoring system maintained by Maricopa County Vector Control. In the outbreak area, the most abundant species in the Town of Gilbert and in the area covered by the Roosevelt Water Conservation District was Culex quinquefasciatus, constituting 75.1% and 71.8% of the total number of mosquitoes collected, respectively. Vector index (VI) profiles showed that the abundance of infected Cx. quinquefasciatus peaked prior to human cases, suggesting that this species was involved in the initiation of the outbreak. In contrast, the VI profiles for Cx. tarsalis were consistently low, suggesting limited involvement in initiating and sustaining transmission. Taken together, the higher abundance and the VI profiles strongly suggest that Cx. quinquefasciatus was the primary vector for this outbreak. The VI profiles consistently showed that the abundance of infected mosquitoes peaked 1 to 2 wk before the peaks of human cases, suggesting that VI could have successfully been utilized to predict the WNV outbreak in Maricopa County, AZ, in 2010.

Intra- and Interseasonal Autoregressive Prediction of Dengue Outbreaks Using Local Weather and Regional Climate for a Tropical Environment in Colombia

PubMed Central

Eastin, Matthew D.; Delmelle, Eric; Casas, Irene; Wexler, Joshua; Self, Cameron

2014-01-01

Dengue fever transmission results from complex interactions between the virus, human hosts, and mosquito vectors—all of which are influenced by environmental factors. Predictive models of dengue incidence rate, based on local weather and regional climate parameters, could benefit disease mitigation efforts. Time series of epidemiological and meteorological data for the urban environment of Cali, Colombia are analyzed from January of 2000 to December of 2011. Significant dengue outbreaks generally occur during warm-dry periods with extreme daily temperatures confined between 18°C and 32°C—the optimal range for mosquito survival and viral transmission. Two environment-based, multivariate, autoregressive forecast models are developed that allow dengue outbreaks to be anticipated from 2 weeks to 6 months in advance. These models have the potential to enhance existing dengue early warning systems, ultimately supporting public health decisions on the timing and scale of vector control efforts. PMID:24957546

Mapping the zoonotic niche of Ebola virus disease in Africa

PubMed Central

Pigott, David M; Golding, Nick; Mylne, Adrian; Huang, Zhi; Henry, Andrew J; Weiss, Daniel J; Brady, Oliver J; Kraemer, Moritz UG; Smith, David L; Moyes, Catherine L; Bhatt, Samir; Gething, Peter W; Horby, Peter W; Bogoch, Isaac I; Brownstein, John S; Mekaru, Sumiko R; Tatem, Andrew J; Khan, Kamran; Hay, Simon I

2014-01-01

Ebola virus disease (EVD) is a complex zoonosis that is highly virulent in humans. The largest recorded outbreak of EVD is ongoing in West Africa, outside of its previously reported and predicted niche. We assembled location data on all recorded zoonotic transmission to humans and Ebola virus infection in bats and primates (1976–2014). Using species distribution models, these occurrence data were paired with environmental covariates to predict a zoonotic transmission niche covering 22 countries across Central and West Africa. Vegetation, elevation, temperature, evapotranspiration, and suspected reservoir bat distributions define this relationship. At-risk areas are inhabited by 22 million people; however, the rarity of human outbreaks emphasises the very low probability of transmission to humans. Increasing population sizes and international connectivity by air since the first detection of EVD in 1976 suggest that the dynamics of human-to-human secondary transmission in contemporary outbreaks will be very different to those of the past. DOI: http://dx.doi.org/10.7554/eLife.04395.001 PMID:25201877

Pre-outbreak forest conditions mediate the effects of spruce beetle outbreaks on fuels in subalpine forests of Colorado.

PubMed

Mietkiewicz, Nathan; Kulakowski, Dominik; Veblen, Thomas T

2018-03-01

Over the past 30 years, forest disturbances have increased in size, intensity, and frequency globally, and are predicted to continue increasing due to climate change, potentially relaxing the constraints of vegetation properties on disturbance regimes. However, the consequences of the potentially declining importance of vegetation in determining future disturbance regimes are not well understood. Historically, bark beetles preferentially attack older trees and stands in later stages of development. However, as climate warming intensifies outbreaks by promoting growth of beetle populations and compromising tree defenses, smaller diameter trees and stands in early stages of development now are being affected by outbreaks. To date, no study has considered how stand age and other pre-outbreak forest conditions mediate the effects of outbreaks on surface and aerial fuel arrangements. We collected fuels data across a chronosequence of post-outbreak sites affected by spruce beetle (SB) between the 1940s and the 2010s, stratified by young (<130 yr) and old (>130 yr) post-fire stands. Canopy and surface fuel loads were calculated for each tree and stand, and available crown fuel load, crown bulk density, and canopy bulk densities were estimated. Canopy bulk density and density of live canopy individuals were reduced in all stands affected by SB, though foliage loss was proportionally greater in old stands as compared to young stands. Fine surface fuel loads in young stands were three times greater shortly (<30 yr) following outbreak as compared to young stands not affected by outbreak, after which the abundance of fine surface fuels decreased to below endemic (i.e., non-outbreak) levels. In both young and old stands, the net effect of SB outbreaks during the 20th and 21st centuries reduced total canopy fuels and increased stand-scale spatial heterogeneity of canopy fuels following outbreak. Importantly, the decrease in canopy fuels following outbreaks was greater in young post-fire stands than in older stands, suggesting that SB outbreaks may more substantially reduce risk of active crown fire when they affect stands in earlier stages of development. The current study shows that the effects of SB outbreaks on forest structure and on fuel profiles are strongly contingent on pre-outbreak conditions as determined by pre-outbreak disturbance history. © 2018 by the Ecological Society of America.

Response to a Large Polio Outbreak in a Setting of Conflict - Middle East, 2013-2015.

PubMed

Mbaeyi, Chukwuma; Ryan, Michael J; Smith, Philip; Mahamud, Abdirahman; Farag, Noha; Haithami, Salah; Sharaf, Magdi; Jorba, Jaume C; Ehrhardt, Derek

2017-03-03

As the world advances toward the eradication of polio, outbreaks of wild poliovirus (WPV) in polio-free regions pose a substantial risk to the timeline for global eradication. Countries and regions experiencing active conflict, chronic insecurity, and large-scale displacement of persons are particularly vulnerable to outbreaks because of the disruption of health care and immunization services (1). A polio outbreak occurred in the Middle East, beginning in Syria in 2013 with subsequent spread to Iraq (2). The outbreak occurred 2 years after the onset of the Syrian civil war, resulted in 38 cases, and was the first time WPV was detected in Syria in approximately a decade (3,4). The national governments of eight countries designated the outbreak a public health emergency and collaborated with partners in the Global Polio Eradication Initiative (GPEI) to develop a multiphase outbreak response plan focused on improving the quality of acute flaccid paralysis (AFP) surveillance* and administering polio vaccines to >27 million children during multiple rounds of supplementary immunization activities (SIAs). † Successful implementation of the response plan led to containment and interruption of the outbreak within 6 months of its identification. The concerted approach adopted in response to this outbreak could serve as a model for responding to polio outbreaks in settings of conflict and political instability.

Healthcare-associated outbreaks due to Mucorales and other uncommon fungi.

PubMed

Davoudi, Setareh; Graviss, Linda S; Kontoyiannis, Dimitrios P

2015-07-01

Healthcare-associated outbreaks of fungal infections, especially with uncommon and emerging fungi, have become more frequent in the past decade. Here, we reviewed the history and definition of healthcare-associated outbreaks of uncommon fungal infections and discussed the principles of investigating, containing and treatment of these outbreaks. In case of these uncommon diseases, occurrence of two or more cases in a short period is considered as an outbreak. Contaminated medical devices and hospital environment are the major sources of these outbreaks. Care must be taken to differentiate a real infection from colonization or contamination. Defining and identifying cases, describing epidemiologic feature of cases, finding and controlling the source of the outbreak, treating patients, and managing asymptomatic exposed patients are main steps for outbreak elimination. These fungal outbreaks are not only difficult to detect but also hard to treat. Early initiation of appropriate antifungal therapy is strongly associated with improved outcomes in infected patients. Choice of antifungal drugs should be made based on spectrum, pharmacodynamic and pharmacokinetic characteristics and adverse effects of available drugs. Combination antifungal therapy and surgical intervention may be also helpful in selected cases. A multidisciplinary approach and close collaboration between all key partners are necessary for successful control of fungal outbreaks. © 2015 Stichting European Society for Clinical Investigation Journal Foundation.

A comprehensive database of the geographic spread of past human Ebola outbreaks

PubMed Central

Mylne, Adrian; Brady, Oliver J.; Huang, Zhi; Pigott, David M.; Golding, Nick; Kraemer, Moritz U.G.; Hay, Simon I.

2014-01-01

Ebola is a zoonotic filovirus that has the potential to cause outbreaks of variable magnitude in human populations. This database collates our existing knowledge of all known human outbreaks of Ebola for the first time by extracting details of their suspected zoonotic origin and subsequent human-to-human spread from a range of published and non-published sources. In total, 22 unique Ebola outbreaks were identified, composed of 117 unique geographic transmission clusters. Details of the index case and geographic spread of secondary and imported cases were recorded as well as summaries of patient numbers and case fatality rates. A brief text summary describing suspected routes and means of spread for each outbreak was also included. While we cannot yet include the ongoing Guinea and DRC outbreaks until they are over, these data and compiled maps can be used to gain an improved understanding of the initial spread of past Ebola outbreaks and help evaluate surveillance and control guidelines for limiting the spread of future epidemics. PMID:25984346

A comprehensive database of the geographic spread of past human Ebola outbreaks.

PubMed

Mylne, Adrian; Brady, Oliver J; Huang, Zhi; Pigott, David M; Golding, Nick; Kraemer, Moritz U G; Hay, Simon I

2014-01-01

Ebola is a zoonotic filovirus that has the potential to cause outbreaks of variable magnitude in human populations. This database collates our existing knowledge of all known human outbreaks of Ebola for the first time by extracting details of their suspected zoonotic origin and subsequent human-to-human spread from a range of published and non-published sources. In total, 22 unique Ebola outbreaks were identified, composed of 117 unique geographic transmission clusters. Details of the index case and geographic spread of secondary and imported cases were recorded as well as summaries of patient numbers and case fatality rates. A brief text summary describing suspected routes and means of spread for each outbreak was also included. While we cannot yet include the ongoing Guinea and DRC outbreaks until they are over, these data and compiled maps can be used to gain an improved understanding of the initial spread of past Ebola outbreaks and help evaluate surveillance and control guidelines for limiting the spread of future epidemics.

Ecological Forecasting of West Nile Virus Outbreaks in a High-Risk Area of the North-Central United States

NASA Astrophysics Data System (ADS)

Wimberly, M. C.; Merkord, C. L.; Kightlinger, L.; Vincent, G.; Hildreth, M. B.

2015-12-01

West Nile virus (WNV) is the most widespread and important mosquito-borne pathogen in North America. Since its emergence in the western hemisphere in 1999, human WNV disease has continued to exhibit recurrent outbreaks. Perplexingly, the incidence of this tropical disease has been highest in the cold-temperate climates of the Northern Great Plains (NGP). The spatial and temporal distributions of the vector mosquitoes and bird hosts, and consequently the risk of disease in humans, are strongly influenced by temperature, precipitation, vegetation, soils, and land use. We have utilized satellite remote sensing to map these environmental factors through time and develop models of disease risk. Outbreak years in South Dakota were preceded by warm winters, and WNV cases were most likely to occur during the hottest weeks of summer. Hot spots of persistent WNV transmission within the state were associated with rural land cover as well as patterns of physiography and climate. These models are currently being integrated into the South Dakota Mosquito Early Warning system (SDMIS), an automated WNV outbreak detection system that integrates remotely-sensed environmental indicators with vector abundance and infection data from a statewide mosquito surveillance network. The major goal of this effort is to leverage global environmental monitoring datasets to provide up-to-date, locally relevant information that can improve the effectiveness of mosquito control and disease prevention activities. This system was implemented for the first time during the summer of 2015. We will review the outcomes of this implementation, including the underlying influences of temperature on WNV risk, a preliminary statewide WNV risk map, and dynamic risk predictions made during the 2015 WNV season. Lessons learned as well as plans for future years will be discussed.

Predicting potential and actual distribution of sudden oak death in Oregon: prioritizing landscape contexts for early detection and eradication of disease outbreaks

Treesearch

Tomas Vaclavik; Alan Kanaskie; Everett M. Hansen; Janet L. Ohmann; Ross K. Meentemeyer

2010-01-01

An isolated outbreak of the emerging forest disease sudden oak death was discovered in Oregon forests in 2001. Despite considerable control efforts, disease continues to spread from the introduction site due to slow and incomplete detection and eradication. Annual field surveys and laboratory tests between 2001 and 2009 confirmed a total of 802 infested locations. Here...

A review of influenza detection and prediction through social networking sites.

PubMed

Alessa, Ali; Faezipour, Miad

2018-02-01

Early prediction of seasonal epidemics such as influenza may reduce their impact in daily lives. Nowadays, the web can be used for surveillance of diseases. Search engines and social networking sites can be used to track trends of different diseases seven to ten days faster than government agencies such as Center of Disease Control and Prevention (CDC). CDC uses the Illness-Like Influenza Surveillance Network (ILINet), which is a program used to monitor Influenza-Like Illness (ILI) sent by thousands of health care providers in order to detect influenza outbreaks. It is a reliable tool, however, it is slow and expensive. For that reason, many studies aim to develop methods that do real time analysis to track ILI using social networking sites. Social media data such as Twitter can be used to predict the spread of flu in the population and can help in getting early warnings. Today, social networking sites (SNS) are used widely by many people to share thoughts and even health status. Therefore, SNS provides an efficient resource for disease surveillance and a good way to communicate to prevent disease outbreaks. The goal of this study is to review existing alternative solutions that track flu outbreak in real time using social networking sites and web blogs. Many studies have shown that social networking sites can be used to conduct real time analysis for better predictions.

Real-time projections of cholera outbreaks through data assimilation and rainfall forecasting

NASA Astrophysics Data System (ADS)

Pasetto, Damiano; Finger, Flavio; Rinaldo, Andrea; Bertuzzo, Enrico

2017-10-01

Although treatment for cholera is well-known and cheap, outbreaks in epidemic regions still exact high death tolls mostly due to the unpreparedness of health care infrastructures to face unforeseen emergencies. In this context, mathematical models for the prediction of the evolution of an ongoing outbreak are of paramount importance. Here, we test a real-time forecasting framework that readily integrates new information as soon as available and periodically issues an updated forecast. The spread of cholera is modeled by a spatially-explicit scheme that accounts for the dynamics of susceptible, infected and recovered individuals hosted in different local communities connected through hydrologic and human mobility networks. The framework presents two major innovations for cholera modeling: the use of a data assimilation technique, specifically an ensemble Kalman filter, to update both state variables and parameters based on the observations, and the use of rainfall forecasts to force the model. The exercise of simulating the state of the system and the predictive capabilities of the novel tools, set at the initial phase of the 2010 Haitian cholera outbreak using only information that was available at that time, serves as a benchmark. Our results suggest that the assimilation procedure with the sequential update of the parameters outperforms calibration schemes based on Markov chain Monte Carlo. Moreover, in a forecasting mode the model usefully predicts the spatial incidence of cholera at least one month ahead. The performance decreases for longer time horizons yet allowing sufficient time to plan for deployment of medical supplies and staff, and to evaluate alternative strategies of emergency management.

Generalized reproduction numbers and the prediction of patterns in waterborne disease.

PubMed

Gatto, Marino; Mari, Lorenzo; Bertuzzo, Enrico; Casagrandi, Renato; Righetto, Lorenzo; Rodriguez-Iturbe, Ignacio; Rinaldo, Andrea

2012-11-27

Understanding, predicting, and controlling outbreaks of waterborne diseases are crucial goals of public health policies, but pose challenging problems because infection patterns are influenced by spatial structure and temporal asynchrony. Although explicit spatial modeling is made possible by widespread data mapping of hydrology, transportation infrastructure, population distribution, and sanitation, the precise condition under which a waterborne disease epidemic can start in a spatially explicit setting is still lacking. Here we show that the requirement that all the local reproduction numbers R0 be larger than unity is neither necessary nor sufficient for outbreaks to occur when local settlements are connected by networks of primary and secondary infection mechanisms. To determine onset conditions, we derive general analytical expressions for a reproduction matrix G0, explicitly accounting for spatial distributions of human settlements and pathogen transmission via hydrological and human mobility networks. At disease onset, a generalized reproduction number Λ0 (the dominant eigenvalue of G0) must be larger than unity. We also show that geographical outbreak patterns in complex environments are linked to the dominant eigenvector and to spectral properties of G0. Tests against data and computations for the 2010 Haiti and 2000 KwaZulu-Natal cholera outbreaks, as well as against computations for metapopulation networks, demonstrate that eigenvectors of G0 provide a synthetic and effective tool for predicting the disease course in space and time. Networked connectivity models, describing the interplay between hydrology, epidemiology, and social behavior sustaining human mobility, thus prove to be key tools for emergency management of waterborne infections.

[The role of the "infection control nurse (ICN)" in an outbreak].

PubMed

Morishita, Sachiko

2004-01-01

The nosocomial infection and its expansion is a big problem for both customers and healthcare organizations. It is one of the most important tasks for the infection control team to prevent spreadng the hospital infection and to minimize its damage for clients and healthcare organizations. Japanese nurse association (JNA) has established the system of the certified nurses who finished authorized certification program in 15 areas including infection control in 1999. They are called "Certified Expert nurse (CEN)" and the number of CEN concerning about infection control (ICN) has reached 146 nurses in Japan in 2004. ICNs should have an ability to predict the possibility of an outbreak and to take measures to meet the situation, practically direct intervention and research for the outbreak. In this literature I have summarized the roles of ICN, introducing our experience of the scabies outbreak in our affiliated intermediate care facility for the senior citizens. It is important for ICN to research and analyze the outbreak, at the same time we have to feedback effectively to the healthcare stuffs to change their attitude and develop the system to discover the outbreak earlier by educating them.

Natural Disasters and Cholera Outbreaks: Current Understanding and Future Outlook.

PubMed

Jutla, Antarpreet; Khan, Rakibul; Colwell, Rita

2017-03-01

Diarrheal diseases remain a serious global public health threat, especially for those populations lacking access to safe water and sanitation infrastructure. Although association of several diarrheal diseases, e.g., cholera, shigellosis, etc., with climatic processes has been documented, the global human population remains at heightened risk of outbreak of diseases after natural disasters, such as earthquakes, floods, or droughts. In this review, cholera was selected as a signature diarrheal disease and the role of natural disasters in triggering and transmitting cholera was analyzed. Key observations include identification of an inherent feedback loop that includes societal structure, prevailing climatic processes, and spatio-temporal seasonal variability of natural disasters. Data obtained from satellite-based remote sensing are concluded to have application, although limited, in predicting risks of a cholera outbreak(s). We argue that with the advent of new high spectral and spatial resolution data, earth observation systems should be seamlessly integrated in a decision support mechanism to be mobilize resources when a region suffers a natural disaster. A framework is proposed that can be used to assess the impact of natural disasters with response to outbreak of cholera, providing assessment of short- and long-term influence of climatic processes on disease outbreaks.

A review of nosocomial Salmonella outbreaks: infection control interventions found effective.

PubMed

Lee, M B; Greig, J D

2013-03-01

To review nosocomial salmonellosis outbreaks to identify: mode of transmission; morbidity and mortality patterns; and recommendations for control and prevention. Documented nosocomial salmonellosis outbreaks in hospitals published from January 1995 to November 2011, written in the English language, were systematically reviewed. The study methodology incorporated steps from the PRISMA statement for a high quality review process. Computer-aided searches of Scopus, CAB Global Health and CINAHL(®), the Cumulative Index to Nursing and Allied Health Literature were completed to identify relevant outbreak reports written in English. To validate the electronic search methodology, bibliographies and reference lists of relevant review articles were hand-searched. Public health and government websites were searched for nosocomial salmonellosis. Fifty-two relevant reports were identified. The most frequently reported routes of transmission were food 31/52 (59.6%) and person-to-person transmission 7/52 (13.5%). Actions taken during the outbreak to control transmission included improvements to: 1) infection control practices (41.8% of actions); isolation or cohorting patients, hand hygiene practices, and enhancing cleaning and disinfection in patient care areas; and 2) food handling practices (24.4% of actions); reviewing food preparation practices, enhancing cleaning and sanitation of the kitchen, and controlling food temperatures. Investigators made recommendations retrospectively in outbreak reports to provide direction to health centees but these recommendations were not statistically evaluated for effectiveness. More emphasis should be placed on improving food handling practices, such as training food workers, monitoring food temperatures, and not using raw foods of animal origin, to prevent nosocomial salmonellosis outbreaks in hospitals because almost 60% of the outbreaks were foodborne. Copyright © 2013 The Royal Institute of Public Health. All rights reserved.

Managing Ebola from rural to urban slum settings: experiences from Uganda.

PubMed

Okware, Sam I; Omaswa, Francis; Talisuna, Ambrose; Amandua, Jacinto; Amone, Jackson; Onek, Paul; Opio, Alex; Wamala, Joseph; Lubwama, Julius; Luswa, Lukwago; Kagwa, Paul; Tylleskar, Thorkild

2015-03-01

Five outbreaks of ebola occurred in Uganda between 2000-2012. The outbreaks were quickly contained in rural areas. However, the Gulu outbreak in 2000 was the largest and complex due to insurgency. It invaded Gulu municipality and the slum- like camps of the internally displaced persons (IDPs). The Bundigugyo district outbreak followed but was detected late as a new virus. The subsequent outbreaks in the districts of Luwero district (2011, 2012) and Kibaale (2012) were limited to rural areas. Detailed records of the outbreak presentation, cases, and outcomes were reviewed and analyzed. Each outbreak was described and the outcomes examined for the different scenarios. Early detection and action provided the best outcomes and results. The ideal scenario occurred in the Luwero outbreak during which only a single case was observed. Rural outbreaks were easier to contain. The community imposed quarantine prevented the spread of ebola following introduction into Masindi district. The outbreak was confined to the extended family of the index case and only one case developed in the general population. However, the outbreak invasion of the town slum areas escalated the spread of infection in Gulu municipality. Community mobilization and leadership was vital in supporting early case detection and isolations well as contact tracing and public education. Palliative care improved survival. Focusing on treatment and not just quarantine should be emphasized as it also enhanced public trust and health seeking behavior. Early detection and action provided the best scenario for outbreak containment. Community mobilization and leadership was vital in supporting outbreak control. International collaboration was essential in supporting and augmenting the national efforts.

Fluctuations in epidemic modeling - disease extinction and control

NASA Astrophysics Data System (ADS)

Schwartz, Ira

2009-03-01

The analysis of infectious disease fluctuations has recently seen an increasing rise in the use of new tools and models from stochastic dynamics and statistical physics. Examples arise in modeling fluctuations of multi-strain diseases, in modeling adaptive social behavior and its impact on disease fluctuations, and in the analysis of disease extinction in finite population models. Proper stochastic model reduction [1] allows one to predict unobserved fluctuations from observed data in multi-strain models [2]. Degree alteration and power law behavior is predicted in adaptive network epidemic models [3,4]. And extinction rates derived from large fluctuation theory exhibit scaling with respect to distance to the bifurcation point of disease onset with an unusual exponent [5]. In addition to outbreak prediction, another main goal of epidemic modeling is one of eliminating the disease to extinction through various control mechanisms, such as vaccine implementation or quarantine. In this talk, a description will be presented of the fluctuational behavior of several epidemic models and their extinction rates. A general framework and analysis of the effect of non-Gaussian control actuations which enhance the rate to disease extinction will be described. In particular, in it is shown that even in the presence of a small Poisson distributed vaccination program, there is an exponentially enhanced rate to disease extinction. These ideas may lead to improved methods of controlling disease where random vaccinations are prevalent. [4pt] Recent papers:[0pt] [1] E. Forgoston and I. B. Schwartz, ``Escape Rates in a Stochastic Environment with Multiple Scales,'' arXiv:0809.1345 2008.[0pt] [2] L. B. Shaw, L. Billings, I. B. Schwartz, ``Using dimension reduction to improve outbreak predictability of multi-strain diseases,'' J. Math. Bio. 55, 1 2007.[0pt] [3] L. B. Shaw and I. B. Schwartz, ``Fluctuating epidemics on adaptive networks,'' Physical Review E 77, 066101 2008.[0pt] [4] L. B. Shaw and I. B. Schwartz, ``Noise induced dynamics in adaptivenetworks with applications to epidemiology,'' arXiv:0807.3455 2008.[0pt] [5] M. I. Dykman, I. B. Schwartz, A. S. Landsman, ``Disease Extinction in the Presence of Random Vaccination,'' Phys. Rev. Letts. 101, 078101 2008.

Sentinel surveillance for human enterovirus 71 in Sarawak, Malaysia: lessons from the first 7 years

PubMed Central

Podin, Yuwana; Gias, Edna LM; Ong, Flora; Leong, Yee-Wei; Yee, Siew-Fung; Yusof, Mohd Apandi; Perera, David; Teo, Bibiana; Wee, Thian-Yew; Yao, Sik-Chi; Yao, Sik-King; Kiyu, Andrew; Arif, Mohd Taha; Cardosa, Mary Jane

2006-01-01

Background A major outbreak of human enterovirus 71-associated hand, foot and mouth disease in Sarawak in 1997 marked the beginning of a series of outbreaks in the Asia Pacific region. Some of these outbreaks had unusually high numbers of fatalities and this generated much fear and anxiety in the region. Methods We established a sentinel surveillance programme for hand, foot and mouth disease in Sarawak, Malaysia, in March 1998, and the observations of the first 7 years are described here. Virus isolation, serotyping and genotyping were performed on throat, rectal, vesicle and other swabs. Results During this period Sarawak had two outbreaks of human enterovirus 71, in 2000 and 2003. The predominant strains circulating in the outbreaks of 1997, 2000 and 2003 were all from genogroup B, but the strains isolated during each outbreak were genetically distinct from each other. Human enterovirus 71 outbreaks occurred in a cyclical pattern every three years and Coxsackievirus A16 co-circulated with human enterovirus 71. Although vesicles were most likely to yield an isolate, this sample was not generally available from most cases and obtaining throat swabs was thus found to be the most efficient way to obtain virological information. Conclusion Knowledge of the epidemiology of human enterovirus 71 transmission will allow public health personnel to predict when outbreaks might occur and to plan interventions in an effective manner in order to reduce the burden of disease. PMID:16827926

Functional Characterization of Adaptive Mutations during the West African Ebola Virus Outbreak.

PubMed

Dietzel, Erik; Schudt, Gordian; Krähling, Verena; Matrosovich, Mikhail; Becker, Stephan

2017-01-15

The Ebola virus (EBOV) outbreak in West Africa started in December 2013, claimed more than 11,000 lives, threatened to destabilize a whole region, and showed how easily health crises can turn into humanitarian disasters. EBOV genomic sequences of the West African outbreak revealed nonsynonymous mutations, which induced considerable public attention, but their role in virus spread and disease remains obscure. In this study, we investigated the functional significance of three nonsynonymous mutations that emerged early during the West African EBOV outbreak. Almost 90% of more than 1,000 EBOV genomes sequenced during the outbreak carried the signature of three mutations: a D759G substitution in the active center of the L polymerase, an A82V substitution in the receptor binding domain of surface glycoprotein GP, and an R111C substitution in the self-assembly domain of RNA-encapsidating nucleoprotein NP. Using a newly developed virus-like particle system and reverse genetics, we found that the mutations have an impact on the functions of the respective viral proteins and on the growth of recombinant EBOVs. The mutation in L increased viral transcription and replication, whereas the mutation in NP decreased viral transcription and replication. The mutation in the receptor binding domain of the glycoprotein GP improved the efficiency of GP-mediated viral entry into target cells. Recombinant EBOVs with combinations of the three mutations showed a growth advantage over the prototype isolate Makona C7 lacking the mutations. This study showed that virus variants with improved fitness emerged early during the West African EBOV outbreak. The dimension of the Ebola virus outbreak in West Africa was unprecedented. Amino acid substitutions in the viral L polymerase, surface glycoprotein GP, and nucleocapsid protein NP emerged, were fixed early in the outbreak, and were found in almost 90% of the sequences. Here we showed that these mutations affected the functional activity of viral proteins and improved viral growth in cell culture. Our results demonstrate emergence of adaptive changes in the Ebola virus genome during virus circulation in humans and prompt further studies on the potential role of these changes in virus transmissibility and pathogenicity. Copyright © 2017 American Society for Microbiology.

Role of Food Handlers in Norovirus Outbreaks in London and South East England, 2013 to 2015.

PubMed

Rumble, C; Addiman, S; Balasegaram, S; Chima, K; Ready, D; Heard, J; Alexander, E

2017-02-01

Outbreaks caused by norovirus infection are common and occur throughout the year. Outbreaks can be related to food outlets either through a contaminated food source or an infected food handler. Both asymptomatic and symptomatic food handlers are potentially implicated in outbreaks, but evidence of transmission is limited. To understand potential food handler transmission in outbreak scenarios, epidemiological and microbiological data on possible and confirmed norovirus outbreaks reported in London and South East England in a 2-year period were reviewed. One hundred eighty-six outbreaks were associated with a food outlet or registered caterer in this period. These occurred throughout the year with peaks in quarter 1 of study years. A case series of 17 outbreaks investigated by the local field epidemiological service were evaluated further, representing more than 606 cases. In five outbreaks, symptomatic food handlers were tested and found positive for norovirus. In four outbreaks, symptomatic food handlers were not tested. Asymptomatic food handlers were tested in three outbreaks but positive for norovirus in one only. Environmental sampling did not identify the causative agent conclusively in any of the outbreaks included in this analysis. Food sampling identified norovirus in one outbreak. Recommendations from this study include for outbreak investigations to encourage testing of symptomatic food handlers and for food and environmental samples to be taken as soon as possible. In addition, sampling of asymptomatic food handlers should be considered when possible. However, in light of the complexity in conclusively identifying a source of infection, general measures to improve hand hygiene are recommended, with specific education among food handlers about the potential for foodborne pathogen transmission during asymptomatic infection, as well as reinforcing the importance of self-exclusion from food handling activities when symptomatic.

Area contact networks and the spatio-temporal spread of infectious salmon anemia virus (ISAV) in Chile.

PubMed

Gustafson, L; Remmenga, M; Sandoval Del Valle, O; Ibarra, R; Antognoli, M; Gallardo, A; Rosenfeld, C; Doddis, J; Enriquez Sais, R; Bell, E; Lara Fica, M

2016-03-01

Area management, the coordination of production and biosecurity practices across neighboring farms, is an important disease control strategy in aquaculture. Area management in aquaculture escalated in prominence in response to outbreaks of infectious salmon anemia (ISA) internationally. Successes in disease control have been attributed to the separation achieved through area-level synchronized stocking, fallowing, movement restrictions, and fomite or pest control. Area management, however, is costly; often demanding extra biosecurity, lengthy or inconveniently timed fallows, and localization of equipment, personnel, and services. Yet, this higher-order organizational structure has received limited epidemiologic attention. Chile's National Fisheries and Aquaculture Service instigated area management practices in response to the 2007 emergence of ISA virus (ISAV). Longitudinal data simultaneously collected allowed retrospective evaluation of the impact of component tenets on virus control. Spatiotemporal analyses identified hydrographic linkages, shared ports, and fish transfers from areas with recent occurrence of ISAV as the strongest predictors of virus spread between areas, though specifics varied by ISAV type (here categorized as HPR0 for the non-virulent genotypes, and HPRv otherwise). Hydrographic linkages were most predictive in the period before implementation of enhanced biosecurity and fallowing regulations, suggesting that viral load can impact spread dynamics. HPR0 arose late in the study period, so few HPRv events were available by which to explore the hypothesis of HPR0 as progenitor of outbreaks. However, spatiotemporal patterns in HPRv occurrence were predictive of subsequent patterns in HPR0 detection, suggesting a parallel, or dependent, means of spread. Better data precision, breadth and consistency, common challenges for retrospective studies, could improve model fit; and, for HPR0, specification of diagnostic test accuracy would improve interpretation. Published by Elsevier B.V.

Using an agent-based model to evaluate the effect of producer specialization on the epidemiological resilience of livestock production networks.

PubMed

Wiltshire, Serge W

2018-01-01

An agent-based computer model that builds representative regional U.S. hog production networks was developed and employed to assess the potential impact of the ongoing trend towards increased producer specialization upon network-level resilience to catastrophic disease outbreaks. Empirical analyses suggest that the spatial distribution and connectivity patterns of contact networks often predict epidemic spreading dynamics. Our model heuristically generates realistic systems composed of hog producer, feed mill, and slaughter plant agents. Network edges are added during each run as agents exchange livestock and feed. The heuristics governing agents' contact patterns account for factors including their industry roles, physical proximities, and the age of their livestock. In each run, an infection is introduced, and may spread according to probabilities associated with the various modes of contact. For each of three treatments-defined by one-phase, two-phase, and three-phase production systems-a parameter variation experiment examines the impact of the spatial density of producer agents in the system upon the length and size of disease outbreaks. Resulting data show phase transitions whereby, above some density threshold, systemic outbreaks become possible, echoing findings from percolation theory. Data analysis reveals that multi-phase production systems are vulnerable to catastrophic outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outbreak scales and durations. Key differences in network-level metrics shed light on these results, suggesting that the absence of potentially-bridging producer-producer edges may be largely responsible for the superior disease resilience of single-phase "farrow to finish" production systems.

Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand

PubMed Central

Lauer, Stephen A.; Sakrejda, Krzysztof; Iamsirithaworn, Sopon; Hinjoy, Soawapak; Suangtho, Paphanij; Suthachana, Suthanun; Clapham, Hannah E.; Salje, Henrik; Cummings, Derek A. T.; Lessler, Justin

2016-01-01

Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making. PMID:27304062

Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand.

PubMed

Reich, Nicholas G; Lauer, Stephen A; Sakrejda, Krzysztof; Iamsirithaworn, Sopon; Hinjoy, Soawapak; Suangtho, Paphanij; Suthachana, Suthanun; Clapham, Hannah E; Salje, Henrik; Cummings, Derek A T; Lessler, Justin

2016-06-01

Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making.

A Qualitative Inquiry About Pruno, an Illicit Alcoholic Beverage Linked to Botulism Outbreaks in United States Prisons

PubMed Central

Sreenivasan, Nandini; Person, Bobbie; Shew, Mark; Wheeler, Daniel; Hall, Julia; Bogdanow, Linda; Leniek, Karyn; Rao, Agam

2015-01-01

Objectives. Since 2011, 3 outbreaks of botulism in US prisons have been attributed to pruno, which is an alcoholic beverage made by inmates. Following 1 outbreak, we conducted a qualitative inquiry to understand pruno brewing and its social context to inform outbreak prevention measures. Methods. We interviewed staff, inmates, and parolees from 1 prison about pruno production methods, the social aspects of pruno, and strategies for communicating the association between botulism and pruno. Results. Twenty-seven inmates and parolees and 13 staff completed interviews. Pruno is fermented from water, fruit, sugar, and miscellaneous ingredients. Knowledge of pruno making was widespread among inmates; staff were familiar with only the most common ingredients and supplies inmates described. Staff and inmates described inconsistent consequences for pruno possession and suggested using graphic health messages from organizations external to the prison to communicate the risk of botulism from pruno. Conclusions. Pruno making was frequent in this prison. Improved staff recognition of pruno ingredients and supplies might improve detection of brewing activities in this and other prisons. Consistent consequences and clear messages about the association between pruno and botulism might prevent outbreaks. PMID:26378846

DEFENDER: Detecting and Forecasting Epidemics Using Novel Data-Analytics for Enhanced Response.

PubMed

Thapen, Nicholas; Simmie, Donal; Hankin, Chris; Gillard, Joseph

2016-01-01

In recent years social and news media have increasingly been used to explain patterns in disease activity and progression. Social media data, principally from the Twitter network, has been shown to correlate well with official disease case counts. This fact has been exploited to provide advance warning of outbreak detection, forecasting of disease levels and the ability to predict the likelihood of individuals developing symptoms. In this paper we introduce DEFENDER, a software system that integrates data from social and news media and incorporates algorithms for outbreak detection, situational awareness and forecasting. As part of this system we have developed a technique for creating a location network for any country or region based purely on Twitter data. We also present a disease nowcasting (forecasting the current but still unknown level) approach which leverages counts from multiple symptoms, which was found to improve the nowcasting accuracy by 37 percent over a model that used only previous case data. Finally we attempt to forecast future levels of symptom activity based on observed user movement on Twitter, finding a moderate gain of 5 percent over a time series forecasting model.

Safety of community drinking-water and outbreaks of waterborne enteric disease: Israel, 1976-97.

PubMed Central

Tulchinsky, T. H.; Burla, E.; Clayman, M.; Sadik, C.; Brown, A.; Goldberger, S.

2000-01-01

Waterborne disease remains a major public health problem in many countries. We report findings on nearly three decades of waterborne disease in Israel and the part these diseases play in the total national burden of enteric disease. During the 1970s and 1980s, Israel's community water supplies were frequently of poor quality according to the microbiological standards at that time, and the country experienced many outbreaks of waterborne enteric disease. New regulations raised water quality standards and made chlorination of community water supplies mandatory, as well as imposing more stringent guidelines on maintaining water sources and distribution systems for both surface water and groundwater. This was followed by improved compliance and water quality, and a marked decline in the number of outbreaks of waterborne disease; no outbreaks were detected between 1992 and 1997. The incidence of waterborne salmonellosis, shigellosis, and typhoid declined markedly as proportions of the total burden of these diseases, but peaked during the time in which there were frequent outbreaks of waterborne disease (1980-85). Long-term trends in the total incidence of reported infectious enteric diseases from all sources, including typhoid, shigellosis, and viral hepatitis (all types) declined, while the total incidence of salmonellosis increased. Mandatory chlorination has had an important impact on improving water quality, in reducing outbreaks of waterborne disease in Israel, and reducing the total burden of enteric disease in the country. PMID:11196499

Novel microbiological and spatial statistical methods to improve strength of epidemiological evidence in a community-wide waterborne outbreak.

PubMed

Jalava, Katri; Rintala, Hanna; Ollgren, Jukka; Maunula, Leena; Gomez-Alvarez, Vicente; Revez, Joana; Palander, Marja; Antikainen, Jenni; Kauppinen, Ari; Räsänen, Pia; Siponen, Sallamaari; Nyholm, Outi; Kyyhkynen, Aino; Hakkarainen, Sirpa; Merentie, Juhani; Pärnänen, Martti; Loginov, Raisa; Ryu, Hodon; Kuusi, Markku; Siitonen, Anja; Miettinen, Ilkka; Santo Domingo, Jorge W; Hänninen, Marja-Liisa; Pitkänen, Tarja

2014-01-01

Failures in the drinking water distribution system cause gastrointestinal outbreaks with multiple pathogens. A water distribution pipe breakage caused a community-wide waterborne outbreak in Vuorela, Finland, July 2012. We investigated this outbreak with advanced epidemiological and microbiological methods. A total of 473/2931 inhabitants (16%) responded to a web-based questionnaire. Water and patient samples were subjected to analysis of multiple microbial targets, molecular typing and microbial community analysis. Spatial analysis on the water distribution network was done and we applied a spatial logistic regression model. The course of the illness was mild. Drinking untreated tap water from the defined outbreak area was significantly associated with illness (RR 5.6, 95% CI 1.9-16.4) increasing in a dose response manner. The closer a person lived to the water distribution breakage point, the higher the risk of becoming ill. Sapovirus, enterovirus, single Campylobacter jejuni and EHEC O157:H7 findings as well as virulence genes for EPEC, EAEC and EHEC pathogroups were detected by molecular or culture methods from the faecal samples of the patients. EPEC, EAEC and EHEC virulence genes and faecal indicator bacteria were also detected in water samples. Microbial community sequencing of contaminated tap water revealed abundance of Arcobacter species. The polyphasic approach improved the understanding of the source of the infections, and aided to define the extent and magnitude of this outbreak.

Impact of Global Climate on Rift Valley Fever and other Vector-borne Disease Outbreaks

NASA Astrophysics Data System (ADS)

Linthicum, K. J.

2017-12-01

Rift Valley fever is a viral disease of animals and humans in Africa and the Middle East that is transmitted by mosquitoes. Since the virus was first isolated in Kenya in 1930 it has caused significant impact to animal and human health and national economies, and it is of concern to the international agricultural and public health community. In this presentation we will describe the (1) ecology of disease transmission as it relates to climate, (2) the impact of climate and other environmental conditions on outbreaks, (3) the ability to use global climate information to predict outbreaks, (4) effective response activities, and (4) the potential to mitigate globalization.

The determinants of spread of Ebola virus disease - an evidence from the past outbreak experiences.

PubMed

Gałas, Aleksander

2014-01-01

The paper summarizes available evidence regarding the determinants of spread of Ebola virus disease, including health care and community related risk factors. It was observed that the level of uncertainty for the estimations is relatively high which may hinder to make some predictions for the future evolution of EVD outbreak. The natural history of EVD has shown that the disease may pose a problem to developed countries and may present a thread to individuals. Although observed modes of transmission mainly include direct contact and contaminated staff, high case fatality ratio and frequent contacts among individuals in developed countries are among determinants which may lead to the development of the EVD outbreak.

Causes of Outbreaks Associated with Drinking Water in the United States from 1971 to 2006

PubMed Central

Craun, Gunther F.; Brunkard, Joan M.; Yoder, Jonathan S.; Roberts, Virginia A.; Carpenter, Joe; Wade, Tim; Calderon, Rebecca L.; Roberts, Jacquelin M.; Beach, Michael J.; Roy, Sharon L.

2010-01-01

Summary: Since 1971, the CDC, EPA, and Council of State and Territorial Epidemiologists (CSTE) have maintained the collaborative national Waterborne Disease and Outbreak Surveillance System (WBDOSS) to document waterborne disease outbreaks (WBDOs) reported by local, state, and territorial health departments. WBDOs were recently reclassified to better characterize water system deficiencies and risk factors; data were analyzed for trends in outbreak occurrence, etiologies, and deficiencies during 1971 to 2006. A total of 833 WBDOs, 577,991 cases of illness, and 106 deaths were reported during 1971 to 2006. Trends of public health significance include (i) a decrease in the number of reported outbreaks over time and in the annual proportion of outbreaks reported in public water systems, (ii) an increase in the annual proportion of outbreaks reported in individual water systems and in the proportion of outbreaks associated with premise plumbing deficiencies in public water systems, (iii) no change in the annual proportion of outbreaks associated with distribution system deficiencies or the use of untreated and improperly treated groundwater in public water systems, and (iv) the increasing importance of Legionella since its inclusion in WBDOSS in 2001. Data from WBDOSS have helped inform public health and regulatory responses. Additional resources for waterborne disease surveillance and outbreak detection are essential to improve our ability to monitor, detect, and prevent waterborne disease in the United States. PMID:20610821

Prevalence of current patterns and predictive trends of multidrug-resistant Salmonella Typhi in Sudan.

PubMed

Elshayeb, Ayman A; Ahmed, Abdelazim A; El Siddig, Marmar A; El Hussien, Adil A

2017-11-14

Enteric fever has persistence of great impact in Sudanese public health especially during rainy season when the causative agent Salmonella enterica serovar Typhi possesses pan endemic patterns in most regions of Sudan - Khartoum. The present study aims to assess the recent state of antibiotics susceptibility of Salmonella Typhi with special concern to multidrug resistance strains and predict the emergence of new resistant patterns and outbreaks. Salmonella Typhi strains were isolated and identified according to the guidelines of the International Standardization Organization and the World Health Organization. The antibiotics susceptibilities were tested using the recommendations of the Clinical Laboratories Standards Institute. Predictions of emerging resistant bacteria patterns and outbreaks in Sudan were done using logistic regression, forecasting linear equations and in silico simulations models. A total of 124 antibiotics resistant Salmonella Typhi strains categorized in 12 average groups were isolated, different patterns of resistance statistically calculated by (y = ax - b). Minimum bactericidal concentration's predication of resistance was given the exponential trend (y = n e x ) and the predictive coefficient R 2  > 0 < 1 are approximately alike. It was assumed that resistant bacteria occurred with a constant rate of antibiotic doses during the whole experimental period. Thus, the number of sensitive bacteria decreases at the same rate as resistant occur following term to the modified predictive model which solved computationally. This study assesses the prediction of multi-drug resistance among S. Typhi isolates by applying low cost materials and simple statistical methods suitable for the most frequently used antibiotics as typhoid empirical therapy. Therefore, bacterial surveillance systems should be implemented to present data on the aetiology and current antimicrobial drug resistance patterns of community-acquired agents causing outbreaks.

Emerging issues, challenges, and changing epidemiology of fungal disease outbreaks.

PubMed

Benedict, Kaitlin; Richardson, Malcolm; Vallabhaneni, Snigdha; Jackson, Brendan R; Chiller, Tom

2017-12-01

Several high-profile outbreaks have drawn attention to invasive fungal infections (IFIs) as an increasingly important public health problem. IFI outbreaks are caused by many different fungal pathogens and are associated with numerous settings and sources. In the community, IFI outbreaks often occur among people without predisposing medical conditions and are frequently precipitated by environmental disruption. Health-care-associated IFI outbreaks have been linked to suboptimal hospital environmental conditions, transmission via health-care workers' hands, contaminated medical products, and transplantation of infected organs. Outbreak investigations provide important insights into the epidemiology of IFIs, uncover risk factors for infection, and identify opportunities for preventing similar events in the future. Well recognised challenges with IFI outbreak recognition, response, and prevention include the need for improved rapid diagnostic methods, the absence of routine surveillance for most IFIs, adherence to infection control practices, and health-care provider awareness. Additionally, IFI outbreak investigations have revealed several emerging issues, including new populations at risk because of travel or relocation, occupation, or immunosuppression; fungal pathogens appearing in geographical areas in which they have not been previously recognised; and contaminated compounded medications. This report highlights notable IFI outbreaks in the past decade, with an emphasis on these emerging challenges in the USA. Copyright © 2017 Elsevier Ltd. All rights reserved.

Upholding Tuberculosis Services during the 2014 Ebola Storm: An Encouraging Experience from Conakry, Guinea.

PubMed

Ortuno-Gutierrez, Nimer; Zachariah, Rony; Woldeyohannes, Desalegn; Bangoura, Adama; Chérif, Gba-Foromo; Loua, Francis; Hermans, Veerle; Tayler-Smith, Katie; Sikhondze, Welile; Camara, Lansana-Mady

2016-01-01

Ten targeted health facilities supported by Damien Foundation (a Belgian Non Governmental Organization) and the National Tuberculosis (TB) Program in Conakry, Guinea. To uphold TB program performance during the Ebola outbreak in the presence of a package of pre-emptive additional measures geared at reinforcing the routine TB program, and ensuring Ebola infection control, health-workers safety and motivation. A retrospective comparative cohort study of a TB program assessing the performance before (2013) and during the (2014) Ebola outbreak. During the Ebola outbreak, all health facilities were maintained opened, there were no reported health-worker Ebola infections, drug stockouts or health staff absences. Of 2,475 presumptive pulmonary TB cases, 13% were diagnosed with TB in both periods (160/1203 in 2013 and 163/1272 in 2014). For new TB, treatment success improved from 84% before to 87% during the Ebola outbreak (P = 0.03). Adjusted Hazard-ratios (AHR) for an unfavorable outcome was alwo lower during the Ebola outbreak, AHR = 0.8, 95% CI:0.7-0.9, P = 0.04). Treatment success improved for HIV co-infected patients (72% to 80%, P<0.01). For retreatment patients, the proportion achieving treatment success was maintained (68% to 72%, P = 0.05). Uptake of HIV-testing and Cotrimoxazole Preventive Treatment was maintained over 85%, and Anti-Retroviral Therapy uptake increased from 77% in 2013 to 86% in 2014 (P<0.01). Contingency planning and health system and worker support during the 2014 Ebola outbreak was associated with encouraging and sustained TB program performance. This is of relevance to future outbreaks.

Meterology-driven Prediction of RSV/RHV Incidence in Rural Nepal

PubMed Central

Scott, Anna; Englund, Janet; Chu, Helen; Tielsch, James; Tielsch, James; Khatry, Subarna; Leclerq, Steven C; Shrestha, Laxman; Kuypers, Jane; Steinhoff, Mark C; Katz, Joanne

2017-01-01

Abstract Background Incidence of respiratory syncytial virus (RSV) and rhinovirus (RHV) varies throughout the year. We aim to quantify the relationship between weather variables (temperature, humidity, precipitation, and aerosol concentration) and disease incidence in order to quantify how outbreaks of RSV and RHV are related to seasonal or sub-seasonal meteorology, and if these relationships can predict viral outbreaks of RSV and RHV. Methods Health data were collected in a community-based, prospective randomized trial of maternal influenza immunization of pregnant women and their infants conducted in rural Nepal from 2011–2014. Adult illness episodes were defined as fever plus cough, sore throat, runny nose, and/or myalgia, with infant illness defined similarly but without fever requirement. Cases were identified through longitudinal household-based weekly surveillance. Temperature, humidity, precipitation, and fine particulate matter (PM 2.5) data come from reanalysis data products NCEP, Era-Interim, and Merra-2, which are produced by assimilating historical in-situ and satellite-based observations into a weather model. Results RSV exhibits a relationship with temperature after removing the seasonal cycle (r = -0.16, N = 208, P = 0.02), and RHV exhibits a strong relationship to daily temperature (r =-0.14, N =208, P = 0.05). When lagging meteorology by up to 15 weeks, correlations with disease count and weather improve (RSV: r_max = 0.45, P < 0.05; RHV: r_max = 0.15, P = 0.05). We use an SIR model forced by lagged meteorological variables to predict RSV and RHV, suggesting that disease burden can be predicted at lead times of weeks to months. Conclusion Meteorological variables are associated with RSV and RHV incidence in rural Nepal and can be used to drive predictive models with a lead time of several months. Disclosures J. Englund, Gilead: Consultant and Investigator, Research support Chimerix: Investigator, Research support Alios: Investigator, Research support Novavax: Investigator, Research support MedImmune: Investigator, Research support GlaxoSmithKline: Investigator, Research support

A broad assessment of factors determining Culicoides imicola abundance: modelling the present and forecasting its future in climate change scenarios.

PubMed

Acevedo, Pelayo; Ruiz-Fons, Francisco; Estrada, Rosa; Márquez, Ana Luz; Miranda, Miguel Angel; Gortázar, Christian; Lucientes, Javier

2010-12-06

Bluetongue (BT) is still present in Europe and the introduction of new serotypes from endemic areas in the African continent is a possible threat. Culicoides imicola remains one of the most relevant BT vectors in Spain and research on the environmental determinants driving its life cycle is key to preventing and controlling BT. Our aim was to improve our understanding of the biotic and abiotic determinants of C. imicola by modelling its present abundance, studying the spatial pattern of predicted abundance in relation to BT outbreaks, and investigating how the predicted current distribution and abundance patterns might change under future (2011-2040) scenarios of climate change according to the Intergovernmental Panel on Climate Change. C. imicola abundance data from the bluetongue national surveillance programme were modelled with spatial, topoclimatic, host and soil factors. The influence of these factors was further assessed by variation partitioning procedures. The predicted abundance of C. imicola was also projected to a future period. Variation partitioning demonstrated that the pure effect of host and topoclimate factors explained a high percentage (>80%) of the variation. The pure effect of soil followed in importance in explaining the abundance of C. imicola. A close link was confirmed between C. imicola abundance and BT outbreaks. To the best of our knowledge, this study is the first to consider wild and domestic hosts in predictive modelling for an arthropod vector. The main findings regarding the near future show that there is no evidence to suggest that there will be an important increase in the distribution range of C. imicola; this contrasts with an expected increase in abundance in the areas where it is already present in mainland Spain. What may be expected regarding the future scenario for orbiviruses in mainland Spain, is that higher predicted C. imicola abundance may significantly change the rate of transmission of orbiviruses.

PREDICT: A next generation platform for near real-time prediction of cholera

NASA Astrophysics Data System (ADS)

Jutla, A.; Aziz, S.; Akanda, A. S.; Alam, M.; Ahsan, G. U.; Huq, A.; Colwell, R. R.

2017-12-01

Data on disease prevalence and infectious pathogens is sparingly collected/available in region(s) where climatic variability and extreme natural events intersect with population vulnerability (such as lack of access to water and sanitation infrastructure). Therefore, traditional time series modeling approach of calibration and validation of a model is inadequate. Hence, prediction of diarrheal infections (such as cholera, Shigella etc) remain a challenge even though disease causing pathogens are strongly associated with modalities of regional climate and weather system. Here we present an algorithm that integrates satellite derived data on several hydroclimatic and ecological processes into a framework that can determine high resolution cholera risk on global scales. Cholera outbreaks can be classified in three forms- epidemic (sudden or seasonal outbreaks), endemic (recurrence and persistence of the disease for several consecutive years) and mixed-mode endemic (combination of certain epidemic and endemic conditions) with significant spatial and temporal heterogeneity. Using data from multiple satellites (AVHRR, TRMM, GPM, MODIS, VIIRS, GRACE), we will show examples from Haiti, Yemen, Nepal and several other regions where our algorithm has been successful in capturing risk of outbreak of infection in human population. A spatial model validation algorithm will also be presented that has capabilities to self-calibrate as new hydroclimatic and disease data become available.

Identifying Future Disease Hot Spots: Infectious Disease Vulnerability Index.

PubMed

Moore, Melinda; Gelfeld, Bill; Okunogbe, Adeyemi; Paul, Christopher

2017-06-01

Recent high-profile outbreaks, such as Ebola and Zika, have illustrated the transnational nature of infectious diseases. Countries that are most vulnerable to such outbreaks might be higher priorities for technical support. RAND created the Infectious Disease Vulnerability Index to help U.S. government and international agencies identify these countries and thereby inform programming to preemptively help mitigate the spread and effects of potential transnational outbreaks. The authors employed a rigorous methodology to identify the countries most vulnerable to disease outbreaks. They conducted a comprehensive review of relevant literature to identify factors influencing infectious disease vulnerability. Using widely available data, the authors created an index for identifying potentially vulnerable countries and then ranked countries by overall vulnerability score. Policymakers should focus on the 25 most-vulnerable countries with an eye toward a potential "disease belt" in the Sahel region of Africa. The infectious disease vulnerability scores for several countries were better than what would have been predicted on the basis of economic status alone. This suggests that low-income countries can overcome economic challenges and become more resilient to public health challenges, such as infectious disease outbreaks.

Detection and forecasting of oyster norovirus outbreaks: recent advances and future perspectives.

PubMed

Wang, Jiao; Deng, Zhiqiang

2012-09-01

Norovirus is a highly infectious pathogen that is commonly found in oysters growing in fecally contaminated waters. Norovirus outbreaks can cause the closure of oyster harvesting waters and acute gastroenteritis in humans associated with consumption of contaminated raw oysters. Extensive efforts and progresses have been made in detection and forecasting of oyster norovirus outbreaks over the past decades. The main objective of this paper is to provide a literature review of methods and techniques for detecting and forecasting oyster norovirus outbreaks and thereby to identify the future directions for improving the detection and forecasting of norovirus outbreaks. It is found that (1) norovirus outbreaks display strong seasonality with the outbreak peak occurring commonly in December-March in the U.S. and April-May in the Europe; (2) norovirus outbreaks are affected by multiple environmental factors, including but not limited to precipitation, temperature, solar radiation, wind, and salinity; (3) various modeling approaches may be employed to forecast norovirus outbreaks, including Bayesian models, regression models, Artificial Neural Networks, and process-based models; and (4) diverse techniques are available for near real-time detection of norovirus outbreaks, including multiplex PCR, seminested PCR, real-time PCR, quantitative PCR, and satellite remote sensing. The findings are important to the management of oyster growing waters and to future investigations into norovirus outbreaks. It is recommended that a combined approach of sensor-assisted real time monitoring and modeling-based forecasting should be utilized for an efficient and effective detection and forecasting of norovirus outbreaks caused by consumption of contaminated oysters. Copyright © 2012 Elsevier Ltd. All rights reserved.

Shigellosis in the Marshall Islands: epidemiologic aspects of an outbreak.

PubMed

Storch, G A; Gunn, R A; Martin, W T; Pollard, R A; Sinclair, S P

1980-05-01

An outbreak of diarrheal illness caused by Shigella flexneri 1b and associated with 11 deaths occurred in the Marshall Islands during June and July 1977. A house-to-house survey on Majuro indicated an attack rate of 6.2%. Neither the survey nor a case-control study uncovered a common source of exposure on Majuro, and it is believed that transmission was mainly person-to-person. Socioeconomic factors, type of water supply, distance to municipal water supply, and type of sanitary facility could not be related to the occurrence of illness, but a poor sanitary rating was associated with increased rate of household transmission. Absence of stool culturing for bacteria and false-positive identifications of amebae in stool specimens led to the outbreak's being attributed to Entamoeba histolytica. Subsequent parasitologic examinations and serologic studies indicated that E. histolytica played no role in the outbreak, and suggested that fecal leukocytes were being mistaken for amebae. Improved bacteriologic capabilities will be important in improving the approach to diarrheal illness in the Marshall Islands.

Earth: Earth Science and Health

NASA Technical Reports Server (NTRS)

Maynard, Nancy G.

2001-01-01

A major new NASA initiative on environmental change and health has been established to promote the application of Earth science remote sensing data, information, observations, and technologies to issues of human health. NASA's Earth Sciences suite of Earth observing instruments are now providing improved observations science, data, and advanced technologies about the Earth's land, atmosphere, and oceans. These new space-based resources are being combined with other agency and university resources, data integration and fusion technologies, geographic information systems (GIS), and the spectrum of tools available from the public health community, making it possible to better understand how the environment and climate are linked to specific diseases, to improve outbreak prediction, and to minimize disease risk. This presentation is an overview of NASA's tools, capabilities, and research advances in this initiative.

A past Haff disease outbreak associated with eating freshwater pomfret in South China

PubMed Central

2013-01-01

Background Haff disease is unexplained rhabdomyolysis caused by consumption of fishery products in the previous 24 h. It was first identified in Europe in 1924 but the condition is extremely rare in China. Here we describe a past outbreak of acute food borne muscle poisoning that occurred in Guangdong Province (South China) in 2009. Methods The first full outbreak of Haff disease reported in Jiangsu Province (East China) in 2010, indicated that the incidence of the disease may be increasing in China. We, therefore first retrospectively reviewed epidemiologic, trace-back, environmental studies, and laboratory analyses, including oral toxicity testing to ascertain risk and chemical analysis to identify toxin(s), from the 2009 Guangdong outbreak. Then we compared data from the 2009 outbreak with data from all other Haff disease outbreaks that were available. Results Clinical symptoms and laboratory findings indicated that the 2009 Guangdong outbreak disease was consistent with rhabdomyolysis. Epidemiologic, trace-back, environmental studies and laboratory analyses implied that the disease was caused by freshwater Pomfrets consumed prior to the onset of symptoms. We also identified common factors between the 2009 Guangdong outbreak and previous Haff disease outbreaks reported around the world, while as with other similar outbreaks, the exact etiological factor(s) of the disease remains unknown. Conclusions The 2009 Guangdong outbreak of ‘muscle poisoning’ was retrospectively identified as an outbreak of Haff disease. This comprised the highest number of cases reported in China thus far. Food borne diseases emerging in this unusual form and the irregular pattern of outbreaks present an ongoing public health risk, highlighting the need for improved surveillance and diagnostic methodology. PMID:23642345

Predictability of gypsy moth defoliation in central hardwoods: a validation study

Treesearch

David E. Fosbroke; Ray R., Jr. Hicks

1993-01-01

A model for predicting gypsy moth defoliation in central hardwood forests based on stand characteristics was evaluated following a 5-year outbreak in Pennsylvania and Maryland. Study area stand characteristics were similar to those of the areas used to develop the model. Comparisons are made between model predictive capability in two physiographic provinces. The tested...

Spatial-temporal analysis of the of the risk of Rift Valley Fever in Kenya

NASA Astrophysics Data System (ADS)

Bett, B.; Omolo, A.; Hansen, F.; Notenbaert, A.; Kemp, S.

2012-04-01

Historical data on Rift Valley Fever (RVF) outbreaks in Kenya covering the period 1951 - 2010 were analyzed using a logistic regression model to identify factors associated with RVF occurrence. The analysis used a division, an administrative unit below a district, as the unit of analysis. The infection status of each division was defined on a monthly time scale and used as a dependent variable. Predictors investigated include: monthly precipitation (minimum, maximum and total), normalized difference vegetation index, altitude, agro-ecological zone, presence of game, livestock and human population densities, the number of times a division has had an outbreak before and time interval in months between successive outbreaks (used as a proxy for immunity). Both univariable and multivariable analyses were conducted. The models used incorporated an auto-regressive correlation matrix to account for clustering of observations in time, while dummy variables were fitted in the multivariable model to account for spatial relatedness/topology between divisions. This last procedure was followed because it is expected that the risk of RVF occurring in a given division increases when its immediate neighbor gets infected. Functional relationships between the continuous and the outcome variables were assessed to ensure that the linearity assumption was met. Deviance and leverage residuals were also generated from the final model and used for evaluating the goodness of fit of the model. Descriptive analyzes indicate that a total of 91 divisions in 42 districts (of the original 69 districts in place by 1999) reported RVF outbreaks at least once over the period. The mean interval between outbreaks was determined to be about 43 months. Factors that were positively associated with RVF occurrence include increased precipitation, high outbreak interval and the number of times a division has been infected or reported an outbreak. The model will be validated and used for developing an RVF forecasting system. This forecasting system can then be used with the existing regional RVF prediction tools such as EMPRES-i to downscale RVF risk predictions to country-specific scales and subsequently link them with decision support systems. The ultimate aim is to increase the capacity of the national institutions to formulate appropriate RVF mitigation measures.

Effects of local and regional climatic fluctuations on dengue outbreaks in southern Taiwan

PubMed Central

Chaves, Luis Fernando; Chen, Po-Jiang

2017-01-01

Background Southern Taiwan has been a hotspot for dengue fever transmission since 1998. During 2014 and 2015, Taiwan experienced unprecedented dengue outbreaks and the causes are poorly understood. This study aims to investigate the influence of regional and local climate conditions on the incidence of dengue fever in Taiwan, as well as to develop a climate-based model for future forecasting. Methodology/Principle findings Historical time-series data on dengue outbreaks in southern Taiwan from 1998 to 2015 were investigated. Local climate variables were analyzed using a distributed lag non-linear model (DLNM), and the model of best fit was used to predict dengue incidence between 2013 and 2015. The cross-wavelet coherence approach was used to evaluate the regional El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) effects on dengue incidence and local climate variables. The DLNM results highlighted the important non-linear and lag effects of minimum temperature and precipitation. Minimum temperature above 23°C or below 17°C can increase dengue incidence rate with lag effects of 10 to 15 weeks. Moderate to high precipitation can increase dengue incidence rates with a lag of 10 or 20 weeks. The model of best fit successfully predicted dengue transmission between 2013 and 2015. The prediction accuracy ranged from 0.7 to 0.9, depending on the number of weeks ahead of the prediction. ENSO and IOD were associated with nonstationary inter-annual patterns of dengue transmission. IOD had a greater impact on the seasonality of local climate conditions. Conclusions/Significance Our findings suggest that dengue transmission can be affected by regional and local climatic fluctuations in southern Taiwan. The climate-based model developed in this study can provide important information for dengue early warning systems in Taiwan. Local climate conditions might be influenced by ENSO and IOD, to result in unusual dengue outbreaks. PMID:28575035

Modeling emergency department visit patterns for infectious disease complaints: results and application to disease surveillance

PubMed Central

Brillman, Judith C; Burr, Tom; Forslund, David; Joyce, Edward; Picard, Rick; Umland, Edith

2005-01-01

Background Concern over bio-terrorism has led to recognition that traditional public health surveillance for specific conditions is unlikely to provide timely indication of some disease outbreaks, either naturally occurring or induced by a bioweapon. In non-traditional surveillance, the use of health care resources are monitored in "near real" time for the first signs of an outbreak, such as increases in emergency department (ED) visits for respiratory, gastrointestinal or neurological chief complaints (CC). Methods We collected ED CCs from 2/1/94 – 5/31/02 as a training set. A first-order model was developed for each of seven CC categories by accounting for long-term, day-of-week, and seasonal effects. We assessed predictive performance on subsequent data from 6/1/02 – 5/31/03, compared CC counts to predictions and confidence limits, and identified anomalies (simulated and real). Results Each CC category exhibited significant day-of-week differences. For most categories, counts peaked on Monday. There were seasonal cycles in both respiratory and undifferentiated infection complaints and the season-to-season variability in peak date was summarized using a hierarchical model. For example, the average peak date for respiratory complaints was January 22, with a season-to-season standard deviation of 12 days. This season-to-season variation makes it challenging to predict respiratory CCs so we focused our effort and discussion on prediction performance for this difficult category. Total ED visits increased over the study period by 4%, but respiratory complaints decreased by roughly 20%, illustrating that long-term averages in the data set need not reflect future behavior in data subsets. Conclusion We found that ED CCs provided timely indicators for outbreaks. Our approach led to successful identification of a respiratory outbreak one-to-two weeks in advance of reports from the state-wide sentinel flu surveillance and of a reported increase in positive laboratory test results. PMID:15743535

Effects of local and regional climatic fluctuations on dengue outbreaks in southern Taiwan.

PubMed

Chuang, Ting-Wu; Chaves, Luis Fernando; Chen, Po-Jiang

2017-01-01

Southern Taiwan has been a hotspot for dengue fever transmission since 1998. During 2014 and 2015, Taiwan experienced unprecedented dengue outbreaks and the causes are poorly understood. This study aims to investigate the influence of regional and local climate conditions on the incidence of dengue fever in Taiwan, as well as to develop a climate-based model for future forecasting. Historical time-series data on dengue outbreaks in southern Taiwan from 1998 to 2015 were investigated. Local climate variables were analyzed using a distributed lag non-linear model (DLNM), and the model of best fit was used to predict dengue incidence between 2013 and 2015. The cross-wavelet coherence approach was used to evaluate the regional El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) effects on dengue incidence and local climate variables. The DLNM results highlighted the important non-linear and lag effects of minimum temperature and precipitation. Minimum temperature above 23°C or below 17°C can increase dengue incidence rate with lag effects of 10 to 15 weeks. Moderate to high precipitation can increase dengue incidence rates with a lag of 10 or 20 weeks. The model of best fit successfully predicted dengue transmission between 2013 and 2015. The prediction accuracy ranged from 0.7 to 0.9, depending on the number of weeks ahead of the prediction. ENSO and IOD were associated with nonstationary inter-annual patterns of dengue transmission. IOD had a greater impact on the seasonality of local climate conditions. Our findings suggest that dengue transmission can be affected by regional and local climatic fluctuations in southern Taiwan. The climate-based model developed in this study can provide important information for dengue early warning systems in Taiwan. Local climate conditions might be influenced by ENSO and IOD, to result in unusual dengue outbreaks.

Prediction of Peaks of Seasonal Influenza in Military Health-Care Data

PubMed Central

Buczak, Anna L.; Baugher, Benjamin; Guven, Erhan; Moniz, Linda; Babin, Steven M.; Chretien, Jean-Paul

2016-01-01

Influenza is a highly contagious disease that causes seasonal epidemics with significant morbidity and mortality. The ability to predict influenza peak several weeks in advance would allow for timely preventive public health planning and interventions to be used to mitigate these outbreaks. Because influenza may also impact the operational readiness of active duty personnel, the US military places a high priority on surveillance and preparedness for seasonal outbreaks. A method for creating models for predicting peak influenza visits per total health-care visits (ie, activity) weeks in advance has been developed using advanced data mining techniques on disparate epidemiological and environmental data. The model results are presented and compared with those of other popular data mining classifiers. By rigorously testing the model on data not used in its development, it is shown that this technique can predict the week of highest influenza activity for a specific region with overall better accuracy than other methods examined in this article. PMID:27127415

Geographic potential of disease caused by Ebola and Marburg viruses in Africa.

PubMed

Peterson, A Townsend; Samy, Abdallah M

2016-10-01

Filoviruses represent a significant public health threat worldwide. West Africa recently experienced the largest-scale and most complex filovirus outbreak yet known, which underlines the need for a predictive understanding of the geographic distribution and potential for transmission to humans of these viruses. Here, we used ecological niche modeling techniques to understand the relationship between known filovirus occurrences and environmental characteristics. Our study derived a picture of the potential transmission geography of Ebola virus species and Marburg, paired with views of the spatial uncertainty associated with model-to-model variation in our predictions. We found that filovirus species have diverged ecologically, but only three species are sufficiently well known that models could be developed with significant predictive power. We quantified uncertainty in predictions, assessed potential for outbreaks outside of known transmission areas, and highlighted the Ethiopian Highlands and scattered areas across East Africa as additional potentially unrecognized transmission areas. Copyright © 2016 Elsevier B.V. All rights reserved.

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

NASA Technical Reports Server (NTRS)

Duda, David P.; Minnis, Patrick

2009-01-01

Previous studies have shown that probabilistic forecasting may be a useful method for predicting persistent contrail formation. A probabilistic forecast to accurately predict contrail formation over the contiguous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and from the Rapid Update Cycle (RUC) as well as GOES water vapor channel measurements, combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The mean accuracies for both the SURFACE and OUTBREAK models typically exceeded 75 percent when based on the RUC or ARPS analysis data, but decreased when the logistic models were derived from ARPS forecast data.

The use of ambient humidity conditions to improve influenza forecast.

PubMed

Shaman, Jeffrey; Kandula, Sasikiran; Yang, Wan; Karspeck, Alicia

2017-11-01

Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance (at 1-4 lead weeks, 3.8% more peak week and 4.4% more peak intensity forecasts are accurate than with no forcing) and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing (4.4% and 2.6% respectively). These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast.

The use of ambient humidity conditions to improve influenza forecast

PubMed Central

Kandula, Sasikiran; Karspeck, Alicia

2017-01-01

Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance (at 1–4 lead weeks, 3.8% more peak week and 4.4% more peak intensity forecasts are accurate than with no forcing) and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing (4.4% and 2.6% respectively). These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast. PMID:29145389

Predator crown-of-thorns starfish (Acanthaster planci) outbreak, mass mortality of corals, and cascading effects on reef fish and benthic communities.

PubMed

Kayal, Mohsen; Vercelloni, Julie; Lison de Loma, Thierry; Bosserelle, Pauline; Chancerelle, Yannick; Geoffroy, Sylvie; Stievenart, Céline; Michonneau, François; Penin, Lucie; Planes, Serge; Adjeroud, Mehdi

2012-01-01

Outbreaks of the coral-killing seastar Acanthaster planci are intense disturbances that can decimate coral reefs. These events consist of the emergence of large swarms of the predatory seastar that feed on reef-building corals, often leading to widespread devastation of coral populations. While cyclic occurrences of such outbreaks are reported from many tropical reefs throughout the Indo-Pacific, their causes are hotly debated, and the spatio-temporal dynamics of the outbreaks and impacts to reef communities remain unclear. Based on observations of a recent event around the island of Moorea, French Polynesia, we show that Acanthaster outbreaks are methodic, slow-paced, and diffusive biological disturbances. Acanthaster outbreaks on insular reef systems like Moorea's appear to originate from restricted areas confined to the ocean-exposed base of reefs. Elevated Acanthaster densities then progressively spread to adjacent and shallower locations by migrations of seastars in aggregative waves that eventually affect the entire reef system. The directional migration across reefs appears to be a search for prey as reef portions affected by dense seastar aggregations are rapidly depleted of living corals and subsequently left behind. Coral decline on impacted reefs occurs by the sequential consumption of species in the order of Acanthaster feeding preferences. Acanthaster outbreaks thus result in predictable alteration of the coral community structure. The outbreak we report here is among the most intense and devastating ever reported. Using a hierarchical, multi-scale approach, we also show how sessile benthic communities and resident coral-feeding fish assemblages were subsequently affected by the decline of corals. By elucidating the processes involved in an Acanthaster outbreak, our study contributes to comprehending this widespread disturbance and should thus benefit targeted management actions for coral reef ecosystems.

A Predictive Risk Model for A(H7N9) Human Infections Based on Spatial-Temporal Autocorrelation and Risk Factors: China, 2013–2014

PubMed Central

Dong, Wen; Yang, Kun; Xu, Quan-Li; Yang, Yu-Lian

2015-01-01

This study investigated the spatial distribution, spatial autocorrelation, temporal cluster, spatial-temporal autocorrelation and probable risk factors of H7N9 outbreaks in humans from March 2013 to December 2014 in China. The results showed that the epidemic spread with significant spatial-temporal autocorrelation. In order to describe the spatial-temporal autocorrelation of H7N9, an improved model was developed by introducing a spatial-temporal factor in this paper. Logistic regression analyses were utilized to investigate the risk factors associated with their distribution, and nine risk factors were significantly associated with the occurrence of A(H7N9) human infections: the spatial-temporal factor φ (OR = 2546669.382, p < 0.001), migration route (OR = 0.993, p < 0.01), river (OR = 0.861, p < 0.001), lake(OR = 0.992, p < 0.001), road (OR = 0.906, p < 0.001), railway (OR = 0.980, p < 0.001), temperature (OR = 1.170, p < 0.01), precipitation (OR = 0.615, p < 0.001) and relative humidity (OR = 1.337, p < 0.001). The improved model obtained a better prediction performance and a higher fitting accuracy than the traditional model: in the improved model 90.1% (91/101) of the cases during February 2014 occurred in the high risk areas (the predictive risk > 0.70) of the predictive risk map, whereas 44.6% (45/101) of which overlaid on the high risk areas (the predictive risk > 0.70) for the traditional model, and the fitting accuracy of the improved model was 91.6% which was superior to the traditional model (86.1%). The predictive risk map generated based on the improved model revealed that the east and southeast of China were the high risk areas of A(H7N9) human infections in February 2014. These results provided baseline data for the control and prevention of future human infections. PMID:26633446

Outbreaks Attributed to Cheese: Differences Between Outbreaks Caused by Unpasteurized and Pasteurized Dairy Products, United States, 1998–2011

PubMed Central

Gould, L. Hannah; Mungai, Elisabeth; Behravesh, Casey Barton

2015-01-01

Introduction The interstate commerce of unpasteurized fluid milk, also known as raw milk, is illegal in the United States, and intrastate sales are regulated independently by each state. However, U.S. Food and Drug Administration regulations allow the interstate sale of certain types of cheeses made from unpasteurized milk if specific aging requirements are met. We describe characteristics of these outbreaks, including differences between outbreaks linked to cheese made from pasteurized or unpasteurized milk. Methods We reviewed reports of outbreaks submitted to the Foodborne Disease Outbreak Surveillance System during 1998–2011 in which cheese was implicated as the vehicle. We describe characteristics of these outbreaks, including differences between outbreaks linked to cheese made from pasteurized versus unpasteurized milk. Results During 1998–2011, 90 outbreaks attributed to cheese were reported; 38 (42%) were due to cheese made with unpasteurized milk, 44 (49%) to cheese made with pasteurized milk, and the pasteurization status was not reported for the other eight (9%). The most common cheese–pathogen pairs were unpasteurized queso fresco or other Mexican-style cheese and Salmonella (10 outbreaks), and pasteurized queso fresco or other Mexican-style cheese and Listeria (6 outbreaks). The cheese was imported from Mexico in 38% of outbreaks caused by cheese made with unpasteurized milk. In at least five outbreaks, all due to cheese made from unpasteurized milk, the outbreak report noted that the cheese was produced or sold illegally. Outbreaks caused by cheese made from pasteurized milk occurred most commonly (64%) in restaurant, delis, or banquet settings where cross-contamination was the most common contributing factor. Conclusions In addition to using pasteurized milk to make cheese, interventions to improve the safety of cheese include limiting illegal importation of cheese, strict sanitation and microbiologic monitoring in cheese-making facilities, and controls to limit food worker contamination. PMID:24750119

Ebolavirus diagnosis made simple, comparable and faster than molecular detection methods: preparing for the future.

PubMed

James, Ameh S; Todd, Shawn; Pollak, Nina M; Marsh, Glenn A; Macdonald, Joanne

2018-04-23

The 2014/2015 Ebolavirus outbreak resulted in more than 28,000 cases and 11,323 reported deaths, as of March 2016. Domestic transmission of the Guinea strain associated with the outbreak occurred mainly in six African countries, and international transmission was reported in four countries. Outbreak management was limited by the inability to rapidly diagnose infected cases. A further fifteen countries in Africa are predicted to be at risk of Ebolavirus outbreaks in the future as a consequence of climate change and urbanization. Early detection of cases and reduction of transmission rates is critical to prevent and manage future severe outbreaks. We designed a rapid assay for detection of Ebolavirus using recombinase polymerase amplification, a rapid isothermal amplification technology that can be combined with portable lateral flow detection technology. The developed rapid assay operates in 30 min and was comparable with real-time TaqMan™ PCR. Designed, screened, selected and optimized oligonucleotides using the NP coding region from Ebola Zaire virus (Guinea strain). We determined the analytical sensitivity of our Ebola rapid molecular test by testing selected primers and probe with tenfold serial dilutions (1.34 × 10 10-  1.34 × 10 1 copies/μL) of cloned NP gene from Mayinga strain of Zaire ebolavirus in pCAGGS vector, and serially diluted cultured Ebolavirus as established by real-time TaqMan™ PCR that was performed using ABI7500 in Fast Mode. We tested extracted and reverse transcribed RNA from cultured Zaire ebolavirus strains - Mayinga, Gueckedou C05, Gueckedou C07, Makona, Kissidougou and Kiwit. We determined the analytical specificity of our assay with related viruses: Marburg, Ebola Reston and Ebola Sudan. We further tested for Dengue virus 1-4, Plasmodium falciparum and West Nile Virus (Kunjin strain). The assay had a detection limit of 134 copies per μL of plasmid containing the NP gene of Ebolavirus Mayinga, and cultured Ebolavirus and was highly specific for the Zaire ebolavirus species, including the Guinea strain responsible for the 2014/2015 outbreak. The assay did not detect related viruses like Marburg, Reston, or Sudan viruses, and other pathogens likely to be isolated from clinical samples. Our assay could be suitable for implementation in district and primary health laboratories, as only a heating block and centrifuge is required for operation. The technique could provide a pathway for rapid screening of patients and animals for improved management of outbreaks.

Multi-step prediction for influenza outbreak by an adjusted long short-term memory.

PubMed

Zhang, J; Nawata, K

2018-05-01

Influenza results in approximately 3-5 million annual cases of severe illness and 250 000-500 000 deaths. We urgently need an accurate multi-step-ahead time-series forecasting model to help hospitals to perform dynamical assignments of beds to influenza patients for the annually varied influenza season, and aid pharmaceutical companies to formulate a flexible plan of manufacturing vaccine for the yearly different influenza vaccine. In this study, we utilised four different multi-step prediction algorithms in the long short-term memory (LSTM). The result showed that implementing multiple single-output prediction in a six-layer LSTM structure achieved the best accuracy. The mean absolute percentage errors from two- to 13-step-ahead prediction for the US influenza-like illness rates were all <15%, averagely 12.930%. To the best of our knowledge, it is the first time that LSTM has been applied and refined to perform multi-step-ahead prediction for influenza outbreaks. Hopefully, this modelling methodology can be applied in other countries and therefore help prevent and control influenza worldwide.

Forest fuels and predicted fire behavior in the first 5 years after a bark beetle outbreak with and without timber harvest (Project INT-EM-F-11-04) [Chapter 12

Treesearch

Carolyn Sieg; Kurt Allen; Chad Hoffman; Joel McMillin

2016-01-01

Unprecedented levels of tree mortality from native bark beetle species have occurred in a variety of forest types in Western United States and Canada in recent decades in response to beetle-favorable forest and climatic conditions (Bentz 2009, Meddens and others 2012). Previous studies suggest that bark beetle outbreaks alter stand structural attributes and fuel...

Hydroclimatological Controls of Endemic and Non-endemic Cholera of the 20th Century

NASA Astrophysics Data System (ADS)

Jutla, A. S.; Whitcombe, E.; Colwell, R.

2012-12-01

Cholera remains a major public health threat for the developing countries. Since the causative agent, Vibrio cholerae, is autochthonous to aquatic environment, it is not possible to eradicate the agent of the disease. Hydroclimatology based prediction and prevention strategies can be implemented in disease susceptible regions for reducing incidence rates. However, the precise role of hydrological and climatological processes, which will further aid in development of suitable prediction models, in creating spatial and temporal environmental conditions favorable for disease outbreak has not been adequately quantified. Here, we show distinction between seasonality and occurrence of cholera in epidemic and non-endemic regions. Using historical cholera mortality data, from the late 1800s for 27 locations in the Indian subcontinent, we show that non-endemic regions are generally located close to regional river systems but away from the coasts and are characterized by single sporadic outbreak in a given year. Increase in air temperature during the low river flow season increases evaporation, leading to an optimal salinity and pH required for bacterial growth. Thereafter, monsoonal rainfall, leads to interactions of contaminated river waters via human activity resulting in cholera epidemics. Endemic regions are located close to coasts where cholera outbreak occurs twice (spring and fall) in a year. Spring outbreak is generally associated with intrusion of bacterial seawater to inland whereas the fall peak is correlated with widespread flooding and cross-contamination of water resources via increased precipitation. This may be one of the first studies to hydroclimatologically quantitatively the seasonality of cholera in both endemic and non-endemic regions. Our results prompt the need of region and cause-specific prediction models for cholera, employing appropriate environmental determinants.

Effect of Climatic Factors and Population Density on the Distribution of Dengue in Sri Lanka: A GIS Based Evaluation for Prediction of Outbreaks.

PubMed

Sirisena, Pdnn; Noordeen, Faseeha; Kurukulasuriya, Harithra; Romesh, Thanuja Alar; Fernando, LakKumar

2017-01-01

Dengue is one of the major hurdles to the public health in Sri Lanka, causing high morbidity and mortality. The present study focuses on the use of geographical information systems (GIS) to map and evaluate the spatial and temporal distribution of dengue in Sri Lanka from 2009 to 2014 and to elucidate the association of climatic factors with dengue incidence. Epidemiological, population and meteorological data were collected from the Epidemiology Unit, Department of Census and Statistics and the Department of Meteorology of Sri Lanka. Data were analyzed using SPSS (Version 20, 2011) and R studio (2012) and the maps were generated using Arc GIS 10.2. The dengue incidence showed a significant positive correlation with rainfall (p<0.0001). No positive correlation was observed between dengue incidence and temperature (p = 0.107) or humidity (p = 0.084). Rainfall prior to 2 and 5 months and a rise in the temperature prior to 9 months positively correlated with dengue incidence as based on the auto-correlation values. A rise in humidity prior to 1 month had a mild positive correlation with dengue incidence. However, a rise in humidity prior to 9 months had a significant negative correlation with dengue incidence based on the auto-correlation values. Remote sensing and GIS technologies give near real time utility of climatic data together with the past dengue incidence for the prediction of dengue outbreaks. In that regard, GIS will be applicable in outbreak predictions including prompt identification of locations with dengue incidence and forecasting future risks and thus direct control measures to minimize major outbreaks.

Effect of Climatic Factors and Population Density on the Distribution of Dengue in Sri Lanka: A GIS Based Evaluation for Prediction of Outbreaks

PubMed Central

Sirisena, PDNN; Noordeen, Faseeha; Kurukulasuriya, Harithra; Romesh, Thanuja ALAR; Fernando, LakKumar

2017-01-01

Dengue is one of the major hurdles to the public health in Sri Lanka, causing high morbidity and mortality. The present study focuses on the use of geographical information systems (GIS) to map and evaluate the spatial and temporal distribution of dengue in Sri Lanka from 2009 to 2014 and to elucidate the association of climatic factors with dengue incidence. Epidemiological, population and meteorological data were collected from the Epidemiology Unit, Department of Census and Statistics and the Department of Meteorology of Sri Lanka. Data were analyzed using SPSS (Version 20, 2011) and R studio (2012) and the maps were generated using Arc GIS 10.2. The dengue incidence showed a significant positive correlation with rainfall (p<0.0001). No positive correlation was observed between dengue incidence and temperature (p = 0.107) or humidity (p = 0.084). Rainfall prior to 2 and 5 months and a rise in the temperature prior to 9 months positively correlated with dengue incidence as based on the auto-correlation values. A rise in humidity prior to 1 month had a mild positive correlation with dengue incidence. However, a rise in humidity prior to 9 months had a significant negative correlation with dengue incidence based on the auto-correlation values. Remote sensing and GIS technologies give near real time utility of climatic data together with the past dengue incidence for the prediction of dengue outbreaks. In that regard, GIS will be applicable in outbreak predictions including prompt identification of locations with dengue incidence and forecasting future risks and thus direct control measures to minimize major outbreaks. PMID:28068339

Quantitative assessment of the microbial risk of leafy greens from farm to consumption: preliminary framework, data, and risk estimates.

PubMed

Danyluk, Michelle D; Schaffner, Donald W

2011-05-01

This project was undertaken to relate what is known about the behavior of Escherichia coli O157:H7 under laboratory conditions and integrate this information to what is known regarding the 2006 E. coli O157:H7 spinach outbreak in the context of a quantitative microbial risk assessment. The risk model explicitly assumes that all contamination arises from exposure in the field. Extracted data, models, and user inputs were entered into an Excel spreadsheet, and the modeling software @RISK was used to perform Monte Carlo simulations. The model predicts that cut leafy greens that are temperature abused will support the growth of E. coli O157:H7, and populations of the organism may increase by as much a 1 log CFU/day under optimal temperature conditions. When the risk model used a starting level of -1 log CFU/g, with 0.1% of incoming servings contaminated, the predicted numbers of cells per serving were within the range of best available estimates of pathogen levels during the outbreak. The model predicts that levels in the field of -1 log CFU/g and 0.1% prevalence could have resulted in an outbreak approximately the size of the 2006 E. coli O157:H7 outbreak. This quantitative microbial risk assessment model represents a preliminary framework that identifies available data and provides initial risk estimates for pathogenic E. coli in leafy greens. Data gaps include retail storage times, correlations between storage time and temperature, determining the importance of E. coli O157:H7 in leafy greens lag time models, and validation of the importance of cross-contamination during the washing process.

A global, multi-disciplinary, multi-sectorial initiative to combat leptospirosis: Global Leptospirosis Environmental Action Network (GLEAN).

PubMed

Durski, Kara N; Jancloes, Michel; Chowdhary, Tej; Bertherat, Eric

2014-06-05

Leptospirosis has emerged as a major public health problem in both animals and humans. The true burden of this epidemic and endemic disease is likely to be grossly under-estimated due to the non-specific clinical presentations of the disease and the difficulty of laboratory confirmation. The complexity that surrounds the transmission dynamics, particularly in epidemic situations, requires a coordinated, multi-disciplinary effort. Therefore, the Global Leptospirosis Environmental Action Network (GLEAN) was developed to improve global and local strategies of how to predict, prevent, detect, and intervene in leptospirosis outbreaks in order to prevent and control leptospirosis in high-risk populations.

A Global, Multi-Disciplinary, Multi-Sectorial Initiative to Combat Leptospirosis: Global Leptospirosis Environmental Action Network (GLEAN)

PubMed Central

Durski, Kara N.; Jancloes, Michel; Chowdhary, Tej; Bertherat, Eric

2014-01-01

Leptospirosis has emerged as a major public health problem in both animals and humans. The true burden of this epidemic and endemic disease is likely to be grossly under-estimated due to the non-specific clinical presentations of the disease and the difficulty of laboratory confirmation. The complexity that surrounds the transmission dynamics, particularly in epidemic situations, requires a coordinated, multi-disciplinary effort. Therefore, the Global Leptospirosis Environmental Action Network (GLEAN) was developed to improve global and local strategies of how to predict, prevent, detect, and intervene in leptospirosis outbreaks in order to prevent and control leptospirosis in high-risk populations. PMID:24905245

A Simulation-Based Study on the Comparison of Statistical and Time Series Forecasting Methods for Early Detection of Infectious Disease Outbreaks.

PubMed

Yang, Eunjoo; Park, Hyun Woo; Choi, Yeon Hwa; Kim, Jusim; Munkhdalai, Lkhagvadorj; Musa, Ibrahim; Ryu, Keun Ho

2018-05-11

Early detection of infectious disease outbreaks is one of the important and significant issues in syndromic surveillance systems. It helps to provide a rapid epidemiological response and reduce morbidity and mortality. In order to upgrade the current system at the Korea Centers for Disease Control and Prevention (KCDC), a comparative study of state-of-the-art techniques is required. We compared four different temporal outbreak detection algorithms: the CUmulative SUM (CUSUM), the Early Aberration Reporting System (EARS), the autoregressive integrated moving average (ARIMA), and the Holt-Winters algorithm. The comparison was performed based on not only 42 different time series generated taking into account trends, seasonality, and randomly occurring outbreaks, but also real-world daily and weekly data related to diarrhea infection. The algorithms were evaluated using different metrics. These were namely, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, symmetric mean absolute percent error (sMAPE), root-mean-square error (RMSE), and mean absolute deviation (MAD). Although the comparison results showed better performance for the EARS C3 method with respect to the other algorithms, despite the characteristics of the underlying time series data, Holt⁻Winters showed better performance when the baseline frequency and the dispersion parameter values were both less than 1.5 and 2, respectively.

Probabilistic prediction of outbreaks of meningococcus W-135 infections over the next few years in Spain

NASA Astrophysics Data System (ADS)

Acedo, L.; Burgos, C.; Cortés, J.-C.; Villanueva, R.-J.

2017-11-01

The genogroups of meningococcal and other bacteria are in competition in the ecosystem they form with the human hosts. Changes in vaccination strategies, prophylactic measures or usual habits, may also change the distribution of the genogroups in the ecosystem but, usually, this competition is ignored in most epidemiological models, despite it can be highly influential in the evolution of infection diseases and outbreaks. Our goal is to propose a susceptible-carrier-susceptible (SCS) epidemiological model to determine the percentage of carriers in the population, and introduce a fractional Lotka-Volterra competition model to describe the evolution of the meningococcal genogroups in Spain among the carriers. Using data from the distribution of the genogroups in Spain in 2011 and 2012, we find the model parameters and their uncertainties according to a probabilistic fitting approach. On this basis, we predict the evolution of the carriers of the different genogroups over the next few years and, in particular, the percentage of carriers of meningococcus W-135 with a 95% confidence interval. Then, we estimate the probability of having a possible outbreak of meningococcus W-135 in Spain over the next few years. According to our model and, under the present conditions, the risk of a serious outbreak of W-135 in Spain in the next 3 years is below 0 . 3%.

Spatially explicit models, generalized reproduction numbers and the prediction of patterns of waterborne disease

NASA Astrophysics Data System (ADS)

Rinaldo, A.; Gatto, M.; Mari, L.; Casagrandi, R.; Righetto, L.; Bertuzzo, E.; Rodriguez-Iturbe, I.

2012-12-01

Metacommunity and individual-based theoretical models are studied in the context of the spreading of infections of water-borne diseases along the ecological corridors defined by river basins and networks of human mobility. The overarching claim is that mathematical models can indeed provide predictive insight into the course of an ongoing epidemic, potentially aiding real-time emergency management in allocating health care resources and by anticipating the impact of alternative interventions. To support the claim, we examine the ex-post reliability of published predictions of the 2010-2011 Haiti cholera outbreak from four independent modeling studies that appeared almost simultaneously during the unfolding epidemic. For each modeled epidemic trajectory, it is assessed how well predictions reproduced the observed spatial and temporal features of the outbreak to date. The impact of different approaches is considered to the modeling of the spatial spread of V. cholera, the mechanics of cholera transmission and in accounting for the dynamics of susceptible and infected individuals within different local human communities. A generalized model for Haitian epidemic cholera and the related uncertainty is thus constructed and applied to the year-long dataset of reported cases now available. Specific emphasis will be dedicated to models of human mobility, a fundamental infection mechanism. Lessons learned and open issues are discussed and placed in perspective, supporting the conclusion that, despite differences in methods that can be tested through model-guided field validation, mathematical modeling of large-scale outbreaks emerges as an essential component of future cholera epidemic control. Although explicit spatial modeling is made routinely possible by widespread data mapping of hydrology, transportation infrastructure, population distribution, and sanitation, the precise condition under which a waterborne disease epidemic can start in a spatially explicit setting is still lacking. Here, we show that the requirement that all the local reproduction numbers R0 be larger than unity is neither necessary nor sufficient for outbreaks to occur when local settlements are connected by networks of primary and secondary infection mechanisms. To determine onset conditions, we derive general analytical expressions for a reproduction matrix G0 explicitly accounting for spatial distributions of human settlements and pathogen transmission via hydrological and human mobility networks. At disease onset, a generalized reproduction number Λ0 (the dominant eigenvalue of G0) must be larger than unity. We also show that geographical outbreak patterns in complex environments are linked to the dominant eigenvector and to spectral properties of G0. Tests against data and computations for the 2010 Haiti and 2000 KwaZulu-Natal cholera outbreaks, as well as against computations for metapopulation networks, demonstrate that eigenvectors of G0 provide a synthetic and effective tool for predicting the disease course in space and time. Networked connectivity models, describing the interplay between hydrology, epidemiology and social behavior sustaining human mobility, thus prove to be key tools for emergency management of waterborne infections.

Ebola and Marburg virus diseases in Africa: increased risk of outbreaks in previously unaffected areas?

PubMed

Changula, Katendi; Kajihara, Masahiro; Mweene, Aaron S; Takada, Ayato

2014-09-01

Filoviral hemorrhagic fever (FHF) is caused by ebolaviruses and marburgviruses, which both belong to the family Filoviridae. Egyptian fruit bats (Rousettus aegyptiacus) are the most likely natural reservoir for marburgviruses and entry into caves and mines that they stay in has often been associated with outbreaks of MVD. On the other hand, the natural reservoir for ebola viruses remains elusive; however, handling of wild animal carcasses has been associated with some outbreaks of EVD. In the last two decades, there has been an increase in the incidence of FHF outbreaks in Africa, some being caused by a newly found virus and some occurring in previously unaffected areas such as Guinea, Liberia and Sierra Leone, in which the most recent EVD outbreak occurred in 2014. Indeed, the predicted geographic distribution of filoviruses and their potential reservoirs in Africa includes many countries in which FHF has not been reported. To minimize the risk of virus dissemination in previously unaffected areas, there is a need for increased investment in health infrastructure in African countries, policies to facilitate collaboration between health authorities from different countries, implementation of outbreak control measures by relevant multi-disciplinary teams and education of the populations at risk. © 2014 The Societies and Wiley Publishing Asia Pty Ltd.

Application of Species Distribution Modeling for Avian Influenza surveillance in the United States considering the North America Migratory Flyways

NASA Astrophysics Data System (ADS)

Belkhiria, Jaber; Alkhamis, Moh A.; Martínez-López, Beatriz

2016-09-01

Highly Pathogenic Avian Influenza (HPAI) has recently (2014-2015) re-emerged in the United States (US) causing the largest outbreak in US history with 232 outbreaks and an estimated economic impact of $950 million. This study proposes to use suitability maps for Low Pathogenic Avian Influenza (LPAI) to identify areas at high risk for HPAI outbreaks. LPAI suitability maps were based on wild bird demographics, LPAI surveillance, and poultry density in combination with environmental, climatic, and socio-economic risk factors. Species distribution modeling was used to produce high-resolution (cell size: 500m x 500m) maps for Avian Influenza (AI) suitability in each of the four North American migratory flyways (NAMF). Results reveal that AI suitability is heterogeneously distributed throughout the US with higher suitability in specific zones of the Midwest and coastal areas. The resultant suitability maps adequately predicted most of the HPAI outbreak areas during the 2014-2015 epidemic in the US (i.e. 89% of HPAI outbreaks were located in areas identified as highly suitable for LPAI). Results are potentially useful for poultry producers and stakeholders in designing risk-based surveillance, outreach and intervention strategies to better prevent and control future HPAI outbreaks in the US.

Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States.

PubMed

Yamana, Teresa K; Kandula, Sasikiran; Shaman, Jeffrey

2017-11-01

Recent research has produced a number of methods for forecasting seasonal influenza outbreaks. However, differences among the predicted outcomes of competing forecast methods can limit their use in decision-making. Here, we present a method for reconciling these differences using Bayesian model averaging. We generated retrospective forecasts of peak timing, peak incidence, and total incidence for seasonal influenza outbreaks in 48 states and 95 cities using 21 distinct forecast methods, and combined these individual forecasts to create weighted-average superensemble forecasts. We compared the relative performance of these individual and superensemble forecast methods by geographic location, timing of forecast, and influenza season. We find that, overall, the superensemble forecasts are more accurate than any individual forecast method and less prone to producing a poor forecast. Furthermore, we find that these advantages increase when the superensemble weights are stratified according to the characteristics of the forecast or geographic location. These findings indicate that different competing influenza prediction systems can be combined into a single more accurate forecast product for operational delivery in real time.

Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States

PubMed Central

Kandula, Sasikiran; Shaman, Jeffrey

2017-01-01

Recent research has produced a number of methods for forecasting seasonal influenza outbreaks. However, differences among the predicted outcomes of competing forecast methods can limit their use in decision-making. Here, we present a method for reconciling these differences using Bayesian model averaging. We generated retrospective forecasts of peak timing, peak incidence, and total incidence for seasonal influenza outbreaks in 48 states and 95 cities using 21 distinct forecast methods, and combined these individual forecasts to create weighted-average superensemble forecasts. We compared the relative performance of these individual and superensemble forecast methods by geographic location, timing of forecast, and influenza season. We find that, overall, the superensemble forecasts are more accurate than any individual forecast method and less prone to producing a poor forecast. Furthermore, we find that these advantages increase when the superensemble weights are stratified according to the characteristics of the forecast or geographic location. These findings indicate that different competing influenza prediction systems can be combined into a single more accurate forecast product for operational delivery in real time. PMID:29107987

Prediction of Protection against Asian Enterovirus 71 Outbreak Strains by Cross-neutralizing Capacity of Serum from Dutch Donors, The Netherlands

PubMed Central

Koen, Gerrit; van Eijk, Hetty; Koekkoek, Sylvie M.; de Jong, Menno D.; Wolthers, Katja C.

2016-01-01

Outbreaks of human enterovirus 71 (EV-71) in Asia are related to high illness and death rates among children. To gain insight into the potential threat for the population of Europe, we determined the neutralizing activity in intravenous immunoglobulin (IVIg) batches and individual serum samples from donors in the Netherlands against EV-71 strains isolated in Europe and in Asia. All IVIg batches and 41%, 79%, and 65% of serum samples from children ≤5 years of age, women of childbearing age, and HIV-positive men, respectively, showed high neutralizing activity against a Dutch C1 strain, confirming widespread circulation of EV-71 in the Netherlands. Asian B3–4 and C4 strains were efficiently cross-neutralized, predicting possible protection against extensive circulation and associated outbreaks of those types in Europe. However, C2 and C5 strains that had few mutations in the capsid region consistently escaped neutralization, emphasizing the importance of monitoring antigenic diversity among circulating EV-71 strains. PMID:27533024

Social ecological analysis of an outbreak of pufferfish egg poisoning in a coastal area of Bangladesh.

PubMed

Islam, M Saiful; Luby, Stephen P; Rahman, Mahmudur; Parveen, Shahana; Homaira, Nusrat; Begum, Nur Har; Dawlat Khan, A K M; Sultana, Rebeca; Akhter, Shammi; Gurley, Emily S

2011-09-01

Recurrent outbreaks of marine pufferfish poisoning in Bangladesh highlight the need to understand the context in which the outbreaks occurred. In a recent outbreak investigation, a multidisciplinary team conducted a mixed-method study to identify the demography and clinical manifestation of the victims and to explore different uses of pufferfish, and local buying, selling, and processing practices. The outbreak primarily affected a low income household where an elderly woman collected and cooked pufferfish egg curry. Nine persons consumed the curry, and symptoms developed in 6 (67%) of these persons. Symptoms included vomiting, diarrhea, paresis, and tingling sensation; 2 (22%) persons died. The unstable income of the affected family, food crisis, and the public disposal of unsafe pufferfish byproducts all contributed to the outbreak. A multi-level intervention should be developed and disseminated with the participation of target communities to discourage unsafe discarding of pufferfish scraps and to improve the community knowledge about the risk of consuming pufferfish.

Social Ecological Analysis of an Outbreak of Pufferfish Egg Poisoning in a Coastal Area of Bangladesh

PubMed Central

Islam, M. Saiful; Luby, Stephen P.; Rahman, Mahmudur; Parveen, Shahana; Homaira, Nusrat; Begum, Nur Har; Dawlat Khan, A. K. M.; Sultana, Rebeca; Akhter, Shammi; Gurley, Emily S.

2011-01-01

Recurrent outbreaks of marine pufferfish poisoning in Bangladesh highlight the need to understand the context in which the outbreaks occurred. In a recent outbreak investigation, a multidisciplinary team conducted a mixed-method study to identify the demography and clinical manifestation of the victims and to explore different uses of pufferfish, and local buying, selling, and processing practices. The outbreak primarily affected a low income household where an elderly woman collected and cooked pufferfish egg curry. Nine persons consumed the curry, and symptoms developed in 6 (67%) of these persons. Symptoms included vomiting, diarrhea, paresis, and tingling sensation; 2 (22%) persons died. The unstable income of the affected family, food crisis, and the public disposal of unsafe pufferfish byproducts all contributed to the outbreak. A multi-level intervention should be developed and disseminated with the participation of target communities to discourage unsafe discarding of pufferfish scraps and to improve the community knowledge about the risk of consuming pufferfish. PMID:21896811

Identifying high-risk areas for sporadic measles outbreaks: lessons from South Africa.

PubMed

Sartorius, Benn; Cohen, C; Chirwa, T; Ntshoe, G; Puren, A; Hofman, K

2013-03-01

To develop a model for identifying areas at high risk for sporadic measles outbreaks based on an analysis of factors associated with a national outbreak in South Africa between 2009 and 2011. Data on cases occurring before and during the national outbreak were obtained from the South African measles surveillance programme, and data on measles immunization and population size, from the District Health Information System. A Bayesian hierarchical Poisson model was used to investigate the association between the risk of measles in infants in a district and first-dose vaccination coverage, population density, background prevalence of human immunodeficiency virus (HIV) infection and expected failure of seroconversion. Model projections were used to identify emerging high-risk areas in 2012. A clear spatial pattern of high-risk areas was noted, with many interconnected (i.e. neighbouring) areas. An increased risk of measles outbreak was significantly associated with both the preceding build-up of a susceptible population and population density. The risk was also elevated when more than 20% of infants in a populous area had missed a first vaccine dose. The model was able to identify areas at high risk of experiencing a measles outbreak in 2012 and where additional preventive measures could be undertaken. The South African measles outbreak was associated with the build-up of a susceptible population (owing to poor vaccine coverage), high prevalence of HIV infection and high population density. The predictive model developed could be applied to other settings susceptible to sporadic outbreaks of measles and other vaccine-preventable diseases.

Reactive strategies for containing developing outbreaks of pandemic influenza

PubMed Central

2011-01-01

Background In 2009 and the early part of 2010, the northern hemisphere had to cope with the first waves of the new influenza A (H1N1) pandemic. Despite high-profile vaccination campaigns in many countries, delays in administration of vaccination programs were common, and high vaccination coverage levels were not achieved. This experience suggests the need to explore the epidemiological and economic effectiveness of additional, reactive strategies for combating pandemic influenza. Methods We use a stochastic model of pandemic influenza to investigate realistic strategies that can be used in reaction to developing outbreaks. The model is calibrated to documented illness attack rates and basic reproductive number (R0) estimates, and constructed to represent a typical mid-sized North American city. Results Our model predicts an average illness attack rate of 34.1% in the absence of intervention, with total costs associated with morbidity and mortality of US$81 million for such a city. Attack rates and economic costs can be reduced to 5.4% and US$37 million, respectively, when low-coverage reactive vaccination and limited antiviral use are combined with practical, minimally disruptive social distancing strategies, including short-term, as-needed closure of individual schools, even when vaccine supply-chain-related delays occur. Results improve with increasing vaccination coverage and higher vaccine efficacy. Conclusions Such combination strategies can be substantially more effective than vaccination alone from epidemiological and economic standpoints, and warrant strong consideration by public health authorities when reacting to future outbreaks of pandemic influenza. PMID:21356128

Modeling the Ecological Niche of Bacillus anthracis to Map Anthrax Risk in Kyrgyzstan

PubMed Central

Blackburn, Jason K.; Matakarimov, Saitbek; Kozhokeeva, Sabira; Tagaeva, Zhyldyz; Bell, Lindsay K.; Kracalik, Ian T.; Zhunushov, Asankadyr

2017-01-01

Anthrax, caused by the environmental bacterium Bacillus anthracis, is an important zoonosis nearly worldwide. In Central Asia, anthrax represents a major veterinary and public health concern. In the Republic of Kyrgyzstan, ongoing anthrax outbreaks have been reported in humans associated with handling infected livestock and contaminated animal by-products such as meat or hides. The current anthrax situation has prompted calls for improved insights into the epidemiology, ecology, and spatial distribution of the disease in Kyrgyzstan to better inform control and surveillance. Disease control for both humans and livestock relies on annual livestock vaccination ahead of outbreaks. Toward this, we used a historic database of livestock anthrax reported from 1932 to 2006 mapped at high resolution to develop an ecological niche model–based prediction of B. anthracis across Kyrgyzstan and identified spatial clusters of livestock anthrax using a cluster morphology statistic. We also defined the seasonality of outbreaks in livestock. Cattle were the most frequently reported across the time period, with the greatest number of cases in late summer months. Our niche models defined four areas as suitable to support pathogen persistence, the plateaus near Talas and Bishkek, the valleys of western Kyrgyzstan along the Fergana Valley, and the low-lying areas along the shore of Lake Isyk-Kul. These areas should be considered “at risk” for livestock anthrax and subsequent human cases. Areas defined by the niche models can be used to prioritize anthrax surveillance and inform efforts to target livestock vaccination campaigns. PMID:28115677

Modeling the Ecological Niche of Bacillus anthracis to Map Anthrax Risk in Kyrgyzstan.

PubMed

Blackburn, Jason K; Matakarimov, Saitbek; Kozhokeeva, Sabira; Tagaeva, Zhyldyz; Bell, Lindsay K; Kracalik, Ian T; Zhunushov, Asankadyr

2017-03-01

AbstractAnthrax, caused by the environmental bacterium Bacillus anthracis , is an important zoonosis nearly worldwide. In Central Asia, anthrax represents a major veterinary and public health concern. In the Republic of Kyrgyzstan, ongoing anthrax outbreaks have been reported in humans associated with handling infected livestock and contaminated animal by-products such as meat or hides. The current anthrax situation has prompted calls for improved insights into the epidemiology, ecology, and spatial distribution of the disease in Kyrgyzstan to better inform control and surveillance. Disease control for both humans and livestock relies on annual livestock vaccination ahead of outbreaks. Toward this, we used a historic database of livestock anthrax reported from 1932 to 2006 mapped at high resolution to develop an ecological niche model-based prediction of B. anthracis across Kyrgyzstan and identified spatial clusters of livestock anthrax using a cluster morphology statistic. We also defined the seasonality of outbreaks in livestock. Cattle were the most frequently reported across the time period, with the greatest number of cases in late summer months. Our niche models defined four areas as suitable to support pathogen persistence, the plateaus near Talas and Bishkek, the valleys of western Kyrgyzstan along the Fergana Valley, and the low-lying areas along the shore of Lake Isyk-Kul. These areas should be considered "at risk" for livestock anthrax and subsequent human cases. Areas defined by the niche models can be used to prioritize anthrax surveillance and inform efforts to target livestock vaccination campaigns.

The role of seafood in foodborne diseases in the United States of America.

PubMed

Lipp, E K; Rose, J B

1997-08-01

In the United States of America, seafood ranked third on the list of products which caused foodborne disease between 1983 and 1992. Outbreaks connected with fish vectors were caused by scombroid, ciguatoxin, bacteria and unknown agents; in shellfish, unknown agents, paralytic shellfish poisoning, Vibrio spp. and other bacteria, followed by hepatitis A virus, were responsible for the outbreaks. At least ten genera of bacterial pathogens have been implicated in seafood-borne diseases. Over the past twenty-five years, bacterial pathogens associated with faecal contamination have represented only 4% of the shellfish-associated outbreaks, while naturally-occurring bacteria accounted for 20% of shellfish-related illnesses and 99% of the deaths. Most of these indigenous bacteria fall into the family Vibrionaceae which includes the genera Vibrio, Aeromonas and Plesiomonas. In general, Vibrio spp. are not associated with faecal contamination and therefore faecal indicators do not correlate with the presence of Vibrio. Viruses are the most significant cause of shellfish-associated disease: in New York State, for example, 33% and 62% of 196 outbreaks between 1981 and 1992 were caused by Norwalk virus and gastrointestinal viruses (small round structured viruses), respectively. In addition, several illnesses are a result of toxic algal blooms, the growth of naturally occurring bacteria and diatoms causing neurotoxic shellfish poisoning, paralytic shellfish poisoning, diarrhoetic shellfish poisoning, amnesic shellfish poisoning and ciguatera. Current estimates place the annual number of ciguatera cases at 20,000 world-wide. Scombroid poisoning is the most significant cause of illness associated with seafood. Scombrotoxin is of bacterial origin and halophilic Vibrio spp. causing high histamine levels are implicated as the source. Scombroid poisoning is geographically diverse and many species have been implicated, namely: tuna, mahi-mahi, bluefish, sardines, mackerel, amberjack and abalone. Temperature abuse has been cited as a major cause of scombroid poisoning. For routine work, the use of faecal indicators to predict the relative level of faecal contamination should not be disposed of. However, the main source of seafood illness is due to species which are not predicted by these organisms. In order to protect public health, routine surveillance using new pathogen-specific techniques such as polymerase chain reaction should be used. This, in combination with risk assessment methods and hazard analysis and critical control points, will begin to address the need for improvement in the safety of seafood.

Outbreak of multidrug-resistant Escherichia coli sequence type 131 in a neonatal intensive care unit: efficient active surveillance prevented fatal outcome.

PubMed

Silwedel, C; Vogel, U; Claus, H; Glaser, K; Speer, C P; Wirbelauer, J

2016-06-01

Outbreaks of infections with multidrug-resistant bacteria in neonatal intensive care units (NICUs) pose a major threat, especially to extremely preterm infants. This study describes a 35-day outbreak of multidrug-resistant Escherichia coli (E. coli) in a tertiary-level NICU in Germany. To underline the importance of surveillance policies in the particularly vulnerable cohort of preterm infants and to describe the efficacy of outbreak control strategies. Data were collected retrospectively from medical reports. Infants and environment were tested for E. coli. The outbreak affected a total of 13 infants between 25(+1) and 35(+0) weeks of gestation with seven infants showing signs of infection. The outbreak strain was identified as E. coli sequence type 131. Environmental screening provided no evidence for an environmental source. Through colonization surveillance and immediate and adequate treatment of potentially infected preterm infants, no fatalities occurred. Outbreak control was achieved by strict contact precautions, enhanced screening and temporary relocation of the NICU. Relocation and reconstruction improved the NICU's structural layout, focusing on isolation capacities. Follow-up indicated carriage for several months in some infants. Routine surveillance allowed early detection of the outbreak. The identification of carriers of the outbreak strain was successfully used to direct antibiotic treatment in case of infection. Enhanced hygienic measures and ward relocation were instrumental in controlling the outbreak. Copyright © 2016. Published by Elsevier Ltd.

Palivizumab prophylaxis during nosocomial outbreaks of respiratory syncytial virus in a neonatal intensive care unit: predicting effectiveness with an artificial neural network model.

PubMed

Saadah, Loai M; Chedid, Fares D; Sohail, Muhammad R; Nazzal, Yazied M; Al Kaabi, Mohammed R; Rahmani, Aiman Y

2014-03-01

To identify subgroups of premature infants who may benefit from palivizumab prophylaxis during nosocomial outbreaks of respiratory syncytial virus (RSV) infection. Retrospective analysis using an artificial intelligence model. Level IIIB, 35-bed, neonatal intensive care unit (NICU) at a tertiary care hospital in the United Arab Emirates. One hundred seventy six premature infants, born at a gestational age of 22-34 weeks, and hospitalized during four RSV outbreaks that occurred between April 2005 and July 2007. We collected demographic and clinical data for each patient by using a standardized form. Input data consisted of seven categoric and continuous variables each. We trained, tested, and validated artificial neural networks for three outcomes of interest: mortality, days of supplemental oxygen, and length of NICU stay after the index case was identified. We compared variable impacts and performed reassignments with live predictions to evaluate the effect of palivizumab. Of the 176 infants, 31 (17.6%) received palivizumab during the outbreaks. All neural network configurations converged within 4 seconds in less than 400 training cycles. Infants who received palivizumab required supplemental oxygen for a shorter duration compared with controls (105.2 ± 7.2 days vs 113.2 ± 10.4 days, p=0.003). This benefit was statistically significant in male infants whose birth weight was less than 0.7 kg and who had hemodynamically significant congenital heart disease. Length of NICU stay after identification of the index case and mortality were independent of palivizumab use. Palivizumab may be an effective intervention during nosocomial outbreaks of RSV in a subgroup of extremely low-birth-weight male infants with hemodynamically significant congenital heart disease. © 2013 Pharmacotherapy Publications, Inc.

Using an agent-based model to evaluate the effect of producer specialization on the epidemiological resilience of livestock production networks

PubMed Central

2018-01-01

An agent-based computer model that builds representative regional U.S. hog production networks was developed and employed to assess the potential impact of the ongoing trend towards increased producer specialization upon network-level resilience to catastrophic disease outbreaks. Empirical analyses suggest that the spatial distribution and connectivity patterns of contact networks often predict epidemic spreading dynamics. Our model heuristically generates realistic systems composed of hog producer, feed mill, and slaughter plant agents. Network edges are added during each run as agents exchange livestock and feed. The heuristics governing agents’ contact patterns account for factors including their industry roles, physical proximities, and the age of their livestock. In each run, an infection is introduced, and may spread according to probabilities associated with the various modes of contact. For each of three treatments—defined by one-phase, two-phase, and three-phase production systems—a parameter variation experiment examines the impact of the spatial density of producer agents in the system upon the length and size of disease outbreaks. Resulting data show phase transitions whereby, above some density threshold, systemic outbreaks become possible, echoing findings from percolation theory. Data analysis reveals that multi-phase production systems are vulnerable to catastrophic outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outbreak scales and durations. Key differences in network-level metrics shed light on these results, suggesting that the absence of potentially-bridging producer–producer edges may be largely responsible for the superior disease resilience of single-phase “farrow to finish” production systems. PMID:29522574

Novel Microbiological and Spatial Statistical Methods to Improve Strength of Epidemiological Evidence in a Community-Wide Waterborne Outbreak

PubMed Central

Jalava, Katri; Rintala, Hanna; Ollgren, Jukka; Maunula, Leena; Gomez-Alvarez, Vicente; Revez, Joana; Palander, Marja; Antikainen, Jenni; Kauppinen, Ari; Räsänen, Pia; Siponen, Sallamaari; Nyholm, Outi; Kyyhkynen, Aino; Hakkarainen, Sirpa; Merentie, Juhani; Pärnänen, Martti; Loginov, Raisa; Ryu, Hodon; Kuusi, Markku; Siitonen, Anja; Miettinen, Ilkka; Santo Domingo, Jorge W.; Hänninen, Marja-Liisa; Pitkänen, Tarja

2014-01-01

Failures in the drinking water distribution system cause gastrointestinal outbreaks with multiple pathogens. A water distribution pipe breakage caused a community-wide waterborne outbreak in Vuorela, Finland, July 2012. We investigated this outbreak with advanced epidemiological and microbiological methods. A total of 473/2931 inhabitants (16%) responded to a web-based questionnaire. Water and patient samples were subjected to analysis of multiple microbial targets, molecular typing and microbial community analysis. Spatial analysis on the water distribution network was done and we applied a spatial logistic regression model. The course of the illness was mild. Drinking untreated tap water from the defined outbreak area was significantly associated with illness (RR 5.6, 95% CI 1.9–16.4) increasing in a dose response manner. The closer a person lived to the water distribution breakage point, the higher the risk of becoming ill. Sapovirus, enterovirus, single Campylobacter jejuni and EHEC O157:H7 findings as well as virulence genes for EPEC, EAEC and EHEC pathogroups were detected by molecular or culture methods from the faecal samples of the patients. EPEC, EAEC and EHEC virulence genes and faecal indicator bacteria were also detected in water samples. Microbial community sequencing of contaminated tap water revealed abundance of Arcobacter species. The polyphasic approach improved the understanding of the source of the infections, and aided to define the extent and magnitude of this outbreak. PMID:25147923

Global biosurveillance: enabling science and technology. Workshop background and motivation: international scientific engagement for global security

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cui, Helen H

2011-01-18

Through discussion the conference aims to: (1) Identify core components of a comprehensive global biosurveillance capability; (2) Determine the scientific and technical bases to support such a program; (3) Explore the improvement in biosurveillance to enhance regional and global disease outbreak prediction; (4) Recommend an engagement approach to establishing an effective international community and regional or global network; (5) Propose implementation strategies and the measures of effectiveness; and (6) Identify the challenges that must be overcome in the next 3-5 years in order to establish an initial global biosurveillance capability that will have significant positive impact on BioNP as wellmore » as public health and/or agriculture. There is also a look back at the First Biothreat Nonproliferation Conference from December 2007. Whereas the first conference was an opportunity for problem solving to enhance and identify new paradigms for biothreat nonproliferation, this conference is moving towards integrated comprehensive global biosurveillance. Main reasons for global biosurveillance are: (1) Rapid assessment of unusual disease outbreak; (2) Early warning of emerging, re-emerging and engineered biothreat enabling reduced morbidity and mortality; (3) Enhanced crop and livestock management; (4) Increase understanding of host-pathogen interactions and epidemiology; (5) Enhanced international transparency for infectious disease research supporting BWC goals; and (6) Greater sharing of technology and knowledge to improve global health.« less

Upholding Tuberculosis Services during the 2014 Ebola Storm: An Encouraging Experience from Conakry, Guinea

PubMed Central

Zachariah, Rony; Woldeyohannes, Desalegn; Bangoura, Adama; Chérif, Gba-Foromo; Loua, Francis; Hermans, Veerle; Tayler-Smith, Katie; Sikhondze, Welile; Camara, Lansana-Mady

2016-01-01

Setting Ten targeted health facilities supported by Damien Foundation (a Belgian Non Governmental Organization) and the National Tuberculosis (TB) Program in Conakry, Guinea. Objectives To uphold TB program performance during the Ebola outbreak in the presence of a package of pre-emptive additional measures geared at reinforcing the routine TB program, and ensuring Ebola infection control, health-workers safety and motivation. Design A retrospective comparative cohort study of a TB program assessing the performance before (2013) and during the (2014) Ebola outbreak. Results During the Ebola outbreak, all health facilities were maintained opened, there were no reported health-worker Ebola infections, drug stockouts or health staff absences. Of 2,475 presumptive pulmonary TB cases, 13% were diagnosed with TB in both periods (160/1203 in 2013 and 163/1272 in 2014). For new TB, treatment success improved from 84% before to 87% during the Ebola outbreak (P = 0.03). Adjusted Hazard-ratios (AHR) for an unfavorable outcome was alwo lower during the Ebola outbreak, AHR = 0.8, 95% CI:0.7–0.9, P = 0.04). Treatment success improved for HIV co-infected patients (72% to 80%, P<0.01). For retreatment patients, the proportion achieving treatment success was maintained (68% to 72%, P = 0.05). Uptake of HIV-testing and Cotrimoxazole Preventive Treatment was maintained over 85%, and Anti-Retroviral Therapy uptake increased from 77% in 2013 to 86% in 2014 (P<0.01). Conclusion Contingency planning and health system and worker support during the 2014 Ebola outbreak was associated with encouraging and sustained TB program performance. This is of relevance to future outbreaks. PMID:27533499

How to make predictions about future infectious disease risks

PubMed Central

Woolhouse, Mark

2011-01-01

Formal, quantitative approaches are now widely used to make predictions about the likelihood of an infectious disease outbreak, how the disease will spread, and how to control it. Several well-established methodologies are available, including risk factor analysis, risk modelling and dynamic modelling. Even so, predictive modelling is very much the ‘art of the possible’, which tends to drive research effort towards some areas and away from others which may be at least as important. Building on the undoubted success of quantitative modelling of the epidemiology and control of human and animal diseases such as AIDS, influenza, foot-and-mouth disease and BSE, attention needs to be paid to developing a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behaviour to land use and climate change. At the same time, there is still considerable room for improvement in how quantitative analyses and their outputs are communicated to policy makers and other stakeholders. A starting point would be generally accepted guidelines for ‘good practice’ for the development and the use of predictive models. PMID:21624924

E. coli O157 outbreaks in the United Kingdom: past, present, and future

PubMed Central

Pennington, Thomas Hugh

2014-01-01

This review describes Escherichia coli O157 outbreaks in the United Kingdom, beginning from the first, in the 1980s, to those recorded in 2013. We point out that the United Kingdom differs from other countries, particularly the United States, in that it has had a considerable number of outbreaks associated with butchers, but very few caused by contaminated burgers. Two of the butcher-associated outbreaks (in central Scotland in 1996 and South Wales in 2005) were very large and are considered here in detail; the reviewer conducted detailed investigations into both outbreaks. Also considered is the very large outbreak that occurred in visitors to an open farm in Surrey in 2009. Detailed descriptions of some milk-borne outbreaks and incidents connected with camping and childrens’ nurseries have been published, and these are also considered in this review. Large outbreaks in the United Kingdom have sometimes led to policy developments regarding food safety, and these are considered, together with public reactions to them, their health effect, and their value, as examples to follow or eschew in terms of the procedures to be adopted in response to incidents of this kind. Regulatory and legal consequences are also considered. As a wise man said, making predictions is difficult, particularly about the future. This review follows this position but points out that although human infections caused by E. coli O157 are rare in the United Kingdom, their incidence has not changed significantly in the last 17 years. This review points out that although a response to an outbreak is to say “lessons must be learned”, this response has been tempered by forgetfulness. Accordingly, this review restricts its recommendations regarding outbreaks to two: the crucial importance of a rapid response and the importance of experience, and even “gut feeling”, when an inspector is evaluating the safety of a food business. PMID:25187729

Perceptions of vulnerability to a future outbreak: a study of horse managers affected by the first Australian equine influenza outbreak

PubMed Central

2013-01-01

Background A growing body of work shows the benefits of applying social cognitive behavioural theory to investigate infection control and biosecurity practices. Protection motivation theory has been used to predict protective health behaviours. The theory outlines that a perception of a lack of vulnerability to a disease contributes to a reduced threat appraisal, which results in poorer motivation, and is linked to poorer compliance with advised health protective behaviours. This study, conducted following the first-ever outbreak of equine influenza in Australia in 2007, identified factors associated with horse managers’ perceived vulnerability to a future equine influenza outbreak. Results Of the 200 respondents, 31.9% perceived themselves to be very vulnerable, 36.6% vulnerable and 31.4% not vulnerable to a future outbreak of equine influenza. Multivariable logistic regression modelling revealed that managers involved in horse racing and those on rural horse premises perceived themselves to have low levels of vulnerability. Managers of horse premises that experienced infection in their horses in 2007 and those seeking infection control information from specific sources reported increased levels of perceived vulnerability to a future outbreak. Conclusion Different groups across the horse industry perceived differing levels of vulnerability to a future outbreak. Increased vulnerability contributes to favourable infection control behaviour and hence these findings are important for understanding uptake of recommended infection control measures. Future biosecurity communication strategies should be delivered through information sources suitable for the horse racing and rural sectors. PMID:23902718

Perceptions of vulnerability to a future outbreak: a study of horse managers affected by the first Australian equine influenza outbreak.

PubMed

Schemann, Kathrin; Firestone, Simon M; Taylor, Melanie R; Toribio, Jenny-Ann L M L; Ward, Michael P; Dhand, Navneet K

2013-07-31

A growing body of work shows the benefits of applying social cognitive behavioural theory to investigate infection control and biosecurity practices. Protection motivation theory has been used to predict protective health behaviours. The theory outlines that a perception of a lack of vulnerability to a disease contributes to a reduced threat appraisal, which results in poorer motivation, and is linked to poorer compliance with advised health protective behaviours. This study, conducted following the first-ever outbreak of equine influenza in Australia in 2007, identified factors associated with horse managers' perceived vulnerability to a future equine influenza outbreak. Of the 200 respondents, 31.9% perceived themselves to be very vulnerable, 36.6% vulnerable and 31.4% not vulnerable to a future outbreak of equine influenza. Multivariable logistic regression modelling revealed that managers involved in horse racing and those on rural horse premises perceived themselves to have low levels of vulnerability. Managers of horse premises that experienced infection in their horses in 2007 and those seeking infection control information from specific sources reported increased levels of perceived vulnerability to a future outbreak. Different groups across the horse industry perceived differing levels of vulnerability to a future outbreak. Increased vulnerability contributes to favourable infection control behaviour and hence these findings are important for understanding uptake of recommended infection control measures. Future biosecurity communication strategies should be delivered through information sources suitable for the horse racing and rural sectors.

Listeriosis Outbreaks in British Columbia, Canada, Caused by Soft Ripened Cheese Contaminated from Environmental Sources

PubMed Central

Wilcott, Lynn; Naus, Monika

2015-01-01

Soft ripened cheese (SRC) caused over 130 foodborne illnesses in British Columbia (BC), Canada, during two separate listeriosis outbreaks. Multiple agencies investigated the events that lead to cheese contamination with Listeria monocytogenes (L.m.), an environmentally ubiquitous foodborne pathogen. In both outbreaks pasteurized milk and the pasteurization process were ruled out as sources of contamination. In outbreak A, environmental transmission of L.m. likely occurred from farm animals to personnel to culture solutions used during cheese production. In outbreak B, birds were identified as likely contaminating the dairy plant's water supply and cheese during the curd-washing step. Issues noted during outbreak A included the risks of operating a dairy plant in a farm environment, potential for transfer of L.m. from the farm environment to the plant via shared toilet facilities, failure to clean and sanitize culture spray bottles, and cross-contamination during cheese aging. L.m. contamination in outbreak B was traced to wild swallows defecating in the plant's open cistern water reservoir and a multibarrier failure in the water disinfection system. These outbreaks led to enhanced inspection and surveillance of cheese plants, test and release programs for all SRC manufactured in BC, improvements in plant design and prevention programs, and reduced listeriosis incidence. PMID:25918702

Listeriosis outbreaks in British Columbia, Canada, caused by soft ripened cheese contaminated from environmental sources.

PubMed

McIntyre, Lorraine; Wilcott, Lynn; Naus, Monika

2015-01-01

Soft ripened cheese (SRC) caused over 130 foodborne illnesses in British Columbia (BC), Canada, during two separate listeriosis outbreaks. Multiple agencies investigated the events that lead to cheese contamination with Listeria monocytogenes (L.m.), an environmentally ubiquitous foodborne pathogen. In both outbreaks pasteurized milk and the pasteurization process were ruled out as sources of contamination. In outbreak A, environmental transmission of L.m. likely occurred from farm animals to personnel to culture solutions used during cheese production. In outbreak B, birds were identified as likely contaminating the dairy plant's water supply and cheese during the curd-washing step. Issues noted during outbreak A included the risks of operating a dairy plant in a farm environment, potential for transfer of L.m. from the farm environment to the plant via shared toilet facilities, failure to clean and sanitize culture spray bottles, and cross-contamination during cheese aging. L.m. contamination in outbreak B was traced to wild swallows defecating in the plant's open cistern water reservoir and a multibarrier failure in the water disinfection system. These outbreaks led to enhanced inspection and surveillance of cheese plants, test and release programs for all SRC manufactured in BC, improvements in plant design and prevention programs, and reduced listeriosis incidence.

How physiological and physical processes contribute to the phenology of cyanobacterial blooms in large shallow lakes: A new Euler-Lagrangian coupled model.

PubMed

Feng, Tao; Wang, Chao; Wang, Peifang; Qian, Jin; Wang, Xun

2018-09-01

Cyanobacterial blooms have emerged as one of the most severe ecological problems affecting large and shallow freshwater lakes. To improve our understanding of the factors that influence, and could be used to predict, surface blooms, this study developed a novel Euler-Lagrangian coupled approach combining the Eulerian model with agent-based modelling (ABM). The approach was subsequently verified based on monitoring datasets and MODIS data in a large shallow lake (Lake Taihu, China). The Eulerian model solves the Eulerian variables and physiological parameters, whereas ABM generates the complete life cycle and transport processes of cyanobacterial colonies. This model ensemble performed well in fitting historical data and predicting the dynamics of cyanobacterial biomass, bloom distribution, and area. Based on the calculated physical and physiological characteristics of surface blooms, principal component analysis (PCA) captured the major processes influencing surface bloom formation at different stages (two bloom clusters). Early bloom outbreaks were influenced by physical processes (horizontal transport and vertical turbulence-induced mixing), whereas buoyancy-controlling strategies were essential for mature bloom outbreaks. Canonical correlation analysis (CCA) revealed the combined actions of multiple environment variables on different bloom clusters. The effects of buoyancy-controlling strategies (ISP), vertical turbulence-induced mixing velocity of colony (VMT) and horizontal drift velocity of colony (HDT) were quantitatively compared using scenario simulations in the coupled model. VMT accounted for 52.9% of bloom formations and maintained blooms over long periods, thus demonstrating the importance of wind-induced turbulence in shallow lakes. In comparison, HDT and buoyancy controlling strategies influenced blooms at different stages. In conclusion, the approach developed here presents a promising tool for understanding the processes of onshore/offshore algal blooms formation and subsequent predicting. Copyright © 2018 Elsevier Ltd. All rights reserved.

Perspectives on West Africa Ebola Virus Disease Outbreak, 2013–2016

PubMed Central

Spengler, Jessica R.; Ervin, Elizabeth D.; Towner, Jonathan S.; Rollin, Pierre E.

2016-01-01

The variety of factors that contributed to the initial undetected spread of Ebola virus disease in West Africa during 2013–2016 and the difficulty controlling the outbreak once the etiology was identified highlight priorities for disease prevention, detection, and response. These factors include occurrence in a region recovering from civil instability and lacking experience with Ebola response; inadequate surveillance, recognition of suspected cases, and Ebola diagnosis; mobile populations and extensive urban transmission; and the community’s insufficient general understanding about the disease. The magnitude of the outbreak was not attributable to a substantial change of the virus. Continued efforts during the outbreak and in preparation for future outbreak response should involve identifying the reservoir, improving in-country detection and response capacity, conducting survivor studies and supporting survivors, engaging in culturally appropriate public education and risk communication, building productive interagency relationships, and continuing support for basic research. PMID:27070842

Perspectives on West Africa Ebola Virus Disease Outbreak, 2013-2016

DOE PAGES

Spengler, Jessica R.; Ervin, Elizabeth D.; Towner, Jonathan S.; ...

2016-06-01

The variety of factors that contributed to the initial undetected spread of Ebola virus disease in West Africa during 2013-2016 and the difficulty controlling the outbreak once the etiology was identified highlight priorities for disease prevention, detection, and response. These factors include occurrence in a region recovering from civil instability and lacking experience with Ebola response; inadequate surveillance, recognition of suspected cases, and Ebola diagnosis; mobile populations and extensive urban transmission; and the community's insufficient general understanding about the disease. The magnitude of the outbreak was not attributable to a substantial change of the virus. Finally, continued efforts during themore » outbreak and in preparation for future outbreak response should involve identifying the reservoir, improving in-country detection and response capacity, conducting survivor studies and supporting survivors, engaging in culturally appropriate public education and risk communication, building productive interagency relationships, and continuing support for basic research.« less

Perspectives on West Africa Ebola Virus Disease Outbreak, 2013-2016

DOE Office of Scientific and Technical Information (OSTI.GOV)

Spengler, Jessica R.; Ervin, Elizabeth D.; Towner, Jonathan S.

The variety of factors that contributed to the initial undetected spread of Ebola virus disease in West Africa during 2013-2016 and the difficulty controlling the outbreak once the etiology was identified highlight priorities for disease prevention, detection, and response. These factors include occurrence in a region recovering from civil instability and lacking experience with Ebola response; inadequate surveillance, recognition of suspected cases, and Ebola diagnosis; mobile populations and extensive urban transmission; and the community's insufficient general understanding about the disease. The magnitude of the outbreak was not attributable to a substantial change of the virus. Finally, continued efforts during themore » outbreak and in preparation for future outbreak response should involve identifying the reservoir, improving in-country detection and response capacity, conducting survivor studies and supporting survivors, engaging in culturally appropriate public education and risk communication, building productive interagency relationships, and continuing support for basic research.« less

Perspectives on West Africa Ebola Virus Disease Outbreak, 2013-2016.

PubMed

Spengler, Jessica R; Ervin, Elizabeth D; Towner, Jonathan S; Rollin, Pierre E; Nichol, Stuart T

2016-06-01

The variety of factors that contributed to the initial undetected spread of Ebola virus disease in West Africa during 2013-2016 and the difficulty controlling the outbreak once the etiology was identified highlight priorities for disease prevention, detection, and response. These factors include occurrence in a region recovering from civil instability and lacking experience with Ebola response; inadequate surveillance, recognition of suspected cases, and Ebola diagnosis; mobile populations and extensive urban transmission; and the community's insufficient general understanding about the disease. The magnitude of the outbreak was not attributable to a substantial change of the virus. Continued efforts during the outbreak and in preparation for future outbreak response should involve identifying the reservoir, improving in-country detection and response capacity, conducting survivor studies and supporting survivors, engaging in culturally appropriate public education and risk communication, building productive interagency relationships, and continuing support for basic research.

Foodborne Botulism in Canada, 1985–2005

PubMed Central

Leclair, Daniel; Fung, Joe; Isaac-Renton, Judith L.; Proulx, Jean-Francois; May-Hadford, Jennifer; Ellis, Andrea; Ashton, Edie; Bekal, Sadjia; Farber, Jeffrey M.; Blanchfield, Burke

2013-01-01

During 1985–2005, a total of 91 laboratory-confirmed outbreaks of foodborne botulism occurred in Canada; these outbreaks involved 205 cases and 11 deaths. Of the outbreaks, 75 (86.2%) were caused by Clostridium botulinum type E, followed by types A (7, 8.1%) and B (5, 5.7%). Approximately 85% of the outbreaks occurred in Alaska Native communities, particularly the Inuit of Nunavik in northern Quebec and the First Nations population of the Pacific coast of British Columbia. These populations were predominantly exposed to type E botulinum toxin through the consumption of traditionally prepared marine mammal and fish products. Two botulism outbreaks were attributed to commercial ready-to-eat meat products and 3 to foods served in restaurants; several cases were attributed to non-Native home-prepared foods. Three affected pregnant women delivered healthy infants. Improvements in botulism case identification and early treatment have resulted in a reduction in the case-fatality rate in Canada. PMID:23735780

Epidemiology and Management of the 2013-16 West African Ebola Outbreak.

PubMed

Boisen, M L; Hartnett, J N; Goba, A; Vandi, M A; Grant, D S; Schieffelin, J S; Garry, R F; Branco, L M

2016-09-29

The 2013-16 West African Ebola outbreak is the largest, most geographically dispersed, and deadliest on record, with 28,616 suspected cases and 11,310 deaths recorded to date in Guinea, Liberia, and Sierra Leone. We provide a review of the epidemiology and management of the 2013-16 Ebola outbreak in West Africa aimed at stimulating reflection on lessons learned that may improve the response to the next international health crisis caused by a pathogen that emerges in a region of the world with a severely limited health care infrastructure. Surveillance efforts employing rapid and effective point-of-care diagnostics designed for environments that lack advanced laboratory infrastructure will greatly aid in early detection and containment efforts during future outbreaks. Introduction of effective therapeutics and vaccines against Ebola into the public health system and the biodefense armamentarium is of the highest priority if future outbreaks are to be adequately managed and contained in a timely manner.

Foodborne botulism in Canada, 1985-2005.

PubMed

Leclair, Daniel; Fung, Joe; Isaac-Renton, Judith L; Proulx, Jean-Francois; May-Hadford, Jennifer; Ellis, Andrea; Ashton, Edie; Bekal, Sadjia; Farber, Jeffrey M; Blanchfield, Burke; Austin, John W

2013-06-01

During 1985-2005, a total of 91 laboratory-confirmed outbreaks of foodborne botulism occurred in Canada; these outbreaks involved 205 cases and 11 deaths. Of the outbreaks, 75 (86.2%) were caused by Clostridium botulinum type E, followed by types A (7, 8.1%) and B (5, 5.7%). Approximately 85% of the outbreaks occurred in Alaska Native communities, particularly the Inuit of Nunavik in northern Quebec and the First Nations population of the Pacific coast of British Columbia. These populations were predominantly exposed to type E botulinum toxin through the consumption of traditionally prepared marine mammal and fish products. Two botulism outbreaks were attributed to commercial ready-to-eat meat products and 3 to foods served in restaurants; several cases were attributed to non-Native home-prepared foods. Three affected pregnant women delivered healthy infants. Improvements in botulism case identification and early treatment have resulted in a reduction in the case-fatality rate in Canada.

Applicability of internet search index for asthma admission forecast using machine learning.

PubMed

Luo, Li; Liao, Chengcheng; Zhang, Fengyi; Zhang, Wei; Li, Chunyang; Qiu, Zhixin; Huang, Debin

2018-04-15

This study aimed to determine whether a search index could provide insight into trends in asthma admission in China. An Internet search index is a powerful tool to monitor and predict epidemic outbreaks. However, whether using an internet search index can significantly improve asthma admissions forecasts remains unknown. The long-term goal is to develop a surveillance system to help early detection and interventions for asthma and to avoid asthma health care resource shortages in advance. In this study, we used a search index combined with air pollution data, weather data, and historical admissions data to forecast asthma admissions using machine learning. Results demonstrated that the best area under the curve in the test set that can be achieved is 0.832, using all predictors mentioned earlier. A search index is a powerful predictor in asthma admissions forecast, and a recent search index can reflect current asthma admissions with a lag-effect to a certain extent. The addition of a real-time, easily accessible search index improves forecasting capabilities and demonstrates the predictive potential of search index. Copyright © 2018 John Wiley & Sons, Ltd.

An Adjusted Likelihood Ratio Approach Analysing Distribution of Food Products to Assist the Investigation of Foodborne Outbreaks

PubMed Central

Norström, Madelaine; Kristoffersen, Anja Bråthen; Görlach, Franziska Sophie; Nygård, Karin; Hopp, Petter

2015-01-01

In order to facilitate foodborne outbreak investigations there is a need to improve the methods for identifying the food products that should be sampled for laboratory analysis. The aim of this study was to examine the applicability of a likelihood ratio approach previously developed on simulated data, to real outbreak data. We used human case and food product distribution data from the Norwegian enterohaemorrhagic Escherichia coli outbreak in 2006. The approach was adjusted to include time, space smoothing and to handle missing or misclassified information. The performance of the adjusted likelihood ratio approach on the data originating from the HUS outbreak and control data indicates that the adjusted approach is promising and indicates that the adjusted approach could be a useful tool to assist and facilitate the investigation of food borne outbreaks in the future if good traceability are available and implemented in the distribution chain. However, the approach needs to be further validated on other outbreak data and also including other food products than meat products in order to make a more general conclusion of the applicability of the developed approach. PMID:26237468

Finding Outbreaks Faster

PubMed Central

Smolinski, Mark S.; Olsen, Jennifer M.

2017-01-01

Rapid detection, reporting, and response to an infectious disease outbreak are critical to prevent localized health events from emerging as pandemic threats. Metrics to evaluate the timeliness of these critical activities, however, are lacking. Easily understood and comparable measures for tracking progress and encouraging investment in rapid detection, reporting, and response are sorely needed. We propose that the timeliness of outbreak detection, reporting, laboratory confirmation, response, and public communication should be considered as measures for improving global health security at the national level, allowing countries to track progress over time and inform investments in disease surveillance. PMID:28384035

[The journey of Legionella pneumophila from amoebae to macrophage. Reflections on the largest outbreak of legionnaire's disease].

PubMed

Segovia Hernández, Manuel

2005-01-01

Legionella, the causative agent of legionnaire's disease (LD), can survive and grow in amoebic cells. Free-living amoebae may play a role in the selection of virulence traits and in adaptation to survival in macrophages, and represent an important reservoir of Legionella. These amoebae may act as a Trojan horse bringing hidden bacteria within the human environments. The community outbreak of LD that occurred in Murcia in July 2001, the largest such outbreak ever reported, afforded an unusual opportunity to improve the knowledge of this disease.

Transmission Pathways of Foot-and-Mouth Disease Virus in the United Kingdom in 2007

PubMed Central

Cottam, Eleanor M.; Wadsworth, Jemma; Shaw, Andrew E.; Rowlands, Rebecca J.; Goatley, Lynnette; Maan, Sushila; Maan, Narender S.; Mertens, Peter P. C.; Ebert, Katja; Li, Yanmin; Ryan, Eoin D.; Juleff, Nicholas; Ferris, Nigel P.; Wilesmith, John W.; Haydon, Daniel T.; King, Donald P.; Paton, David J.; Knowles, Nick J.

2008-01-01

Foot-and-mouth disease (FMD) virus causes an acute vesicular disease of domesticated and wild ruminants and pigs. Identifying sources of FMD outbreaks is often confounded by incomplete epidemiological evidence and the numerous routes by which virus can spread (movements of infected animals or their products, contaminated persons, objects, and aerosols). Here, we show that the outbreaks of FMD in the United Kingdom in August 2007 were caused by a derivative of FMDV O1 BFS 1860, a virus strain handled at two FMD laboratories located on a single site at Pirbright in Surrey. Genetic analysis of complete viral genomes generated in real-time reveals a probable chain of transmission events, predicting undisclosed infected premises, and connecting the second cluster of outbreaks in September to those in August. Complete genome sequence analysis of FMD viruses conducted in real-time have identified the initial and intermediate sources of these outbreaks and demonstrate the value of such techniques in providing information useful to contemporary disease control programmes. PMID:18421380

Information content of contact-pattern representations and predictability of epidemic outbreaks

PubMed Central

Holme, Petter

2015-01-01

To understand the contact patterns of a population—who is in contact with whom, and when the contacts happen—is crucial for modeling outbreaks of infectious disease. Traditional theoretical epidemiology assumes that any individual can meet any with equal probability. A more modern approach, network epidemiology, assumes people are connected into a static network over which the disease spreads. Newer yet, temporal network epidemiology, includes the time in the contact representations. In this paper, we investigate the effect of these successive inclusions of more information. Using empirical proximity data, we study both outbreak sizes from unknown sources, and from known states of ongoing outbreaks. In the first case, there are large differences going from a fully mixed simulation to a network, and from a network to a temporal network. In the second case, differences are smaller. We interpret these observations in terms of the temporal network structure of the data sets. For example, a fast overturn of nodes and links seem to make the temporal information more important. PMID:26403504

Genomic Analysis of Salmonella enterica Serovar Typhimurium DT160 Associated with a 14-Year Outbreak, New Zealand, 1998–2012

PubMed Central

Benschop, Jackie; Biggs, Patrick J.; Marshall, Jonathan C.; Hayman, David T.S.; Carter, Philip E.; Midwinter, Anne C.; Mather, Alison E.; French, Nigel P.

2017-01-01

During 1998–2012, an extended outbreak of Salmonella enterica serovar Typhimurium definitive type 160 (DT160) affected >3,000 humans and killed wild birds in New Zealand. However, the relationship between DT160 within these 2 host groups and the origin of the outbreak are unknown. Whole-genome sequencing was used to compare 109 Salmonella Typhimurium DT160 isolates from sources throughout New Zealand. We provide evidence that DT160 was introduced into New Zealand around 1997 and rapidly propagated throughout the country, becoming more genetically diverse over time. The genetic heterogeneity was evenly distributed across multiple predicted functional protein groups, and we found no evidence of host group differentiation between isolates collected from human, poultry, bovid, and wild bird sources, indicating ongoing transmission between these host groups. Our findings demonstrate how a comparative genomic approach can be used to gain insight into outbreaks, disease transmission, and the evolution of a multihost pathogen after a probable point-source introduction. PMID:28516864

Inferences from the Chronology of Dengue and Zika Outbreaks in Human Populations

NASA Astrophysics Data System (ADS)

McDonald, C.; Usmani, M.; Colwell, R. R.; Jutla, A.

2017-12-01

Dengue and Zika virus are becoming global health threats. With a recent resurgence of Zika virus in the Americas, there is a renewed interest to understand the physical pathways on interactions of vectors with human population. However, the challenge is in the availability of the vectors and viruses in regions that have suffered from outbreaks of these infections. Aedes spp. mosquitoes are the primary vectors of both Zika and Dengue viruses. The critical question is how one species of mosquito is able to transmit two different infections. Therefore, there is a need to understand the coherence and co-emergence behavior of Dengue and Zika infections. Our dominant hypothesis is that Dengue precedes Zika viruses. Here, we will show a global chronological trend of Dengue and Zika virus, or how an outbreak of dengue may lead to an outbreak of Zika virus, as regions with Zika virus outbreaks had demonstrated peak dengue incidences in prior months. We will also present global trends on key climatological and weather processes as a function of the emergence of these two viruses. We anticipate that this information can be used concurrently with geographical and meteorological information to more accurately predict the spread of Zika virus.

Innovative technology for web-based data management during an outbreak

PubMed Central

Mukhi, Shamir N; Chester, Tammy L Stuart; Klaver-Kibria, Justine DA; Nowicki, Deborah L; Whitlock, Mandy L; Mahmud, Salah M; Louie, Marie; Lee, Bonita E

2011-01-01

Lack of automated and integrated data collection and management, and poor linkage of clinical, epidemiological and laboratory data during an outbreak can inhibit effective and timely outbreak investigation and response. This paper describes an innovative web-based technology, referred to as Web Data, developed for the rapid set-up and provision of interactive and adaptive data management during outbreak situations. We also describe the benefits and limitations of the Web Data technology identified through a questionnaire that was developed to evaluate the use of Web Data implementation and application during the 2009 H1N1 pandemic by Winnipeg Regional Health Authority and Provincial Laboratory for Public Health of Alberta. Some of the main benefits include: improved and secure data access, increased efficiency and reduced error, enhanced electronic collection and transfer of data, rapid creation and modification of the database, conversion of specimen-level to case-level data, and user-defined data extraction and query capabilities. Areas requiring improvement include: better understanding of privacy policies, increased capability for data sharing and linkages between jurisdictions to alleviate data entry duplication. PMID:23569597

Fiber optic distributed temperature sensing for fire source localization

NASA Astrophysics Data System (ADS)

Sun, Miao; Tang, Yuquan; Yang, Shuang; Sigrist, Markus W.; Li, Jun; Dong, Fengzhong

2017-08-01

A method for localizing a fire source based on a distributed temperature sensor system is proposed. Two sections of optical fibers were placed orthogonally to each other as the sensing elements. A tray of alcohol was lit to act as a fire outbreak in a cabinet with an uneven ceiling to simulate a real scene of fire. Experiments were carried out to demonstrate the feasibility of the method. Rather large fluctuations and systematic errors with respect to predicting the exact room coordinates of the fire source caused by the uneven ceiling were observed. Two mathematical methods (smoothing recorded temperature curves and finding temperature peak positions) to improve the prediction accuracy are presented, and the experimental results indicate that the fluctuation ranges and systematic errors are significantly reduced. The proposed scheme is simple and appears reliable enough to locate a fire source in large spaces.

Viewing Marine Bacteria, Their Activity and Response to Environmental Drivers from Orbit

PubMed Central

Grimes, D. Jay; Ford, Tim E.; Colwell, Rita R.; Baker-Austin, Craig; Martinez-Urtaza, Jaime; Subramaniam, Ajit; Capone, Douglas G.

2014-01-01

Satellite-based remote sensing of marine microorganisms has become a useful tool in predicting human health risks associated with these microscopic targets. Early applications were focused on harmful algal blooms, but more recently methods have been developed to interrogate the ocean for bacteria. As satellite-based sensors have become more sophisticated and our ability to interpret information derived from these sensors has advanced, we have progressed from merely making fascinating pictures from space to developing process models with predictive capability. Our understanding of the role of marine microorganisms in primary production and global elemental cycles has been vastly improved as has our ability to use the combination of remote sensing data and models to provide early warning systems for disease outbreaks. This manuscript will discuss current approaches to monitoring cyanobacteria and vibrios, their activity and response to environmental drivers, and will also suggest future directions. PMID:24477922

Morbidity Forecast in Cities: A Study of Urban Air Pollution and Respiratory Diseases in the Metropolitan Region of Curitiba, Brazil.

PubMed

de Souza, Fabio Teodoro

2018-05-29

In the last two decades, urbanization has intensified, and in Brazil, about 90% of the population now lives in urban centers. Atmospheric patterns have changed owing to the high growth rate of cities, with negative consequences for public health. This research aims to elucidate the spatial patterns of air pollution and respiratory diseases. A data-based model to aid local urban management to improve public health policies concerning air pollution is described. An example of data preparation and multivariate analysis with inventories from different cities in the Metropolitan Region of Curitiba was studied. A predictive model with outstanding accuracy in prediction of outbreaks was developed. Preliminary results describe relevant relations among morbidity scales, air pollution levels, and atmospheric seasonal patterns. The knowledge gathered here contributes to the debate on social issues and public policies. Moreover, the results of this smaller scale study can be extended to megacities.

Viewing marine bacteria, their activity and response to environmental drivers from orbit: satellite remote sensing of bacteria.

PubMed

Grimes, D Jay; Ford, Tim E; Colwell, Rita R; Baker-Austin, Craig; Martinez-Urtaza, Jaime; Subramaniam, Ajit; Capone, Douglas G

2014-04-01

Satellite-based remote sensing of marine microorganisms has become a useful tool in predicting human health risks associated with these microscopic targets. Early applications were focused on harmful algal blooms, but more recently methods have been developed to interrogate the ocean for bacteria. As satellite-based sensors have become more sophisticated and our ability to interpret information derived from these sensors has advanced, we have progressed from merely making fascinating pictures from space to developing process models with predictive capability. Our understanding of the role of marine microorganisms in primary production and global elemental cycles has been vastly improved as has our ability to use the combination of remote sensing data and models to provide early warning systems for disease outbreaks. This manuscript will discuss current approaches to monitoring cyanobacteria and vibrios, their activity and response to environmental drivers, and will also suggest future directions.

Enhancing Surveillance and Diagnostics in Anthrax-Endemic Countries

PubMed Central

Salzer, Johanna S.; Traxler, Rita M.; Hendricks, Katherine A.; Kadzik, Melissa E.; Marston, Chung K.; Kolton, Cari B.; Stoddard, Robyn A.; Hoffmaster, Alex R.; Bower, William A.; Walke, Henry T.

2017-01-01

Naturally occurring anthrax disproportionately affects the health and economic welfare of poor, rural communities in anthrax-endemic countries. However, many of these countries have limited anthrax prevention and control programs. Effective prevention of anthrax outbreaks among humans is accomplished through routine livestock vaccination programs and prompt response to animal outbreaks. The Centers for Disease Control and Prevention uses a 2-phase framework when providing technical assistance to partners in anthrax-endemic countries. The first phase assesses and identifies areas for improvement in existing human and animal surveillance, laboratory diagnostics, and outbreak response. The second phase provides steps to implement improvements to these areas. We describe examples of implementing this framework in anthrax-endemic countries. These activities are at varying stages of completion; however, the public health impact of these initiatives has been encouraging. The anthrax framework can be extended to other zoonotic diseases to build on these efforts, improve human and animal health, and enhance global health security. PMID:29155651

Bio-ALIRT biosurveillance detection algorithm evaluation.

PubMed

Siegrist, David; Pavlin, J

2004-09-24

Early detection of disease outbreaks by a medical biosurveillance system relies on two major components: 1) the contribution of early and reliable data sources and 2) the sensitivity, specificity, and timeliness of biosurveillance detection algorithms. This paper describes an effort to assess leading detection algorithms by arranging a common challenge problem and providing a common data set. The objectives of this study were to determine whether automated detection algorithms can reliably and quickly identify the onset of natural disease outbreaks that are surrogates for possible terrorist pathogen releases, and do so at acceptable false-alert rates (e.g., once every 2-6 weeks). Historic de-identified data were obtained from five metropolitan areas over 23 months; these data included International Classification of Diseases, Ninth Revision (ICD-9) codes related to respiratory and gastrointestinal illness syndromes. An outbreak detection group identified and labeled two natural disease outbreaks in these data and provided them to analysts for training of detection algorithms. All outbreaks in the remaining test data were identified but not revealed to the detection groups until after their analyses. The algorithms established a probability of outbreak for each day's counts. The probability of outbreak was assessed as an "actual" alert for different false-alert rates. The best algorithms were able to detect all of the outbreaks at false-alert rates of one every 2-6 weeks. They were often able to detect for the same day human investigators had identified as the true start of the outbreak. Because minimal data exists for an actual biologic attack, determining how quickly an algorithm might detect such an attack is difficult. However, application of these algorithms in combination with other data-analysis methods to historic outbreak data indicates that biosurveillance techniques for analyzing syndrome counts can rapidly detect seasonal respiratory and gastrointestinal illness outbreaks. Further research is needed to assess the value of electronic data sources for predictive detection. In addition, simulations need to be developed and implemented to better characterize the size and type of biologic attack that can be detected by current methods by challenging them under different projected operational conditions.

The Methanol Poisoning Outbreaks in Libya 2013 and Kenya 2014.

PubMed

Rostrup, Morten; Edwards, Jeffrey K; Abukalish, Mohamed; Ezzabi, Masoud; Some, David; Ritter, Helga; Menge, Tom; Abdelrahman, Ahmed; Rootwelt, Rebecca; Janssens, Bart; Lind, Kyrre; Paasma, Raido; Hovda, Knut Erik

2016-01-01

Outbreaks of methanol poisoning occur frequently on a global basis, affecting poor and vulnerable populations. Knowledge regarding methanol is limited, likely many cases and even outbreaks go unnoticed, with patients dying unnecessarily. We describe findings from the first three large outbreaks of methanol poisoning where Médecins Sans Frontières (MSF) responded, and evaluate the benefits of a possible future collaboration between local health authorities, a Non-Governmental Organisation and international expertise. Retrospective study of three major methanol outbreaks in Libya (2013) and Kenya (May and July 2014). Data were collected from MSF field personnel, local health personnel, hospital files, and media reports. In Tripoli, Libya, over 1,000 patients were poisoned with a reported case fatality rate of 10% (101/1,066). In Kenya, two outbreaks resulted in approximately 341 and 126 patients, with case fatality rates of 29% (100/341) and 21% (26/126), respectively. MSF launched an emergency team with international experts, medications and equipment, however, the outbreaks were resolving by the time of arrival. Recognition of an outbreak of methanol poisoning and diagnosis seem to be the most challenging tasks, with significant delay from time of first presentations to public health warnings being issued. In spite of the rapid response from an emergency team, the outbreaks were nearly concluded by the time of arrival. A major impact on the outcome was not seen, but large educational trainings were conducted to increase awareness and knowledge about methanol poisoning. Based on this training, MSF was able to send a local emergency team during the second outbreak, supporting that such an approach could improve outcomes. Basic training, simplified treatment protocols, point-of-care diagnostic tools, and early support when needed, are likely the most important components to impact the consequences of methanol poisoning outbreaks in these challenging contexts.

Predicting live and dead tree basal area of bark beetle affected forests from discrete-return lidar

Treesearch

Benjamin C. Bright; Andrew T. Hudak; Robert McGaughey; Hans-Erik Andersen; Jose Negron

2013-01-01

Bark beetle outbreaks have killed large numbers of trees across North America in recent years. Lidar remote sensing can be used to effectively estimate forest biomass, but prediction of both live and dead standing biomass in beetle-affected forests using lidar alone has not been demonstrated. We developed Random Forest (RF) models predicting total, live, dead, and...

Transmission characteristics of different students during a school outbreak of (H1N1) pdm09 influenza in China, 2009.

PubMed

Wang, Ligui; Chu, Chenyi; Yang, Guang; Hao, Rongzhang; Li, Zhenjun; Cao, Zhidong; Qiu, Shaofu; Li, Peng; Wu, Zhihao; Yuan, Zhengquan; Xu, Yuanyong; Zeng, Dajun; Wang, Yong; Song, Hongbin

2014-08-07

Many outbreaks of A(H1N1)pdm09 influenza have occurred in schools with a high population density. Containment of school outbreaks is predicted to help mitigate pandemic influenza. Understanding disease transmission characteristics within the school setting is critical to implementing effective control measures. Based on a school outbreak survey, we found almost all (93.7%) disease transmission occurred within a single grade, only 6.3% crossed grades. Transmissions originating from freshmen exhibited a star-shaped network; other grades exhibited branch- or line-shaped networks, indicating freshmen have higher activity and are more likely to cause infection. R0 for freshmen, calculated as 2.04, estimated as 2.76, was greater than for other grades (P < 0.01). Without intervention, the estimated number of cases was much greater when the outbreak was initiated by freshmen than by other grades. Furthermore, the estimated number of cases required to be under quarantine and isolation for freshmen was less than that of equivalent other grades. So we concluded that different grades have different transmission mode. Freshmen were the main facilitators of the spread of A(H1N1)pdm09 influenza during this school outbreak, so control measures (e.g. close contact isolation) priority used for freshmen would likely have effectively reduced spread of influenza in school settings.

Ecosystem carbon exchange in response to locust outbreaks in a temperate steppe.

PubMed

Song, Jian; Wu, Dandan; Shao, Pengshuai; Hui, Dafeng; Wan, Shiqiang

2015-06-01

It is predicted that locust outbreaks will occur more frequently under future climate change scenarios, with consequent effects on ecological goods and services. A field manipulative experiment was conducted to examine the responses of gross ecosystem productivity (GEP), net ecosystem carbon dioxide (CO2) exchange (NEE), ecosystem respiration (ER), and soil respiration (SR) to locust outbreaks in a temperate steppe of northern China from 2010 to 2011. Two processes related to locust outbreaks, natural locust feeding and carcass deposition, were mimicked by clipping 80 % of aboveground biomass and adding locust carcasses, respectively. Ecosystem carbon (C) exchange (i.e., GEP, NEE, ER, and SR) was suppressed by locust feeding in 2010, but stimulated by locust carcass deposition in both years (except SR in 2011). Experimental locust outbreaks (i.e., clipping plus locust carcass addition) decreased GEP and NEE in 2010 whereas they increased GEP, NEE, and ER in 2011, leading to neutral changes in GEP, NEE, and SR across the 2 years. The responses of ecosystem C exchange could have been due to the changes in soil ammonium nitrogen, community cover, and aboveground net primary productivity. Our findings of the transient and neutral changes in ecosystem C cycling under locust outbreaks highlight the importance of resistance, resilience, and stability of the temperate steppe in maintaining reliable ecosystem services, and facilitate the projections of ecosystem functioning in response to natural disturbance and climate change.

Infection prevention and control of the Ebola outbreak in Liberia, 2014-2015: key challenges and successes.

PubMed

Cooper, Catherine; Fisher, Dale; Gupta, Neil; MaCauley, Rose; Pessoa-Silva, Carmem L

2016-01-05

Prior to the 2014-2015 Ebola outbreak, infection prevention and control (IPC) activities in Liberian healthcare facilities were basic. There was no national IPC guidance, nor dedicated staff at any level of government or healthcare facility (HCF) to ensure the implementation of best practices. Efforts to improve IPC early in the outbreak were ad hoc and messaging was inconsistent. In September 2014, at the height of the outbreak, the national IPC Task Force was established with a Ministry of Health (MoH) mandate to coordinate IPC response activities. A steering group of the Task Force, including representatives of the World Health Organization (WHO) and the United States Centers for Disease Control and Prevention (CDC), supported MoH leadership in implementing standardized messaging and IPC training for the health workforce. This structure, and the activities implemented under this structure, played a crucial role in the implementation of IPC practices and successful containment of the outbreak. Moving forward, a nationwide culture of IPC needs to be maintained through this governance structure in Liberia's health system to prevent and respond to future outbreaks.

Invasive Species Distribution Modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?

Treesearch

Tomáš Václavík; Ross K. Meentemeyer

2009-01-01

Species distribution models (SDMs) based on statistical relationships between occurrence data and underlying environmental conditions are increasingly used to predict spatial patterns of biological invasions and prioritize locations for early detection and control of invasion outbreaks. However, invasive species distribution models (iSDMs) face special challenges...

Hand-hygiene compliance does not predict rates of resistant infections in critically ill surgical patients.

PubMed

Jayaraman, Sudha P; Klompas, Michael; Bascom, Molli; Liu, Xiaoxia; Piszcz, Regina; Rogers, Selwyn O; Askari, Reza

2014-10-01

Our institution had a major outbreak of multi-drug-resistant Acinetobacter (MDRA) in its general surgical and trauma intensive care units (ICUs) in 2011, requiring implementation of an aggressive infection-control response. We hypothesized that poor hand-hygiene compliance (HHC) may have contributed to the outbreak of MDRA. A response to the outbreak including aggressive environmental cleaning, cohorting, and increased hand hygiene compliance monitoring may have led to an increase in HHC after the outbreak and to a consequent decrease in the rates of infection by the nosocomial pathogens methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), and Clostridium difficile. Hand-hygiene compliance, tracked in monthly audits by trained and anonymous observers, was abstracted from an infection control database. The incidences of nosocomial MRSA, VRE, and C. difficile were calculated from a separate prospectively collected data base for 6 mo before and 12 mo after the 2011 outbreak of MDRA in the institution's general surgical and trauma ICUs, and data collected prospectively from two unaffected ICUs (the thoracic surgical ICU and medical intensive care unit [MICU]). We created a composite endpoint of "any resistant pathogen," defined as MRSA, VRE, or C. difficile, and compared incidence rates over time, using the Wilcoxon signed rank test and Pearson product-moment correlation coefficient to measure the correlations among these rates. Rates of HHC before and after the outbreak of MDRA were consistently high in both the general surgical (median rates: 100% before and 97.6% after the outbreak, p=0.93) and trauma ICUs (median rates: 90% before and 96.75% after the outbreak, p=0.14). In none of the ICUs included in the study did the rates of HHC increase in response to the outbreak of MDRA. The incidence of "any resistant pathogen" decreased in the general surgical ICU after the outbreak (from 6.7/1,000 patient-days before the outbreak to 2.7/1,000 patient-days after the outbreak, p=0.04), but this decrease did not correlate with HHC (trauma ICU: Pearson correlation [ρ]=-0.34, p=0.28; general surgical ICU: ρ=0.52, p=0.08). The 2011 outbreak of MDRA at our institution occurred despite high rates of HHC. Notwithstanding stable rates of HHC, the rates of infection with MRSA, VRE and C. difficile decreased in the general surgical ICU after the outbreak. This suggests that infection control tactics other than HHC play a crucial role in preventing the transmission of nosocomial pathogens, especially when rates of HHC have been maximized.

Implementing systems thinking for infection prevention: The cessation of repeated scabies outbreaks in a respiratory care ward.

PubMed

Chuang, Sheuwen; Howley, Peter P; Lin, Shih-Hua

2015-05-01

Root cause analysis (RCA) is often adopted to complement epidemiologic investigation for outbreaks and infection-related adverse events in hospitals; however, RCA has been argued to have limited effectiveness in preventing such events. We describe how an innovative systems analysis approach halted repeated scabies outbreaks, and highlight the importance of systems thinking for outbreaks analysis and sustaining effective infection prevention and control. Following RCA for a third successive outbreak of scabies over a 17-month period in a 60-bed respiratory care ward of a Taiwan hospital, a systems-oriented event analysis (SOEA) model was used to reanalyze the outbreak. Both approaches and the recommendations were compared. No nosocomial scabies have been reported for more than 1975 days since implementation of the SOEA. Previous intervals between seeming eradication and repeat outbreaks following RCA were 270 days and 180 days. Achieving a sustainable positive resolution relied on applying systems thinking and the holistic analysis of the system, not merely looking for root causes of events. To improve the effectiveness of outbreaks analysis and infection control, an emphasis on systems thinking is critical, along with a practical approach to ensure its effective implementation. The SOEA model provides the necessary framework and is a viable complementary approach, or alternative, to RCA. Copyright © 2015 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Connecting Environmental Observations with Cholera Outbreaks in Bangladesh

NASA Astrophysics Data System (ADS)

Stack, D.; Sandborn, A.; Widmeyer, P. A.; Escobar, V. M.

2011-12-01

Research has demonstrated that cholera epidemics in Bangladesh occur seasonally. This bimodal outbreak pattern closely follows times when large monsoon events are most frequent (spring and fall). While these patterns are presented in regional data, this knowledge alone cannot forecast the severity and location of cholera outbreaks until a monsoon event occurs, or an outbreak is reported. Therefore, there can only be reactive responses to cholera outbreaks. A heightened understanding of the link between environmental factors and outbreak occurrence will greatly enhance disease management capabilities. A predictive tool capable of giving an advanced warning of the environmental hazards that lead to location specific outbreaks allows for proactive and preventative responses, minimizing negative effects. A specific goal of this research was to relate latitude-longitude data with existing points associated with V. cholerae human case data collected from four cities in Bangladesh. Remotely sensed products were used to better understand the correlation between human outbreak occurrences, chlorophyll-a estimates, sea surface temperature (SST), and rainfall. Using MODIS, SeaWiFS, and TRMM satellite data, a gridded regional image was developed. Correlation analyses of the data were studied within the context of geographically diverse locations for the four cities of interest. Seasonal relationships were found between the cholera case data and all three of the chosen remotely sensed parameters. The strongest correlation found was between chlorophyll-a concentrations and reported human cases. The primary deliverable of this project was the production of an interactive Google Earth base map for use in a pilot design study that will lead to the development of applications to connect earth science products with water and health studies. The base map, with its inherent value of merging remotely sensed data with in situ observation points, can be used as a basis for constructing better models of disease outbreaks. This effort will build upon current research at University of Maryland, College Park, which focuses on the impacts of climate on both water and health.

DEFENDER: Detecting and Forecasting Epidemics Using Novel Data-Analytics for Enhanced Response

PubMed Central

Simmie, Donal; Hankin, Chris; Gillard, Joseph

2016-01-01

In recent years social and news media have increasingly been used to explain patterns in disease activity and progression. Social media data, principally from the Twitter network, has been shown to correlate well with official disease case counts. This fact has been exploited to provide advance warning of outbreak detection, forecasting of disease levels and the ability to predict the likelihood of individuals developing symptoms. In this paper we introduce DEFENDER, a software system that integrates data from social and news media and incorporates algorithms for outbreak detection, situational awareness and forecasting. As part of this system we have developed a technique for creating a location network for any country or region based purely on Twitter data. We also present a disease nowcasting (forecasting the current but still unknown level) approach which leverages counts from multiple symptoms, which was found to improve the nowcasting accuracy by 37 percent over a model that used only previous case data. Finally we attempt to forecast future levels of symptom activity based on observed user movement on Twitter, finding a moderate gain of 5 percent over a time series forecasting model. PMID:27192059

Outbreaks by canopy-feeding geometrid moth cause state-dependent shifts in understorey plant communities.

PubMed

Karlsen, Stein Rune; Jepsen, Jane Uhd; Odland, Arvid; Ims, Rolf Anker; Elvebakk, Arve

2013-11-01

The increased spread of insect outbreaks is among the most severe impacts of climate warming predicted for northern boreal forest ecosystems. Compound disturbances by insect herbivores can cause sharp transitions between vegetation states with implications for ecosystem productivity and climate feedbacks. By analysing vegetation plots prior to and immediately after a severe and widespread outbreak by geometrid moths in the birch forest-tundra ecotone, we document a shift in forest understorey community composition in response to the moth outbreak. Prior to the moth outbreak, the plots divided into two oligotrophic and one eutrophic plant community. The moth outbreak caused a vegetation state shift in the two oligotrophic communities, but only minor changes in the eutrophic community. In the spatially most widespread communities, oligotrophic dwarf shrub birch forest, dominance by the allelopathic dwarf shrub Empetrum nigrum ssp. hermaphroditum, was effectively broken and replaced by a community dominated by the graminoid Avenella flexuosa, in a manner qualitatively similar to the effect of wild fires in E. nigrum communities in coniferous boreal forest further south. As dominance by E. nigrum is associated with retrogressive succession the observed vegetation state shift has widespread implications for ecosystem productivity on a regional scale. Our findings reveal that the impact of moth outbreaks on the northern boreal birch forest system is highly initial-state dependent, and that the widespread oligotrophic communities have a low resistance to such disturbances. This provides a case for the notion that climate impacts on arctic and northern boreal vegetation may take place most abruptly when conveyed by changed dynamics of irruptive herbivores.

Ecological niche modelling of potential West Nile virus vector mosquito species and their geographical association with equine epizootics in Italy.

PubMed

Mughini-Gras, Lapo; Mulatti, Paolo; Severini, Francesco; Boccolini, Daniela; Romi, Roberto; Bongiorno, Gioia; Khoury, Cristina; Bianchi, Riccardo; Montarsi, Fabrizio; Patregnani, Tommaso; Bonfanti, Lebana; Rezza, Giovanni; Capelli, Gioia; Busani, Luca

2014-01-01

In Italy, West Nile virus (WNV) equine outbreaks have occurred annually since 2008. Characterizing WNV vector habitat requirements allows for the identification of areas at risk of viral amplification and transmission. Maxent-based ecological niche models were developed using literature records of 13 potential WNV Italian vector mosquito species to predict their habitat suitability range and to investigate possible geographical associations with WNV equine outbreak occurrence in Italy from 2008 to 2010. The contribution of different environmental variables to the niche models was also assessed. Suitable habitats for Culex pipiens, Aedes albopictus, and Anopheles maculipennis were widely distributed; Culex modestus, Ochlerotatus geniculatus, Ochlerotatus caspius, Coquillettidia richiardii, Aedes vexans, and Anopheles plumbeus were concentrated in north-central Italy; Aedes cinereus, Culex theileri, Ochlerotatus dorsalis, and Culiseta longiareolata were restricted to coastal/southern areas. Elevation, temperature, and precipitation variables showed the highest predictive power. Host population and landscape variables provided minor contributions. WNV equine outbreaks had a significantly higher probability to occur in habitats suitable for Cx. modestus and Cx. pipiens, providing circumstantial evidence that the potential distribution of these two species coincides geographically with the observed distribution of the disease in equines.

Bayesian data assimilation provides rapid decision support for vector-borne diseases

PubMed Central

Jewell, Chris P.; Brown, Richard G.

2015-01-01

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host–vector–pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. PMID:26136225

Mapping and detecting bark beetle-caused tree mortality in the western United States

NASA Astrophysics Data System (ADS)

Meddens, Arjan J. H.

Recently, insect outbreaks across North America have dramatically increased and the forest area affected by bark beetles is similar to that affected by fire. Remote sensing offers the potential to detect insect outbreaks with high accuracy. Chapter one involved detection of insect-caused tree mortality on the tree level for a 90km2 area in northcentral Colorado. Classes of interest included green trees, multiple stages of post-insect attack tree mortality including dead trees with red needles ("red-attack") and dead trees without needles ("gray-attack"), and non-forest. The results illustrated that classification of an image with a spatial resolution similar to the area of a tree crown outperformed that from finer and coarser resolution imagery for mapping tree mortality and non-forest classes. I also demonstrated that multispectral imagery could be used to separate multiple postoutbreak attack stages (i.e., red-attack and gray-attack) from other classes in the image. In Chapter 2, I compared and improved methods for detecting bark beetle-caused tree mortality using medium-resolution satellite data. I found that overall classification accuracy was similar between single-date and multi-date classification methods. I developed regression models to predict percent red attack within a 30-m grid cell and these models explained >75% of the variance using three Landsat spectral explanatory variables. Results of the final product showed that approximately 24% of the forest within the Landsat scene was comprised of tree mortality caused by bark beetles. In Chapter 3, I developed a gridded data set with 1-km2 resolution using aerial survey data and improved estimates of tree mortality across the western US and British Columbia. In the US, I also produced an upper estimate by forcing the mortality area to match that from high-resolution imagery in Idaho, Colorado, and New Mexico. Cumulative mortality area from all bark beetles was 5.46 Mha in British Columbia in 2001-2010 and 0.47-5.37 Mha (lower and upper estimate) in the western conterminous US during 1997-2010. Improved methods for detection and mapping of insect outbreak areas will lead to improved assessments of the effects of these forest disturbances on the economy, carbon cycle (and feedback to climate change), fuel loads, hydrology and forest ecology.

Performance of statistical process control methods for regional surgical site infection surveillance: a 10-year multicentre pilot study.

PubMed

Baker, Arthur W; Haridy, Salah; Salem, Joseph; Ilieş, Iulian; Ergai, Awatef O; Samareh, Aven; Andrianas, Nicholas; Benneyan, James C; Sexton, Daniel J; Anderson, Deverick J

2017-11-24

Traditional strategies for surveillance of surgical site infections (SSI) have multiple limitations, including delayed and incomplete outbreak detection. Statistical process control (SPC) methods address these deficiencies by combining longitudinal analysis with graphical presentation of data. We performed a pilot study within a large network of community hospitals to evaluate performance of SPC methods for detecting SSI outbreaks. We applied conventional Shewhart and exponentially weighted moving average (EWMA) SPC charts to 10 previously investigated SSI outbreaks that occurred from 2003 to 2013. We compared the results of SPC surveillance to the results of traditional SSI surveillance methods. Then, we analysed the performance of modified SPC charts constructed with different outbreak detection rules, EWMA smoothing factors and baseline SSI rate calculations. Conventional Shewhart and EWMA SPC charts both detected 8 of the 10 SSI outbreaks analysed, in each case prior to the date of traditional detection. Among detected outbreaks, conventional Shewhart chart detection occurred a median of 12 months prior to outbreak onset and 22 months prior to traditional detection. Conventional EWMA chart detection occurred a median of 7 months prior to outbreak onset and 14 months prior to traditional detection. Modified Shewhart and EWMA charts additionally detected several outbreaks earlier than conventional SPC charts. Shewhart and SPC charts had low false-positive rates when used to analyse separate control hospital SSI data. Our findings illustrate the potential usefulness and feasibility of real-time SPC surveillance of SSI to rapidly identify outbreaks and improve patient safety. Further study is needed to optimise SPC chart selection and calculation, statistical outbreak detection rules and the process for reacting to signals of potential outbreaks. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Behavioural phenotypes predict disease susceptibility and infectiousness

PubMed Central

Araujo, Alessandra; Kirschman, Lucas

2016-01-01

Behavioural phenotypes may provide a means for identifying individuals that disproportionally contribute to disease spread and epizootic outbreaks. For example, bolder phenotypes may experience greater exposure and susceptibility to pathogenic infection because of distinct interactions with conspecifics and their environment. We tested the value of behavioural phenotypes in larval amphibians for predicting ranavirus transmission in experimental trials. We found that behavioural phenotypes characterized by latency-to-food and swimming profiles were predictive of disease susceptibility and infectiousness defined as the capacity of an infected host to transmit an infection by contacts. While viral shedding rates were positively associated with transmission, we also found an inverse relationship between contacts and infections. Together these results suggest intrinsic traits that influence behaviour and the quantity of pathogens shed during conspecific interactions may be an important contributor to ranavirus transmission. These results suggest that behavioural phenotypes provide a means to identify individuals more likely to spread disease and thus give insights into disease outbreaks that threaten wildlife and humans. PMID:27555652

Behavioural phenotypes predict disease susceptibility and infectiousness.

PubMed

Araujo, Alessandra; Kirschman, Lucas; Warne, Robin W

2016-08-01

Behavioural phenotypes may provide a means for identifying individuals that disproportionally contribute to disease spread and epizootic outbreaks. For example, bolder phenotypes may experience greater exposure and susceptibility to pathogenic infection because of distinct interactions with conspecifics and their environment. We tested the value of behavioural phenotypes in larval amphibians for predicting ranavirus transmission in experimental trials. We found that behavioural phenotypes characterized by latency-to-food and swimming profiles were predictive of disease susceptibility and infectiousness defined as the capacity of an infected host to transmit an infection by contacts. While viral shedding rates were positively associated with transmission, we also found an inverse relationship between contacts and infections. Together these results suggest intrinsic traits that influence behaviour and the quantity of pathogens shed during conspecific interactions may be an important contributor to ranavirus transmission. These results suggest that behavioural phenotypes provide a means to identify individuals more likely to spread disease and thus give insights into disease outbreaks that threaten wildlife and humans. © 2016 The Author(s).

The impact of resource limitation and the phenology of parasitoid attack on the duration of insect herbivore outbreaks.

PubMed

Umbanhowar, James; Hastings, Alan

2002-11-01

Fluctuations in resource quality and quantity, and changes in mortality due to predators and parasites are thought to be of prime importance in the regular fluctuations of forest insects. We examine how food limitation and parasitoids with different phenologies of attack regulate the population cycles of insect hosts. Our analysis of the limit cycle of a model with a biologically realistic form of density dependence in the host yields two novel predictions. First, outbreaks will typically last for only 2 generations after parasitoids begin to reduce the host population below the maximum density. Second, host growth rate is important in determining cycle length only when parasitoids attack before the impacts of resource limitation affect the host. The robustness of these predictions are tested using a more general form of density dependence in the host, revealing that our predictions are valid as long as density dependence in the host is not too overcompensatory.

Use of Bioclimatic Factors to Determine Potential Niche of Vaccinia Virus, an Emerging and Zoonotic Pathogen

NASA Astrophysics Data System (ADS)

Quiner, C. A.; Nakazawa, Y.

2017-12-01

Emerging and understudied pathogens often lack information that most commonly used analytical tools require, such as negative controls or baseline data making public health control of emerging pathogens challenging. In lieu of opportunities to collect more data from larger outbreaks or formal epidemiological studies, new analytical strategies, merging case data with publically available datasets, can be used to understand transmission patterns and drivers of disease emergence. Zoonotic infections with Vaccinia virus (VACV) were first reported in Brazil in 1999, VACV is an emerging zoonotic Orthopoxvirus, which primarily infects dairy cattle and farmers in close contact with infected cows. Prospective studies of emerging pathogens could provide critical data that would inform public health planning and response to outbreaks. By using the location of 87-recorded outbreaks and publicly available bioclimatic data we demonstrate one such approach. Using an Ecological Niche Model (ENM), we identify the environmental conditions under which VACV outbreaks have occurred, and determine additional locations in two affected South American countries that may be susceptible to transmission. Further, we show how suitability for the virus responds to different levels of various environmental factors and highlight the most important climatic factors in determining its transmission. The final ENM predicted all areas where Brazilian outbreaks occurred, two out of five Colombian outbreaks and identified new regions within Brazil that are suitable for transmission based on bioclimatic factors. Further, the most important factors in determining transmission suitability are precipitation of the wettest quarter, annual precipitation, mean temperature of the coldest quarter and mean diurnal range. The analyses here provide a means by which to study patterns of an emerging infectious disease, and regions that are potentially at risk for it, in spite of the paucity of critical data. Policy and methods for the control of infectious diseases often use a reactionary model, addressing diseases only after significant impact on human health has ensued. Here, we provide a means to predict where the disease is likely to appear, providing a map for directed intervention.

Herd diagnosis of low pathogen diarrhoea in growing pigs - a pilot study.

PubMed

Pedersen, Ken Steen; Johansen, Markku; Angen, Oystein; Jorsal, Sven Erik; Nielsen, Jens Peter; Jensen, Tim K; Guedes, Roberto; Ståhl, Marie; Bækbo, Poul

2014-01-01

The major indication for antibiotic use in Danish pigs is treatment of intestinal diseases post weaning. Clinical decisions on antibiotic batch medication are often based on inspection of diarrhoeic pools on the pen floor. In some of these treated diarrhoea outbreaks, intestinal pathogens can only be demonstrated in a small number of pigs within the treated group (low pathogen diarrhoea). Termination of antibiotic batch medication in herds suffering from such diarrhoea could potentially reduce the consumption of antibiotics in the pig industry. The objective of the present pilot study was to suggest criteria for herd diagnosis of low pathogen diarrhoea in growing pigs. Data previously collected from 20 Danish herds were used to create a case series of clinical diarrhoea outbreaks normally subjected to antibiotic treatment. In the present study, these diarrhoea outbreaks were classified as low pathogen (<15% of the pigs having bacterial intestinal disease) (n =5 outbreaks) or high pathogen (≥15% of the pigs having bacterial intestinal disease) (n =15 outbreaks). Based on the case series, different diagnostic procedures were explored, and criteria for herd diagnosis of low pathogen diarrhoea were suggested. The effect of sampling variation was explored by simulation. The diagnostic procedure with the highest combined herd-level sensitivity and specificity was qPCR testing of a pooled sample containing 20 randomly selected faecal samples. The criteria for a positive test result (high pathogen diarrhoea outbreak) were an average of 1.5 diarrhoeic faecal pools on the floor of each pen in the room under investigation and a pathogenic bacterial load ≥35,000 per gram in the faecal pool tested by qPCR. The bacterial load was the sum of Lawsonia intracellularis, Brachyspira pilosicoli and Escherichia coli F4 and F18 bacteria per gram faeces. The herd-diagnostic performance was (herd-level) diagnostic sensitivity =0.99, diagnostic specificity =0.80, positive predictive value =0.94 and negative predictive value =0.96. The pilot study suggests criteria for herd diagnosis of low pathogen diarrhoea in growing pigs. The suggested criteria should now be evaluated, and the effect of terminating antibiotic batch medication in herds identified as suffering from low pathogen diarrhoea should be explored.

Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review.

PubMed

Charles-Smith, Lauren E; Reynolds, Tera L; Cameron, Mark A; Conway, Mike; Lau, Eric H Y; Olsen, Jennifer M; Pavlin, Julie A; Shigematsu, Mika; Streichert, Laura C; Suda, Katie J; Corley, Courtney D

2015-01-01

Research studies show that social media may be valuable tools in the disease surveillance toolkit used for improving public health professionals' ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. A social media work group, consisting of surveillance practitioners, academic researchers, and other subject matter experts convened by the International Society for Disease Surveillance, conducted a systematic primary literature review using the PRISMA framework to identify research, published through February 2013, answering either of the following questions: Can social media be integrated into disease surveillance practice and outbreak management to support and improve public health?Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes?Examples of social media included are Facebook, MySpace, microblogs (e.g., Twitter), blogs, and discussion forums. For Question 1, 33 manuscripts were identified, starting in 2009 with topics on Influenza-like Illnesses (n = 15), Infectious Diseases (n = 6), Non-infectious Diseases (n = 4), Medication and Vaccines (n = 3), and Other (n = 5). For Question 2, 32 manuscripts were identified, the first in 2000 with topics on Health Risk Behaviors (n = 10), Infectious Diseases (n = 3), Non-infectious Diseases (n = 9), and Other (n = 10). The literature on the use of social media to support public health practice has identified many gaps and biases in current knowledge. Despite the potential for success identified in exploratory studies, there are limited studies on interventions and little use of social media in practice. However, information gleaned from the articles demonstrates the effectiveness of social media in supporting and improving public health and in identifying target populations for intervention. A primary recommendation resulting from the review is to identify opportunities that enable public health professionals to integrate social media analytics into disease surveillance and outbreak management practice.

Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review

PubMed Central

Charles-Smith, Lauren E.; Reynolds, Tera L.; Cameron, Mark A.; Conway, Mike; Lau, Eric H. Y.; Olsen, Jennifer M.; Pavlin, Julie A.; Shigematsu, Mika; Streichert, Laura C.; Suda, Katie J.; Corley, Courtney D.

2015-01-01

Objective Research studies show that social media may be valuable tools in the disease surveillance toolkit used for improving public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. A social media work group, consisting of surveillance practitioners, academic researchers, and other subject matter experts convened by the International Society for Disease Surveillance, conducted a systematic primary literature review using the PRISMA framework to identify research, published through February 2013, answering either of the following questions: Can social media be integrated into disease surveillance practice and outbreak management to support and improve public health? Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes? Examples of social media included are Facebook, MySpace, microblogs (e.g., Twitter), blogs, and discussion forums. For Question 1, 33 manuscripts were identified, starting in 2009 with topics on Influenza-like Illnesses (n = 15), Infectious Diseases (n = 6), Non-infectious Diseases (n = 4), Medication and Vaccines (n = 3), and Other (n = 5). For Question 2, 32 manuscripts were identified, the first in 2000 with topics on Health Risk Behaviors (n = 10), Infectious Diseases (n = 3), Non-infectious Diseases (n = 9), and Other (n = 10). Conclusions The literature on the use of social media to support public health practice has identified many gaps and biases in current knowledge. Despite the potential for success identified in exploratory studies, there are limited studies on interventions and little use of social media in practice. However, information gleaned from the articles demonstrates the effectiveness of social media in supporting and improving public health and in identifying target populations for intervention. A primary recommendation resulting from the review is to identify opportunities that enable public health professionals to integrate social media analytics into disease surveillance and outbreak management practice. PMID:26437454

Pulsed-field gel electrophoresis and multi locus sequence typing for characterizing genotype variability of Yersinia ruckeri isolated from farmed fish in France.

PubMed

Calvez, Ségolène; Fournel, Catherine; Douet, Diane-Gaëlle; Daniel, Patrick

2015-06-23

Yersinia ruckeri is a pathogen that has an impact on aquaculture worldwide. The disease caused by this bacterial species, yersiniosis or redmouth disease, generates substantial economic losses due to the associated mortality and veterinary costs. For predicting outbreaks and improving control strategies, it is important to characterize the population structure of the bacteria. The phenotypic and genetic homogeneities described previously indicate a clonal population structure as observed in other fish bacteria. In this study, the pulsed-field gel electrophoresis (PFGE) and multi locus sequence typing (MLST) methods were used to describe a population of isolates from outbreaks on French fish farms. For the PFGE analysis, two enzymes (NotI and AscI) were used separately and together. Results from combining the enzymes showed the great homogeneity of the outbreak population with a similarity > 80.0% but a high variability within the cluster (cut-off value = 80.0%) with a total of 43 pulsotypes described and an index of diversity = 0.93. The dominant pulsotypes described with NotI (PtN4 and PtN7) have already been described in other European countries (Finland, Germany, Denmark, Spain and Italy). The MLST approach showed two dominant sequence types (ST31 and ST36), an epidemic structure of the French Y. ruckeri population and a preferentially clonal evolution for rainbow trout isolates. Our results point to multiple types of selection pressure on the Y. ruckeri population attributable to geographical origin, ecological niche specialization and movements of farmed fish.

Detection of severe respiratory disease epidemic outbreaks by CUSUM-based overcrowd-severe-respiratory-disease-index model.

PubMed

Polanco, Carlos; Castañón-González, Jorge Alberto; Macías, Alejandro E; Samaniego, José Lino; Buhse, Thomas; Villanueva-Martínez, Sebastián

2013-01-01

A severe respiratory disease epidemic outbreak correlates with a high demand of specific supplies and specialized personnel to hold it back in a wide region or set of regions; these supplies would be beds, storage areas, hemodynamic monitors, and mechanical ventilators, as well as physicians, respiratory technicians, and specialized nurses. We describe an online cumulative sum based model named Overcrowd-Severe-Respiratory-Disease-Index based on the Modified Overcrowd Index that simultaneously monitors and informs the demand of those supplies and personnel in a healthcare network generating early warnings of severe respiratory disease epidemic outbreaks through the interpretation of such variables. A post hoc historical archive is generated, helping physicians in charge to improve the transit and future allocation of supplies in the entire hospital network during the outbreak. The model was thoroughly verified in a virtual scenario, generating multiple epidemic outbreaks in a 6-year span for a 13-hospital network. When it was superimposed over the H1N1 influenza outbreak census (2008-2010) taken by the National Institute of Medical Sciences and Nutrition Salvador Zubiran in Mexico City, it showed that it is an effective algorithm to notify early warnings of severe respiratory disease epidemic outbreaks with a minimal rate of false alerts.

Communitywide cryptosporidiosis outbreak associated with a surface water-supplied municipal water system--Baker City, Oregon, 2013.

PubMed

DeSilva, M B; Schafer, S; Kendall Scott, M; Robinson, B; Hills, A; Buser, G L; Salis, K; Gargano, J; Yoder, J; Hill, V; Xiao, L; Roellig, D; Hedberg, K

2016-01-01

Cryptosporidium, a parasite known to cause large drinking and recreational water outbreaks, is tolerant of chlorine concentrations used for drinking water treatment. Human laboratory-based surveillance for enteric pathogens detected a cryptosporidiosis outbreak in Baker City, Oregon during July 2013 associated with municipal drinking water. Objectives of the investigation were to confirm the outbreak source and assess outbreak extent. The watershed was inspected and city water was tested for contamination. To determine the community attack rate, a standardized questionnaire was administered to randomly sampled households. Weighted attack rates and confidence intervals (CIs) were calculated. Water samples tested positive for Cryptosporidium species; a Cryptosporidium parvum subtype common in cattle was detected in human stool specimens. Cattle were observed grazing along watershed borders; cattle faeces were observed within watershed barriers. The city water treatment facility chlorinated, but did not filter, water. The community attack rate was 28·3% (95% CI 22·1-33·6), sickening an estimated 2780 persons. Watershed contamination by cattle probably caused this outbreak; water treatments effective against Cryptosporidium were not in place. This outbreak highlights vulnerability of drinking water systems to pathogen contamination and underscores the need for communities to invest in system improvements to maintain multiple barriers to drinking water contamination.

Massive outbreak of poliomyelitis caused by type-3 wild poliovirus in Angola in 1999.

PubMed Central

Valente, F.; Otten, M.; Balbina, F.; Van de Weerdt, R.; Chezzi, C.; Eriki, P.; Van-Dúnnen, J.; Bele, J. M.

2000-01-01

The largest outbreak of poliomyelitis ever recorded in Africa (1093 cases) occurred from 1 March to 28 May 1999 in Luanda, Angola, and in surrounding areas. The outbreak was caused primarily by a type-3 wild poliovirus, although type-1 wild poliovirus was circulating in the outbreak area at the same time. Infected individuals ranged in age from 2 months to 22 years; 788 individuals (72%) were younger than 3 years. Of the 590 individuals whose vaccination status was known, 23% had received no vaccine and 54% had received fewer than three doses of oral poliovirus vaccine (OPV). The major factors that contributed to this outbreak were as follows: massive displacement of unvaccinated persons to urban settings; low routine OPV coverage; inaccessible populations during the previous three national immunization days (NIDs); and inadequate sanitation. This outbreak indicates the urgent need to improve accessibility to all children during NIDs and the dramatic impact that war can have by displacing persons and impeding access to routine immunizations. The period immediately after an outbreak provides an enhanced opportunity to eradicate poliomyelitis. If continuous access in all districts for acute flaccid paralysis surveillance and supplemental immunizations cannot be assured, the current war in Angola may threaten global poliomyelitis eradication. PMID:10812730

Detection of Severe Respiratory Disease Epidemic Outbreaks by CUSUM-Based Overcrowd-Severe-Respiratory-Disease-Index Model

PubMed Central

Castañón-González, Jorge Alberto; Macías, Alejandro E.; Samaniego, José Lino; Buhse, Thomas; Villanueva-Martínez, Sebastián

2013-01-01

A severe respiratory disease epidemic outbreak correlates with a high demand of specific supplies and specialized personnel to hold it back in a wide region or set of regions; these supplies would be beds, storage areas, hemodynamic monitors, and mechanical ventilators, as well as physicians, respiratory technicians, and specialized nurses. We describe an online cumulative sum based model named Overcrowd-Severe-Respiratory-Disease-Index based on the Modified Overcrowd Index that simultaneously monitors and informs the demand of those supplies and personnel in a healthcare network generating early warnings of severe respiratory disease epidemic outbreaks through the interpretation of such variables. A post hoc historical archive is generated, helping physicians in charge to improve the transit and future allocation of supplies in the entire hospital network during the outbreak. The model was thoroughly verified in a virtual scenario, generating multiple epidemic outbreaks in a 6-year span for a 13-hospital network. When it was superimposed over the H1N1 influenza outbreak census (2008–2010) taken by the National Institute of Medical Sciences and Nutrition Salvador Zubiran in Mexico City, it showed that it is an effective algorithm to notify early warnings of severe respiratory disease epidemic outbreaks with a minimal rate of false alerts. PMID:24069063

U.S. Compounding Pharmacy-related Outbreaks, 2001–2013—Public Health and Patient Safety Lessons Learned

PubMed Central

Shehab, Nadine; Brown, Megan N.; Kallen, Alexander J.; Perz, Joseph F.

2015-01-01

Objectives Pharmacy-compounded sterile preparations (P-CSPs) are frequently relied upon in U.S. healthcare, but are increasingly being linked to outbreaks of infections. We provide an updated overview of outbreak burden and characteristics, identify drivers of P-CSP demand, and discuss public health and patient safety lessons learned to help inform prevention. Methods Outbreaks of infections linked to contaminated P-CSPs that occurred between January 1, 2001 and December 31, 2013 were identified from internal Centers for Disease Control and Prevention reports, Food and Drug Administration drug safety communications, and published literature. Results We identified 19 outbreaks linked to P-CSPs, resulting in at least 1000 cases, including deaths. Outbreaks were reported across two-thirds of states, with almost one-half (8/19) involving cases in more than one state. Almost one-half of outbreaks were linked to injectable steroids (5/19) and intraocular bevacizumab (3/19). Non-patient-specific compounding originating from non-sterile ingredients and re-packaging of already sterile products were the most common practices associated with P-CSP contamination. Breaches in aseptic processing and deficiencies in sterilization procedures or in sterility/endotoxin testing were consistent findings. Hospital outsourcing, preference for variations of commercially available products, commercial drug shortages, and lower prices were drivers of P-CSP demand. Conclusions Recognized outbreaks linked to P-CSPs have been most commonly associated with non-patient-specific re-packaging and non-sterile to sterile compounding, and linked to lack of adherence to sterile compounding standards. Recently-enhanced regulatory oversight of compounding may improve adherence to such standards. Additional measures to limit and control these outbreaks include vigilance when outsourcing P-CSPs, scrutiny of drivers for P-CSP demand, and early recognition and notification of possible outbreaks. PMID:26001553

Implementation of Nationwide Real-time Whole-genome Sequencing to Enhance Listeriosis Outbreak Detection and Investigation

PubMed Central

Jackson, Brendan R.; Tarr, Cheryl; Strain, Errol; Jackson, Kelly A.; Conrad, Amanda; Carleton, Heather; Katz, Lee S.; Stroika, Steven; Gould, L. Hannah; Mody, Rajal K.; Silk, Benjamin J.; Beal, Jennifer; Chen, Yi; Timme, Ruth; Doyle, Matthew; Fields, Angela; Wise, Matthew; Tillman, Glenn; Defibaugh-Chavez, Stephanie; Kucerova, Zuzana; Sabol, Ashley; Roache, Katie; Trees, Eija; Simmons, Mustafa; Wasilenko, Jamie; Kubota, Kristy; Pouseele, Hannes; Klimke, William; Besser, John; Brown, Eric; Allard, Marc; Gerner-Smidt, Peter

2016-01-01

Listeria monocytogenes (Lm) causes severe foodborne illness (listeriosis). Previous molecular subtyping methods, such as pulsed-field gel electrophoresis (PFGE), were critical in detecting outbreaks that led to food safety improvements and declining incidence, but PFGE provides limited genetic resolution. A multiagency collaboration began performing real-time, whole-genome sequencing (WGS) on all US Lm isolates from patients, food, and the environment in September 2013, posting sequencing data into a public repository. Compared with the year before the project began, WGS, combined with epidemiologic and product trace-back data, detected more listeriosis clusters and solved more outbreaks (2 outbreaks in pre-WGS year, 5 in WGS year 1, and 9 in year 2). Whole-genome multilocus sequence typing and single nucleotide polymorphism analyses provided equivalent phylogenetic relationships relevant to investigations; results were most useful when interpreted in context of epidemiological data. WGS has transformed listeriosis outbreak surveillance and is being implemented for other foodborne pathogens. PMID:27090985

Recommendation of the Advisory Committee on Immunization Practices for Use of a Third Dose of Mumps Virus-Containing Vaccine in Persons at Increased Risk for Mumps During an Outbreak.

PubMed

Marin, Mona; Marlow, Mariel; Moore, Kelly L; Patel, Manisha

2018-01-12

A substantial increase in the number of mumps outbreaks and outbreak-associated cases has occurred in the United States since late 2015 (1,2). To address this public health problem, the Advisory Committee on Immunization Practices (ACIP) reviewed the available evidence and determined that a third dose of measles, mumps, rubella (MMR) vaccine is safe and effective at preventing mumps. During its October 2017 meeting, ACIP recommended a third dose of a mumps virus-containing vaccine* for persons previously vaccinated with 2 doses who are identified by public health authorities as being part of a group or population at increased risk for acquiring mumps because of an outbreak. The purpose of the recommendation is to improve protection of persons in outbreak settings against mumps disease and mumps-related complications. This recommendation supplements the existing ACIP recommendations for mumps vaccination (3).

Estimated Deaths and Illnesses Averted During Fungal Meningitis Outbreak Associated with Contaminated Steroid Injections, United States, 2012–2013

PubMed Central

Derado, Gordana; Wise, Matthew; Harris, Julie R.; Chiller, Tom; Meltzer, Martin I.; Park, Benjamin J.

2015-01-01

During 2012–2013, the US Centers for Disease Control and Prevention and partners responded to a multistate outbreak of fungal infections linked to methylprednisolone acetate (MPA) injections produced by a compounding pharmacy. We evaluated the effects of public health actions on the scope of this outbreak. A comparison of 60-day case-fatality rates and clinical characteristics of patients given a diagnosis on or before October 4, the date the outbreak was widely publicized, with those of patients given a diagnosis after October 4 showed that an estimated 3,150 MPA injections, 153 cases of meningitis or stroke, and 124 deaths were averted. Compared with diagnosis after October 4, diagnosis on or before October 4 was significantly associated with a higher 60-day case-fatality rate (28% vs. 5%; p<0.0001). Aggressive public health action resulted in a substantially reduced estimated number of persons affected by this outbreak and improved survival of affected patients. PMID:25989264

Estimated deaths and illnesses averted during fungal meningitis outbreak associated with contaminated steroid injections, United States, 2012-2013.

PubMed

Smith, Rachel M; Derado, Gordana; Wise, Matthew; Harris, Julie R; Chiller, Tom; Meltzer, Martin I; Park, Benjamin J

2015-06-01

During 2012-2013, the US Centers for Disease Control and Prevention and partners responded to a multistate outbreak of fungal infections linked to methylprednisolone acetate (MPA) injections produced by a compounding pharmacy. We evaluated the effects of public health actions on the scope of this outbreak. A comparison of 60-day case-fatality rates and clinical characteristics of patients given a diagnosis on or before October 4, the date the outbreak was widely publicized, with those of patients given a diagnosis after October 4 showed that an estimated 3,150 MPA injections, 153 cases of meningitis or stroke, and 124 deaths were averted. Compared with diagnosis after October 4, diagnosis on or before October 4 was significantly associated with a higher 60-day case-fatality rate (28% vs. 5%; p<0.0001). Aggressive public health action resulted in a substantially reduced estimated number of persons affected by this outbreak and improved survival of affected patients.

Review: Evaluation of Foot-and-Mouth Disease Control Using Fault Tree Analysis.

PubMed

Isoda, N; Kadohira, M; Sekiguchi, S; Schuppers, M; Stärk, K D C

2015-06-01

An outbreak of foot-and-mouth disease (FMD) causes huge economic losses and animal welfare problems. Although much can be learnt from past FMD outbreaks, several countries are not satisfied with their degree of contingency planning and aiming at more assurance that their control measures will be effective. The purpose of the present article was to develop a generic fault tree framework for the control of an FMD outbreak as a basis for systematic improvement and refinement of control activities and general preparedness. Fault trees are typically used in engineering to document pathways that can lead to an undesired event, that is, ineffective FMD control. The fault tree method allows risk managers to identify immature parts of the control system and to analyse the events or steps that will most probably delay rapid and effective disease control during a real outbreak. The present developed fault tree is generic and can be tailored to fit the specific needs of countries. For instance, the specific fault tree for the 2001 FMD outbreak in the UK was refined based on control weaknesses discussed in peer-reviewed articles. Furthermore, the specific fault tree based on the 2001 outbreak was applied to the subsequent FMD outbreak in 2007 to assess the refinement of control measures following the earlier, major outbreak. The FMD fault tree can assist risk managers to develop more refined and adequate control activities against FMD outbreaks and to find optimum strategies for rapid control. Further application using the current tree will be one of the basic measures for FMD control worldwide. © 2013 Blackwell Verlag GmbH.

A Simple Microsoft Excel Method to Predict Antibiotic Outbreaks and Underutilization.

PubMed

Miglis, Cristina; Rhodes, Nathaniel J; Avedissian, Sean N; Zembower, Teresa R; Postelnick, Michael; Wunderink, Richard G; Sutton, Sarah H; Scheetz, Marc H

2017-07-01

Benchmarking strategies are needed to promote the appropriate use of antibiotics. We have adapted a simple regressive method in Microsoft Excel that is easily implementable and creates predictive indices. This method trends consumption over time and can identify periods of over- and underuse at the hospital level. Infect Control Hosp Epidemiol 2017;38:860-862.

Integrated permanent plot and aerial monitoring for the spruce budworm decision support system

Treesearch

David A. MacLean

2000-01-01

Spruce budworm (Choristoneura fumiferana Clem.) outbreaks cause severe mortality and growth loss of spruce and fir forest over ranch of eastern North America. The Spruce Budworm Decision Support System (DSS) links prediction and interpretation models to the ARC/1NFO GIS, under an ArcView graphical user interface. It helps forest managers predict...

An Updated Scheme for Categorizing Foods Implicated in Foodborne Disease Outbreaks: A Tri-Agency Collaboration.

PubMed

Richardson, LaTonia Clay; Bazaco, Michael C; Parker, Cary Chen; Dewey-Mattia, Daniel; Golden, Neal; Jones, Karen; Klontz, Karl; Travis, Curtis; Kufel, Joanna Zablotsky; Cole, Dana

2017-12-01

Foodborne disease data collected during outbreak investigations are used to estimate the percentage of foodborne illnesses attributable to specific food categories. Current food categories do not reflect whether or how the food has been processed and exclude many multiple-ingredient foods. Representatives from three federal agencies worked collaboratively in the Interagency Food Safety Analytics Collaboration (IFSAC) to develop a hierarchical scheme for categorizing foods implicated in outbreaks, which accounts for the type of processing and provides more specific food categories for regulatory purposes. IFSAC also developed standard assumptions for assigning foods to specific food categories, including some multiple-ingredient foods. The number and percentage of outbreaks assignable to each level of the hierarchy were summarized. The IFSAC scheme is a five-level hierarchy for categorizing implicated foods with increasingly specific subcategories at each level, resulting in a total of 234 food categories. Subcategories allow distinguishing features of implicated foods to be reported, such as pasteurized versus unpasteurized fluid milk, shell eggs versus liquid egg products, ready-to-eat versus raw meats, and five different varieties of fruit categories. Twenty-four aggregate food categories contained a sufficient number of outbreaks for source attribution analyses. Among 9791 outbreaks reported from 1998 to 2014 with an identified food vehicle, 4607 (47%) were assignable to food categories using this scheme. Among these, 4218 (92%) were assigned to one of the 24 aggregate food categories, and 840 (18%) were assigned to the most specific category possible. Updates to the food categorization scheme and new methods for assigning implicated foods to specific food categories can help increase the number of outbreaks attributed to a single food category. The increased specificity of food categories in this scheme may help improve source attribution analyses, eventually leading to improved foodborne illness source attribution estimates and enhanced food safety and regulatory efforts.

Spatio-temporal patterns of distribution of West Nile virus vectors in eastern Piedmont Region, Italy

PubMed Central

2011-01-01

Background West Nile Virus (WNV) transmission in Italy was first reported in 1998 as an equine outbreak near the swamps of Padule di Fucecchio, Tuscany. No other cases were identified during the following decade until 2008, when horse and human outbreaks were reported in Emilia Romagna, North Italy. Since then, WNV outbreaks have occurred annually, spreading from their initial northern foci throughout the country. Following the outbreak in 1998 the Italian public health authority defined a surveillance plan to detect WNV circulation in birds, horses and mosquitoes. By applying spatial statistical analysis (spatial point pattern analysis) and models (Bayesian GLMM models) to a longitudinal dataset on the abundance of the three putative WNV vectors [Ochlerotatus caspius (Pallas 1771), Culex pipiens (Linnaeus 1758) and Culex modestus (Ficalbi 1890)] in eastern Piedmont, we quantified their abundance and distribution in space and time and generated prediction maps outlining the areas with the highest vector productivity and potential for WNV introduction and amplification. Results The highest abundance and significant spatial clusters of Oc. caspius and Cx. modestus were in proximity to rice fields, and for Cx. pipiens, in proximity to highly populated urban areas. The GLMM model showed the importance of weather conditions and environmental factors in predicting mosquito abundance. Distance from the preferential breeding sites and elevation were negatively associated with the number of collected mosquitoes. The Normalized Difference Vegetation Index (NDVI) was positively correlated with mosquito abundance in rice fields (Oc. caspius and Cx. modestus). Based on the best models, we developed prediction maps for the year 2010 outlining the areas where high abundance of vectors could favour the introduction and amplification of WNV. Conclusions Our findings provide useful information for surveillance activities aiming to identify locations where the potential for WNV introduction and local transmission are highest. Such information can be used by vector control offices to stratify control interventions in areas prone to the invasion of WNV and other mosquito-transmitted pathogens. PMID:22152822

Spatio-temporal patterns of distribution of West Nile virus vectors in eastern Piedmont Region, Italy.

PubMed

Bisanzio, Donal; Giacobini, Mario; Bertolotti, Luigi; Mosca, Andrea; Balbo, Luca; Kitron, Uriel; Vazquez-Prokopec, Gonzalo M

2011-12-09

West Nile Virus (WNV) transmission in Italy was first reported in 1998 as an equine outbreak near the swamps of Padule di Fucecchio, Tuscany. No other cases were identified during the following decade until 2008, when horse and human outbreaks were reported in Emilia Romagna, North Italy. Since then, WNV outbreaks have occurred annually, spreading from their initial northern foci throughout the country. Following the outbreak in 1998 the Italian public health authority defined a surveillance plan to detect WNV circulation in birds, horses and mosquitoes. By applying spatial statistical analysis (spatial point pattern analysis) and models (Bayesian GLMM models) to a longitudinal dataset on the abundance of the three putative WNV vectors [Ochlerotatus caspius (Pallas 1771), Culex pipiens (Linnaeus 1758) and Culex modestus (Ficalbi 1890)] in eastern Piedmont, we quantified their abundance and distribution in space and time and generated prediction maps outlining the areas with the highest vector productivity and potential for WNV introduction and amplification. The highest abundance and significant spatial clusters of Oc. caspius and Cx. modestus were in proximity to rice fields, and for Cx. pipiens, in proximity to highly populated urban areas. The GLMM model showed the importance of weather conditions and environmental factors in predicting mosquito abundance. Distance from the preferential breeding sites and elevation were negatively associated with the number of collected mosquitoes. The Normalized Difference Vegetation Index (NDVI) was positively correlated with mosquito abundance in rice fields (Oc. caspius and Cx. modestus). Based on the best models, we developed prediction maps for the year 2010 outlining the areas where high abundance of vectors could favour the introduction and amplification of WNV. Our findings provide useful information for surveillance activities aiming to identify locations where the potential for WNV introduction and local transmission are highest. Such information can be used by vector control offices to stratify control interventions in areas prone to the invasion of WNV and other mosquito-transmitted pathogens.

Learning from history, predicting the future: the UK Dutch elm disease outbreak in relation to contemporary tree disease threats

PubMed Central

Potter, Clive; Harwood, Tom; Knight, Jon; Tomlinson, Isobel

2011-01-01

Expanding international trade and increased transportation are heavily implicated in the growing threat posed by invasive pathogens to biodiversity and landscapes. With trees and woodland in the UK now facing threats from a number of disease systems, this paper looks to historical experience with the Dutch elm disease (DED) epidemic of the 1970s to see what can be learned about an outbreak and attempts to prevent, manage and control it. The paper draws on an interdisciplinary investigation into the history, biology and policy of the epidemic. It presents a reconstruction based on a spatial modelling exercise underpinned by archival research and interviews with individuals involved in the attempted management of the epidemic at the time. The paper explores what, if anything, might have been done to contain the outbreak and discusses the wider lessons for plant protection. Reading across to present-day biosecurity concerns, the paper looks at the current outbreak of ramorum blight in the UK and presents an analysis of the unfolding epidemiology and policy of this more recent, and potentially very serious, disease outbreak. The paper concludes by reflecting on the continuing contemporary relevance of the DED experience at an important juncture in the evolution of plant protection policy. PMID:21624917

[Waterborne diseases outbreaks in the Czech Republic, 1995-2005].

PubMed

Kozísek, F; Jeligová, H; Dvoráková, A

2009-08-01

Despite considerable advances in drinking water safety assurance and adherence to the public health standards, waterborne diaseases outbreaks have still been observed even in industrialized countries. The study objective was to map such outbreaks in the Czech Republic in 1995-2005. In this study, an outbreak is the occurrence of more cases of disease than normally expected within a specific place over a given period of time and a waterborne disease is a disease where water is the vehicle or source of infection. The data on waterborne outbreaks was obtained from the EPIDAT database (national infectious diseases reporting system) information provided by epidemiologists of all regional public health authorities and the National Reference Laboratory for Legionella. In 1995 - 2005, 33 outbreaks with water indicated as the route of transmission were recorded in the Czech Republic. The leading cause was unsafe drinking water (27 outbreaks), mainly from wells (19 outbreaks); nevertheless, the most serious consequences were observed in two outbreaks caused by microbiologically contaminated hot water. Other sources of waterborne infection were mineral water springs, a swimming pool and a brook. The total of reported cases of waterborne diseases was 1655, 356 hospitalisations and ten deaths due to legionellosis were recorded. The highest number of outbreaks (7) as well as the highest number of cases (841) were reported in 1997. Comparison of two five-year periods, i.e. 1996-2000 and 2001-2005, showed a nearly one third decrease in the total of outbreaks and a half reduction in the total of cases in the latter. In view of the limited length of monitoring, it is not possible to say with certainty whether it is a random distribution or an actual trend. Almost two thirds of cases were diagnosed as acute gastroenteritis of probable infectious origin and other frequent waterborne diseases were viral hepatitis A and bacillary dysentery. When analyzing the described outbreaks, it should be taken into account that only the diagnosed and reported outbreak cases are covered, while the actual number of cases is likely to be underreported. Although no evidence is available that any vast and serious waterborne diseases outbreaks escaped reporting, some small and less serious outbreaks may have occurred unnoticed. In the future, the diagnosis, investigation and evaluation of waterborne diseases outbreaks should be improved, among others by implementing an evidence-based classification system and issuing regular surveys of outbreaks and their causes which would be helpful in preventing failures in other similar water sources.

Ensemble forecast of human West Nile virus cases and mosquito infection rates

NASA Astrophysics Data System (ADS)

Defelice, Nicholas B.; Little, Eliza; Campbell, Scott R.; Shaman, Jeffrey

2017-02-01

West Nile virus (WNV) is now endemic in the continental United States; however, our ability to predict spillover transmission risk and human WNV cases remains limited. Here we develop a model depicting WNV transmission dynamics, which we optimize using a data assimilation method and two observed data streams, mosquito infection rates and reported human WNV cases. The coupled model-inference framework is then used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, New York for 2001-2014. Accurate forecasts of mosquito infection rates are generated before peak infection, and >65% of forecasts accurately predict seasonal total human WNV cases up to 9 weeks before the past reported case. This work provides the foundation for implementation of a statistically rigorous system for real-time forecast of seasonal outbreaks of WNV.

Updates to the zoonotic niche map of Ebola virus disease in Africa

PubMed Central

Pigott, David M; Millear, Anoushka I; Earl, Lucas; Morozoff, Chloe; Han, Barbara A; Shearer, Freya M; Weiss, Daniel J; Brady, Oliver J; Kraemer, Moritz UG; Moyes, Catherine L; Bhatt, Samir; Gething, Peter W; Golding, Nick; Hay, Simon I

2016-01-01

As the outbreak of Ebola virus disease (EVD) in West Africa is now contained, attention is turning from control to future outbreak prediction and prevention. Building on a previously published zoonotic niche map (Pigott et al., 2014), this study incorporates new human and animal occurrence data and expands upon the way in which potential bat EVD reservoir species are incorporated. This update demonstrates the potential for incorporating and updating data used to generate the predicted suitability map. A new data portal for sharing such maps is discussed. This output represents the most up-to-date estimate of the extent of EVD zoonotic risk in Africa. These maps can assist in strengthening surveillance and response capacity to contain viral haemorrhagic fevers. DOI: http://dx.doi.org/10.7554/eLife.16412.001 PMID:27414263

Ensemble forecast of human West Nile virus cases and mosquito infection rates.

PubMed

DeFelice, Nicholas B; Little, Eliza; Campbell, Scott R; Shaman, Jeffrey

2017-02-24

West Nile virus (WNV) is now endemic in the continental United States; however, our ability to predict spillover transmission risk and human WNV cases remains limited. Here we develop a model depicting WNV transmission dynamics, which we optimize using a data assimilation method and two observed data streams, mosquito infection rates and reported human WNV cases. The coupled model-inference framework is then used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, New York for 2001-2014. Accurate forecasts of mosquito infection rates are generated before peak infection, and >65% of forecasts accurately predict seasonal total human WNV cases up to 9 weeks before the past reported case. This work provides the foundation for implementation of a statistically rigorous system for real-time forecast of seasonal outbreaks of WNV.

Predictive maps for Juno perijoves and identification of significant features

NASA Astrophysics Data System (ADS)

Rogers, J. H.; Adamoli, G.; Jacquesson, M.; Vedovato, M.; Mettig, H.-J.; Eichstädt, G.; Caplinger, M.; Momary, T. W.; Orton, G. S.; Tabataba-Vakili, F.; Hansen, C. J.

2017-09-01

At each Juno perijove, JunoCam takes hi-res images of selected latitudes along the sub-spacecraft track, as determined by public voting. To inform this target election process, we use the continuous coverage of Jupiter's visible clouds by amateur imaging, and the tracking of features from those images by the JUPOS project, to identify the features which are expected to be visible at the upcoming perijove. We produce a predictive map for each perijove, and subsequently annotate the JunoCam images to locate the known jets and circulation. Up to perijove 5, this collaboration has contributed to hi-res imaging of several long-lived circulations in northern and southern hemispheres, of major new convective outbreaks in the North and South Equatorial Belts, and of the North Temperate Belt maturing after a cyclic outbreak.

Surveillance on the endemic of Zika virus infection by meteorological factors in Colombia: a population-based spatial and temporal study.

PubMed

Chien, Lung-Chang; Lin, Ro-Ting; Liao, Yunqi; Sy, Francisco S; Pérez, Adriana

2018-04-17

Zika virus (ZIKV) infection is a pandemic and a public health emergency. It is transmitted by mosquitoes, primarily the Aedes genus. In light of no treatment currently, it is crucial to develop effective vector control programs to prevent the spread of ZIKV infection earlier when observing possible risk factors, such as weather conditions enhancing mosquito breeding and surviving. This study collected daily meteorological measurements and weekly ZIKV infectious cases among 32 departments of Colombia from January 2015-December 2016. This study applied the distributed lag nonlinear model to estimate the association between the number of ZIKA virus infection and meteorological measurements, controlling for spatial and temporal variations. We examined at most three meteorological factors with 20 lags in weeks in the model. Average humidity, total rainfall, and maximum temperature were more predictable of ZIKV infection outbreaks than other meteorological factors. Our models can detect significantly lagged effects of average humidity, total rainfall, and maximum temperature on outbreaks up to 15, 14, and 20 weeks, respectively. The spatial analysis identified 12 departments with a significant threat of ZIKV, and eight of those high-risk departments were located between the Equator and 6°N. The outbreak prediction also performed well in identified high-risk departments. Our results demonstrate that meteorological factors could be used for predicting ZIKV epidemics. Building an early warning surveillance system is important for preventing ZIKV infection, particularly in endemic areas.

Environmental Monitoring of Endemic Cholera

NASA Astrophysics Data System (ADS)

ElNemr, W.; Jutla, A. S.; Constantin de Magny, G.; Hasan, N. A.; Islam, M.; Sack, R.; Huq, A.; Hashem, F.; Colwell, R.

2012-12-01

Cholera remains a major public health threat. Since Vibrio cholerae, the causative agent of the disease, is autochthonous to riverine, estuarine, and coastal waters, it is unlikely the bacteria can be eradicated from its natural habitat. Prediction of disease, in conjunction with preventive vaccination can reduce the prevalence rate of a disease. Understanding the influence of environmental parameters on growth and proliferation of bacteria is an essential first step in developing prediction methods for outbreaks. Large scale geophysical variables, such as SST and coastal chlorophyll, are often associated with conditions favoring growth of V. cholerae. However, local environmental factors, meaning biological activity in ponds from where the bulk of populations in endemic regions derive water for daily usage, are either neglected or oversimplified. Using data collected from several sites in two geographically distinct locations in South Asia, we have identified critical local environmental factors associated with cholera outbreak. Of 18 environmental variables monitored for water sources in Mathbaria (a coastal site near the Bay of Bengal) and Bakergonj (an inland site) of Bangladesh, water depth and chlorophyll were found to be important factors associated with initiation of cholera outbreaks. Cholera in coastal regions appears to be related to intrusion. However, monsoonal flooding creates conditions for cholera epidemics in inland regions. This may be one of the first attempts to relate in-situ environmental observations with cholera. We anticipate that it will be useful for further development of prediction models in the resource constrained regions.

Data-Driven Disease Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Generous, Nicholas

If disease outbreaks could be forecasted like the weather, communities could set up protective measures to mitigate their impact. At Los Alamos National Laboratory, scientists are improving disease-forecasting mathematical models by using clinical data--as well as internet data sources such as Wikipedia, Twitter, and Google--and coupling it with satellite imagery. The goal is to better understanding how diseases spread and, eventually, forecast disease outbreaks.

Structural models used in real-time biosurveillance outbreak detection and outbreak curve isolation from noisy background morbidity levels

PubMed Central

Cheng, Karen Elizabeth; Crary, David J; Ray, Jaideep; Safta, Cosmin

2013-01-01

Objective We discuss the use of structural models for the analysis of biosurveillance related data. Methods and results Using a combination of real and simulated data, we have constructed a data set that represents a plausible time series resulting from surveillance of a large scale bioterrorist anthrax attack in Miami. We discuss the performance of anomaly detection with structural models for these data using receiver operating characteristic (ROC) and activity monitoring operating characteristic (AMOC) analysis. In addition, we show that these techniques provide a method for predicting the level of the outbreak valid for approximately 2 weeks, post-alarm. Conclusions Structural models provide an effective tool for the analysis of biosurveillance data, in particular for time series with noisy, non-stationary background and missing data. PMID:23037798

From SARS to H7N9: the mechanism of responding to emerging communicable diseases has made great progress in China.

PubMed

Yao, Linong; Chen, Enfu; Chen, Zhiping; Gong, Zhenyu

2013-12-01

The outbreak of severe acute respiratory syndrome (SARS) in 2003 indicated that China's existing former mechanism for emergency management was very vulnerable. The Chinese Government has since established a new mechanism for responding to emerging communicable diseases. This paper examined the current status of and developments in China's response to emerging communicable diseases from the outbreak of SARS in 2003 to the outbreak of H7N9 virus infection in 2013. Results indicated that the current mechanism for emergency responses to emerging communicable diseases in China has made great achievements in terms of command and decision-making, organization and collaboration, monitoring and early warning systems, protection, and international communication and cooperation. This mechanism for responding to emerging communicable diseases allowed China to successfully deal with outbreaks of the H5N1 bird flu, H1N1 flu, and H7N9 bird flu. However, a better coordination system, a more complete Office of Responses to Public Health Emergencies, administrative responsibility and error correction, better personnel training, and government responsibility may help to improve the response to emerging communicable diseases. Such improvements are eagerly anticipated.

Characterization of Resistance Genes and Plasmids from Outbreaks and Illness Clusters Caused by Salmonella Resistant to Ceftriaxone in the United States, 2011–2012

PubMed Central

Folster, Jason P.; Grass, Julian E.; Bicknese, Amelia; Taylor, Julia; Friedman, Cindy R.; Whichard, Jean M.

2017-01-01

Salmonella is an important cause of foodborne illness; however, quickly identifying the source of these infections can be difficult, and source identification is a crucial step in preventing additional illnesses. Although most infections are self-limited, invasive salmonellosis may require antimicrobial treatment. Ceftriaxone, an extended-spectrum cephalosporin, is commonly used for treatment of salmonellosis. Previous studies have identified a correlation between the food animal/retail meat source of ceftriaxone-resistant Salmonella and the type of resistance gene and plasmid it carries. In this study, we examined seven outbreaks of ceftriaxone-resistant Salmonella infections, caused by serotypes Typhimurium, Newport, Heidelberg, and Infantis. All isolates were positive for a plasmid-encoded blaCMY gene. Plasmid incompatibility typing identified five IncI1 and two IncA/C plasmids. Both outbreaks containing blaCMY-IncA/C plasmids were linked to consumption of cattle products. Three of five outbreaks with blaCMY-IncI1 (ST12) plasmids were linked to a poultry source. The remaining IncI1 outbreaks were associated with ground beef (ST20) and tomatoes (ST12). Additionally, we examined isolates from five unsolved clusters of ceftriaxone-resistant Salmonella infections and used our plasmid encoded gene findings to predict the source. Overall, we identified a likely association between the source of ceftriaxone-resistant Salmonella outbreaks and the type of resistance gene/plasmid it carries. PMID:27828730

History of Meningococcal Outbreaks in the United States: Implications for Vaccination and Disease Prevention.

PubMed

Atkinson, Bruce; Gandhi, Ashesh; Balmer, Paul

2016-08-01

Invasive meningococcal disease caused by Neisseria meningitidis presents a significant public health concern. Meningococcal disease is rare but potentially fatal within 24 hours of onset of illness, and survivors may experience permanent sequelae. This review presents the epidemiology, incidence, and outbreak data for invasive meningococcal disease in the United States since 1970, and it highlights recent changes in vaccine recommendations to prevent meningococcal disease. Relevant publications were obtained by database searches for articles published between January 1970 and July 2015. The incidence of meningococcal disease has decreased in the United States since 1970, but serogroup B meningococcal disease is responsible for an increasing proportion of disease burden in young adults. Recent serogroup B outbreaks on college campuses warrant broader age-based recommendations for meningococcal group B vaccines, similar to the currently recommended quadrivalent vaccine that protects against serogroups A, C, W, and Y. After the recent approval of two serogroup B vaccines, the Advisory Committee on Immunization Practices first updated its recommendations for routine meningococcal vaccination to cover at-risk populations, including those at risk during serogroup B outbreaks, and later it issued a recommendation for those aged 16-23 years. Meningococcal disease outbreaks remain challenging to predict, making the optimal disease management strategy one of prevention through vaccination rather than containment. How the epidemiology of serogroup B disease and prevention of outbreaks will be affected by the new category B recommendation for serogroup B vaccines remains to be seen. © 2016 Pharmacotherapy Publications, Inc.

Characterization of Resistance Genes and Plasmids from Outbreaks and Illness Clusters Caused by Salmonella Resistant to Ceftriaxone in the United States, 2011-2012.

PubMed

Folster, Jason P; Grass, Julian E; Bicknese, Amelia; Taylor, Julia; Friedman, Cindy R; Whichard, Jean M

2017-03-01

Salmonella is an important cause of foodborne illness; however, quickly identifying the source of these infections can be difficult, and source identification is a crucial step in preventing additional illnesses. Although most infections are self-limited, invasive salmonellosis may require antimicrobial treatment. Ceftriaxone, an extended-spectrum cephalosporin, is commonly used for treatment of salmonellosis. Previous studies have identified a correlation between the food animal/retail meat source of ceftriaxone-resistant Salmonella and the type of resistance gene and plasmid it carries. In this study, we examined seven outbreaks of ceftriaxone-resistant Salmonella infections, caused by serotypes Typhimurium, Newport, Heidelberg, and Infantis. All isolates were positive for a plasmid-encoded bla CMY gene. Plasmid incompatibility typing identified five IncI1 and two IncA/C plasmids. Both outbreaks containing bla CMY -IncA/C plasmids were linked to consumption of cattle products. Three of five outbreaks with bla CMY -IncI1 (ST12) plasmids were linked to a poultry source. The remaining IncI1 outbreaks were associated with ground beef (ST20) and tomatoes (ST12). In addition, we examined isolates from five unsolved clusters of ceftriaxone-resistant Salmonella infections and used our plasmid-encoded gene findings to predict the source. Overall, we identified a likely association between the source of ceftriaxone-resistant Salmonella outbreaks and the type of resistance gene/plasmid it carries.

Serratia marcescens outbreak in a neonatal intensive care unit (NICU): new insights from next-generation sequencing applications.

PubMed

Martineau, Christine; Li, Xuejing; Lalancette, Cindy; Perreault, Thérèse; Fournier, Eric; Tremblay, Julien; Gonzales, Milagros; Yergeau, Étienne; Quach, Caroline

2018-06-13

Serratia marcescens is an environmental bacterium commonly associated with outbreaks in neonatal intensive care units (NICU). Investigation of S. marcescens outbreaks requires efficient recovery and typing of clinical and environmental isolates. In this study, we described how the use of next-generation sequencing applications, such as bacterial whole-genome sequencing (WGS) and bacterial community profiling, could improve S. marcescens outbreak investigation. Phylogenomic links and potential antibiotic resistance genes and plasmids in S. marcescens isolates were investigated using WGS, while bacterial communities and relative abundances of Serratia in environmental samples were assessed using sequencing of bacterial phylogenetic marker genes (16S rRNA and gyrB genes). Typing results obtained using WGS for the ten S. marcescens isolates recovered during a NICU outbreak investigation were highly consistent with those from pulse-field gel electrophoresis (PFGE), the current gold standard typing method for this bacterium. WGS also allowed for the identification of genes associated with antibiotic resistance in all isolates, while no plasmid was detected. Sequencing of the 16S rRNA and gyrB genes both showed higher relative abundances of Serratia in environmental sampling sites that were in close contact with infected babies. Much lower relative abundances of Serratia were observed following disinfection of a room, indicating that the protocol used was efficient. Variations in the bacterial community composition and structure following room disinfection and between sampling sites were also identified through 16S rRNA gene sequencing. Globally, results from this study highlight the potential for next-generation sequencing tools to improve and facilitate outbreak investigation. Copyright © 2018 American Society for Microbiology.

Student behavior during a school closure caused by pandemic influenza A/H1N1.

PubMed

Miller, Joel C; Danon, Leon; O'Hagan, Justin J; Goldstein, Edward; Lajous, Martin; Lipsitch, Marc

2010-05-05

Many schools were temporarily closed in response to outbreaks of the recently emerged pandemic influenza A/H1N1 virus. The effectiveness of closing schools to reduce transmission depends largely on student/family behavior during the closure. We sought to improve our understanding of these behaviors. To characterize this behavior, we surveyed students in grades 9-12 and parents of students in grades 5-8 about student activities during a week long closure of a school during the first months after the disease emerged. We found significant interaction with the community and other students-though less interaction with other students than during school-with the level of interaction increasing with grade. Our results are useful for the future design of social distancing policies and to improving the ability of modeling studies to accurately predict their impact.

Student Behavior during a School Closure Caused by Pandemic Influenza A/H1N1

PubMed Central

Miller, Joel C.; Danon, Leon; O'Hagan, Justin J.; Goldstein, Edward; Lajous, Martin; Lipsitch, Marc

2010-01-01

Background Many schools were temporarily closed in response to outbreaks of the recently emerged pandemic influenza A/H1N1 virus. The effectiveness of closing schools to reduce transmission depends largely on student/family behavior during the closure. We sought to improve our understanding of these behaviors. Methodology/Principal Findings To characterize this behavior, we surveyed students in grades 9–12 and parents of students in grades 5–8 about student activities during a weeklong closure of a school during the first months after the disease emerged. We found significant interaction with the community and other students–though less interaction with other students than during school–with the level of interaction increasing with grade. Conclusions Our results are useful for the future design of social distancing policies and to improving the ability of modeling studies to accurately predict their impact. PMID:20463960

The Prevalence Rate of Tuberculin Skin Test Positive by Contacts Group to Predict the Development of Active Tuberculosis After School Outbreaks.

PubMed

Kim, Hee Jin; Chun, Byung Chul; Kwon, AmyM; Lee, Gyeong-Ho; Ryu, Sungweon; Oh, Soo Yeon; Lee, Jin Beom; Yoo, Se Hwa; Kim, Eui Sook; Kim, Je Hyeong; Shin, Chol; Lee, Seung Heon

2015-10-01

The tuberculin skin test (TST) is the standard tool to diagnose latent tuberculosis infection (LTBI) in mass screening. The aim of this study is to find an optimal cut-off point of the TST+ rate within tuberculosis (TB) contacts to predict the active TB development among adolescents in school TB outbreaks. The Korean National Health Insurance Review and Assessment database was used to identify active TB development in relation to the initial TST (cut-off, 10 mm). The 7,475 contacts in 89 schools were divided into two groups: Incident TB group (43 schools) and no incident TB group (46 schools). LTBI treatment was initiated in 607 of the 1,761 TST+ contacts. The association with active TB progression was examined at different cut-off points of the TST+ rate. The mean duration of follow-up was 3.9±0.9 years. Thirty-three contacts developed active TB during the 4,504 person-years among the TST+ contacts without LTBI treatment (n=1,154). The average TST+ rate for the incident TB group (n=43) and no incident TB group (n=46) were 31.0% and 15.5%, respectively. The TST+ rate per group was related with TB progression (odds ratio [OR], 1.025; 95% confidence interval [CI], 1.001-1.050; p=0.037). Based on the TST+ rate per group, active TB was best predicted at TST+ ≥ 16% (OR, 3.11; 95% CI, 1.29-7.51; area under curve, 0.64). Sixteen percent of the TST+ rate per group within the same grade students can be suggested as an optimal cut-off to predict active TB development in middle and high schools TB outbreaks.

Oceanic influence on seasonal malaria outbreaks over Senegal and Sahel. Predictability using S4CAST model

NASA Astrophysics Data System (ADS)

Diouf, Ibrahima; Deme, Abdoulaye; Rodriguez-Fonseca, Belen; Suárez-Moreno, Roberto; Cisse, Moustapha; Ndione, Jacques-André; Thierno Gaye, Amadou

2014-05-01

Senegal and, in general, West African regions are affected by important outbreaks of diseases with destructive consequences for human population, livestock and country's economy. The vector-borne diseases such as mainly malaria, Rift Valley Fever and dengue are affected by the interanual to decadal variability of climate. Analysis of the spatial and temporal variability of climate parameters and associated oceanic patterns is important in order to assess the climate impact on malaria transmission. In this study, the approach developed to study the malaria-climate link is predefined by the QWeCI project (Quantifying Weather and Climate Impacts on Health in Developing Countries). Preliminary observations and simulations results over Senegal Ferlo region, confirm that the risk of malaria transmission is mainly linked to climate parameters such as rainfall, temperature and relative humidity; and a lag of one to two months between the maximum of malaria and the maximum of climate parameters as rainfall is observed. As climate variables are able to be predicted from oceanic SST variability in remote regions, this study explores seasonal predictability of malaria incidence outbreaks from previous sea surface temperatures conditions in different ocean basins. We have found causal or coincident relationship between El Niño and malaria parameters by coupling LMM UNILIV malaria model and S4CAST statistiscal model with the aim of predicting the malaria parameters with more than 6 months in advance. In particular, El Niño is linked to an important decrease of the number of mosquitoes and the malaria incidence. Results from this research, after assessing the seasonal malaria parameters, are expected to be useful for decision makers to better access to climate forecasts and application on health in the framework of rolling back malaria transmission.

Impact of the Legionella urinary antigen test on epidemiological trends in community outbreaks of legionellosis in Catalonia, Spain, 1990-2004.

PubMed

Alvarez, Josep; Domínguez, Angela; Sabrià, Miquel; Ruiz, Laura; Torner, Nuria; Cayla, Joan; Barrabeig, Irene; Sala, M Rosa; Godoy, Pere; Camps, Neus; Minguell, Sofia

2009-11-01

To describe the characteristics of community outbreaks of legionellosis in Catalonia, Spain from 1990 to 2004, to compare two time periods (1990-1996 and 1997-2004), and to assess the influence of outbreak characteristics on the case fatality rate (CFR). This is a descriptive analysis of the outbreaks detected by epidemiological surveillance units in Catalonia. Variables potentially related to the CFR were analyzed by logistic regression. Of the 118 outbreaks involving 690 patients (overall CFR 4.5%), the urinary antigen test (UAT) was used for first case diagnosis in 80.5%. The origin of the outbreak was identified as a cooling tower in 35.6%, as a water distribution system in a public building in 14.4%, and a water distribution system at other sites in 7.6%. Statistically significant differences were found in the CFR (12.2% vs. 3.9%; p=0.018) and detection of the first case by UAT (0.0% vs. 87.2%; p<0.001) between the two time periods investigated. Logistic regression showed an increase in the CFR according to outbreak size (adjusted odds ratio (aOR) 1.18; 95% confidence interval (CI) 1.05-1.33) that was significantly lower in the second period (aOR 0.09; 95% CI 0.04-0.20). Since the UAT was introduced, early diagnosis and treatment has helped to improve the outcomes and CFR of cases involved in outbreaks of legionellosis.

Dynamics of epidemics outbreaks in heterogeneous populations

NASA Astrophysics Data System (ADS)

Brockmann, Dirk; Morales-Gallardo, Alejandro; Geisel, Theo

2007-03-01

The dynamics of epidemic outbreaks have been investigated in recent years within two alternative theoretical paradigms. The key parameter of mean field type of models such as the SIR model is the basic reproduction number R0, the average number of secondary infections caused by one infected individual. Recently, scale free network models have received much attention as they account for the high variability in the number of social contacts involved. These models predict an infinite basic reproduction number in some cases. We investigate the impact of heterogeneities of contact rates in a generic model for epidemic outbreaks. We present a system in which both the time periods of being infectious and the time periods between transmissions are Poissonian processes. The heterogeneities are introduced by means of strongly variable contact rates. In contrast to scale free network models we observe a finite basic reproduction number and, counterintuitively a smaller overall epidemic outbreak as compared to the homogeneous system. Our study thus reveals that heterogeneities in contact rates do not necessarily facilitate the spread to infectious disease but may well attenuate it.

Hierarchical dose response of E. coli O157:H7 from human outbreaks incorporating heterogeneity in exposure.

PubMed

Teunis, P F M; Ogden, I D; Strachan, N J C

2008-06-01

The infectivity of pathogenic microorganisms is a key factor in the transmission of an infectious disease in a susceptible population. Microbial infectivity is generally estimated from dose-response studies in human volunteers. This can only be done with mildly pathogenic organisms. Here a hierarchical Beta-Poisson dose-response model is developed utilizing data from human outbreaks. On the lowest level each outbreak is modelled separately and these are then combined at a second level to produce a group dose-response relation. The distribution of foodborne pathogens often shows strong heterogeneity and this is incorporated by introducing an additional parameter to the dose-response model, accounting for the degree of overdispersion relative to Poisson distribution. It was found that heterogeneity considerably influences the shape of the dose-response relationship and increases uncertainty in predicted risk. This uncertainty is greater than previously reported surrogate and outbreak models using a single level of analysis. Monte Carlo parameter samples (alpha, beta of the Beta-Poisson model) can be readily incorporated in risk assessment models built using tools such as S-plus and @ Risk.

Middle East respiratory syndrome coronavirus: current situation and travel-associated concerns.

PubMed

Al-Tawfiq, Jaffar A; Omrani, Ali S; Memish, Ziad A

2016-06-01

The emergence of Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 brought back memories of the occurrence of severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002. More than 1500 MERS-CoV cases were recorded in 42 months with a case fatality rate (CFR) of 40%. Meanwhile, 8000 cases of SARS-CoV were confirmed in six months with a CFR of 10%. The clinical presentation of MERS-CoV ranges from mild and non-specific presentation to progressive and severe pneumonia. No predictive signs or symptoms exist to differentiate MERS-CoV from community-acquired pneumonia in hospitalized patients. An apparent heterogeneity was observed in transmission. Most MERS-CoV cases were secondary to large outbreaks in healthcare settings. These cases were secondary to community-acquired cases, which may also cause family outbreaks. Travel-associated MERS infection remains low. However, the virus exhibited a clear tendency to cause large outbreaks outside the Arabian Peninsula as exemplified by the outbreak in the Republic of Korea. In this review, we summarize the current knowledge about MERS-CoV and highlight travel-related issues.

Rapid Field Response to a Cluster of Illnesses and Deaths - Sinoe County, Liberia, April-May, 2017.

PubMed

Doedeh, John; Frimpong, Joseph Asamoah; Yealue, Kwuakuan D M; Wilson, Himiede W; Konway, Youhn; Wiah, Samson Q; Doedeh, Vivian; Bao, Umaru; Seneh, George; Gorwor, Lawrence; Toe, Sylvester; Ghartey, Emmanuel; Larway, Lawrence; Gweh, Dedesco; Gonotee, Philemon; Paasewe, Thomas; Tamatai, George; Yarkeh, James; Smith, Samuel; Brima-Davis, Annette; Dauda, George; Monger, Thomas; Gornor-Pewu, Leleh W; Lombeh, Siafa; Naiene, Jeremias; Dovillie, Nathaniel; Korvayan, Mark; George, Geraldine; Kerwillain, Garrison; Jetoh, Ralph; Friesen, Suzanne; Kinkade, Carl; Katawera, Victoria; Amo-Addae, Maame; George, Roseline N; Gbanya, Miatta Z; Dokubo, E Kainne

2017-10-27

On April 25, 2017, the Sinoe County Health Team (CHT) notified the Liberia Ministry of Health (MoH) and the National Public Health Institute of Liberia of an unknown illness among 14 persons that resulted in eight deaths in Sinoe County. On April 26, the National Rapid Response Team and epidemiologists from CDC, the World Health Organization (WHO) and the African Field Epidemiology Network (AFENET) in Liberia were deployed to support the county-led response. Measures were immediately implemented to identify all cases, ascertain the cause of illness, and control the outbreak. Illness was associated with attendance at a funeral event, and laboratory testing confirmed Neisseria meningitidis in biologic specimens from cases. The 2014-2015 Ebola virus disease (Ebola) outbreak in West Africa devastated Liberia's already fragile health system, and it took many months for the country to mount an effective response to control the outbreak. Substantial efforts have been made to strengthen Liberia's health system to prevent, detect, and respond to health threats. The rapid and efficient field response to this outbreak of N. meningitidis resulted in implementation of appropriate steps to prevent a widespread outbreak and reflects improved public health and outbreak response capacity in Liberia.

Contaminated water caused the first outbreak of giardiasis in Finland, 2007: a descriptive study.

PubMed

Rimhanen-Finne, Ruska; Hänninen, Marja-Liisa; Vuento, Risto; Laine, Janne; Jokiranta, T Sakari; Snellman, Marja; Pitkänen, Tarja; Miettinen, Ilkka; Kuusi, Markku

2010-08-01

The severe sewage contamination of a drinking water distribution network affected inhabitants in the town of Nokia, Finland in November 2007-February 2008. One of the pathogens found in patient and environmental samples was Giardia, which for the first time was detected as the causal agent of an outbreak in Finland. To describe the existence and the importance of Giardia infections related to this outbreak, we described characteristics of the giardiasis cases and calculated the incidence of giardiasis as well as the frequency of positive Giardia tests both before and during the outbreak. Persons reported to the Finnish Infectious Disease Registry (FIDR) with Giardia infections were interviewed. The number of persons tested for Giardia was obtained from the Centre for Laboratory Medicine at the Tampere University Hospital. The investigations provided strong evidence that Giardia infections in Nokia resulted from the contaminated water. The proportion of persons testing positive for Giardia and the incidence of giardiasis multiplied during the outbreak. To improve outbreak management, national guidelines on testing environmental samples for Giardia should be developed, and further resources should be allocated to both clinical and environmental laboratories that perform parasitological analyses.

Estimating true hospital morbidity of complications associated with mumps outbreak, England, 2004/05

PubMed Central

Yung, CF; Ramsay, M

2016-01-01

Mumps outbreaks in highly vaccinated populations continue to be reported globally. Therefore, quantifying the burden of mumps morbidity accurately will be necessary to better assess the impact of mumps vaccination programmes. We aim to estimate the true morbidity resulting from mumps complications in terms of hospitalised orchitis, meningitis, oophoritis and pancreatitis in England during the outbreak in 2004/05. This outbreak in England led to a clear increase in hospitalisations coded to mumps for complications of orchitis in those born in the 1970s and 1980s and possibly for meningitis in those born in the 1980s. A simple statistical model, based on analysing time trends for diagnosed complications in hospital databases with routine laboratory surveillance data, found that the actual morbidity was much higher. There were 2.5 times (166 cases) more mumps orchitis cases in the 1970s cohort and 2.0 times (708 cases) more mumps orchitis cases in the 1980s cohort than complications coded to mumps in hospital databases. Our study demonstrated that the mumps outbreak in England 2004/05 resulted in a substantial increase in hospitalised mumps complications, and the model we used can improve the ascertainment of morbidity from a mumps outbreak. PMID:27562958

The Methanol Poisoning Outbreaks in Libya 2013 and Kenya 2014

PubMed Central

Rostrup, Morten; Edwards, Jeffrey K.; Abukalish, Mohamed; Ezzabi, Masoud; Some, David; Ritter, Helga; Menge, Tom; Abdelrahman, Ahmed; Rootwelt, Rebecca; Janssens, Bart; Lind, Kyrre; Paasma, Raido; Hovda, Knut Erik

2016-01-01

Background Outbreaks of methanol poisoning occur frequently on a global basis, affecting poor and vulnerable populations. Knowledge regarding methanol is limited, likely many cases and even outbreaks go unnoticed, with patients dying unnecessarily. We describe findings from the first three large outbreaks of methanol poisoning where Médecins Sans Frontières (MSF) responded, and evaluate the benefits of a possible future collaboration between local health authorities, a Non-Governmental Organisation and international expertise. Methods Retrospective study of three major methanol outbreaks in Libya (2013) and Kenya (May and July 2014). Data were collected from MSF field personnel, local health personnel, hospital files, and media reports. Findings In Tripoli, Libya, over 1,000 patients were poisoned with a reported case fatality rate of 10% (101/1,066). In Kenya, two outbreaks resulted in approximately 341 and 126 patients, with case fatality rates of 29% (100/341) and 21% (26/126), respectively. MSF launched an emergency team with international experts, medications and equipment, however, the outbreaks were resolving by the time of arrival. Interpretation Recognition of an outbreak of methanol poisoning and diagnosis seem to be the most challenging tasks, with significant delay from time of first presentations to public health warnings being issued. In spite of the rapid response from an emergency team, the outbreaks were nearly concluded by the time of arrival. A major impact on the outcome was not seen, but large educational trainings were conducted to increase awareness and knowledge about methanol poisoning. Based on this training, MSF was able to send a local emergency team during the second outbreak, supporting that such an approach could improve outcomes. Basic training, simplified treatment protocols, point-of-care diagnostic tools, and early support when needed, are likely the most important components to impact the consequences of methanol poisoning outbreaks in these challenging contexts. PMID:27030969

Factors determining dengue outbreak in Malaysia.

PubMed

Ahmad, Rohani; Suzilah, Ismail; Wan Najdah, Wan Mohamad Ali; Topek, Omar; Mustafakamal, Ibrahim; Lee, Han Lim

2018-01-01

A large scale study was conducted to elucidate the true relationship among entomological, epidemiological and environmental factors that contributed to dengue outbreak in Malaysia. Two large areas (Selayang and Bandar Baru Bangi) were selected in this study based on five consecutive years of high dengue cases. Entomological data were collected using ovitraps where the number of larvae was used to reflect Aedes mosquito population size; followed by RT-PCR screening to detect and serotype dengue virus in mosquitoes. Notified cases, date of disease onset, and number and type of the interventions were used as epidemiological endpoint, while rainfall, temperature, relative humidity and air pollution index (API) were indicators for environmental data. The field study was conducted during 81 weeks of data collection. Correlation and Autoregressive Distributed Lag Model were used to determine the relationship. The study showed that, notified cases were indirectly related with the environmental data, but shifted one week, i.e. last 3 weeks positive PCR; last 4 weeks rainfall; last 3 weeks maximum relative humidity; last 3 weeks minimum and maximum temperature; and last 4 weeks air pollution index (API), respectively. Notified cases were also related with next week intervention, while conventional intervention only happened 4 weeks after larvae were found, indicating ample time for dengue transmission. Based on a significant relationship among the three factors (epidemiological, entomological and environmental), estimated Autoregressive Distributed Lag (ADL) model for both locations produced high accuracy 84.9% for Selayang and 84.1% for Bandar Baru Bangi in predicting the actual notified cases. Hence, such model can be used in forestalling dengue outbreak and acts as an early warning system. The existence of relationships among the entomological, epidemiological and environmental factors can be used to build an early warning system for the prediction of dengue outbreak so that preventive interventions can be taken early to avert the outbreaks.

Effective Network Size Predicted From Simulations of Pathogen Outbreaks Through Social Networks Provides a Novel Measure of Structure-Standardized Group Size.

PubMed

McCabe, Collin M; Nunn, Charles L

2018-01-01

The transmission of infectious disease through a population is often modeled assuming that interactions occur randomly in groups, with all individuals potentially interacting with all other individuals at an equal rate. However, it is well known that pairs of individuals vary in their degree of contact. Here, we propose a measure to account for such heterogeneity: effective network size (ENS), which refers to the size of a maximally complete network (i.e., unstructured, where all individuals interact with all others equally) that corresponds to the outbreak characteristics of a given heterogeneous, structured network. We simulated susceptible-infected (SI) and susceptible-infected-recovered (SIR) models on maximally complete networks to produce idealized outbreak duration distributions for a disease on a network of a given size. We also simulated the transmission of these same diseases on random structured networks and then used the resulting outbreak duration distributions to predict the ENS for the group or population. We provide the methods to reproduce these analyses in a public R package, "enss." Outbreak durations of simulations on randomly structured networks were more variable than those on complete networks, but tended to have similar mean durations of disease spread. We then applied our novel metric to empirical primate networks taken from the literature and compared the information represented by our ENSs to that by other established social network metrics. In AICc model comparison frameworks, group size and mean distance proved to be the metrics most consistently associated with ENS for SI simulations, while group size, centralization, and modularity were most consistently associated with ENS for SIR simulations. In all cases, ENS was shown to be associated with at least two other independent metrics, supporting its use as a novel metric. Overall, our study provides a proof of concept for simulation-based approaches toward constructing metrics of ENS, while also revealing the conditions under which this approach is most promising.

Characterizing the reproduction number of epidemics with early subexponential growth dynamics

PubMed Central

Viboud, Cécile; Simonsen, Lone; Moghadas, Seyed M.

2016-01-01

Early estimates of the transmission potential of emerging and re-emerging infections are increasingly used to inform public health authorities on the level of risk posed by outbreaks. Existing methods to estimate the reproduction number generally assume exponential growth in case incidence in the first few disease generations, before susceptible depletion sets in. In reality, outbreaks can display subexponential (i.e. polynomial) growth in the first few disease generations, owing to clustering in contact patterns, spatial effects, inhomogeneous mixing, reactive behaviour changes or other mechanisms. Here, we introduce the generalized growth model to characterize the early growth profile of outbreaks and estimate the effective reproduction number, with no need for explicit assumptions about the shape of epidemic growth. We demonstrate this phenomenological approach using analytical results and simulations from mechanistic models, and provide validation against a range of empirical disease datasets. Our results suggest that subexponential growth in the early phase of an epidemic is the rule rather the exception. Mechanistic simulations show that slight modifications to the classical susceptible–infectious–removed model result in subexponential growth, and in turn a rapid decline in the reproduction number within three to five disease generations. For empirical outbreaks, the generalized-growth model consistently outperforms the exponential model for a variety of directly and indirectly transmitted diseases datasets (pandemic influenza, measles, smallpox, bubonic plague, cholera, foot-and-mouth disease, HIV/AIDS and Ebola) with model estimates supporting subexponential growth dynamics. The rapid decline in effective reproduction number predicted by analytical results and observed in real and synthetic datasets within three to five disease generations contrasts with the expectation of invariant reproduction number in epidemics obeying exponential growth. The generalized-growth concept also provides us a compelling argument for the unexpected extinction of certain emerging disease outbreaks during the early ascending phase. Overall, our approach promotes a more reliable and data-driven characterization of the early epidemic phase, which is important for accurate estimation of the reproduction number and prediction of disease impact. PMID:27707909

Challenges and Opportunities in Disease Forecasting in Outbreak Settings: A Case Study of Measles in Lola Prefecture, Guinea

PubMed Central

Graham, Matthew; Suk, Jonathan E.; Takahashi, Saki; Metcalf, C. Jessica; Jimenez, A. Paez; Prikazsky, Vladimir; Ferrari, Matthew J.; Lessler, Justin

2018-01-01

Abstract. We report on and evaluate the process and findings of a real-time modeling exercise in response to an outbreak of measles in Lola prefecture, Guinea, in early 2015 in the wake of the Ebola crisis. Multiple statistical methods for the estimation of the size of the susceptible (i.e., unvaccinated) population were applied to weekly reported measles case data on seven subprefectures throughout Lola. Stochastic compartmental models were used to project future measles incidence in each subprefecture in both an initial and a follow-up iteration of forecasting. Measles susceptibility among 1- to 5-year-olds was estimated to be between 24% and 43% at the beginning of the outbreak. Based on this high baseline susceptibility, initial projections forecasted a large outbreak occurring over approximately 10 weeks and infecting 40 children per 1,000. Subsequent forecasts based on updated data mitigated this initial projection, but still predicted a significant outbreak. A catch-up vaccination campaign took place at the same time as this second forecast and measles cases quickly receded. Of note, case reports used to fit models changed significantly between forecast rounds. Model-based projections of both current population risk and future incidence can help in setting priorities and planning during an outbreak response. A swiftly changing situation on the ground, coupled with data uncertainties and the need to adjust standard analytical approaches to deal with sparse data, presents significant challenges. Appropriate presentation of results as planning scenarios, as well as presentations of uncertainty and two-way communication, is essential to the effective use of modeling studies in outbreak response. PMID:29532773

Challenges and Opportunities in Disease Forecasting in Outbreak Settings: A Case Study of Measles in Lola Prefecture, Guinea.

PubMed

Graham, Matthew; Suk, Jonathan E; Takahashi, Saki; Metcalf, C Jessica; Jimenez, A Paez; Prikazsky, Vladimir; Ferrari, Matthew J; Lessler, Justin

2018-05-01

We report on and evaluate the process and findings of a real-time modeling exercise in response to an outbreak of measles in Lola prefecture, Guinea, in early 2015 in the wake of the Ebola crisis. Multiple statistical methods for the estimation of the size of the susceptible (i.e., unvaccinated) population were applied to weekly reported measles case data on seven subprefectures throughout Lola. Stochastic compartmental models were used to project future measles incidence in each subprefecture in both an initial and a follow-up iteration of forecasting. Measles susceptibility among 1- to 5-year-olds was estimated to be between 24% and 43% at the beginning of the outbreak. Based on this high baseline susceptibility, initial projections forecasted a large outbreak occurring over approximately 10 weeks and infecting 40 children per 1,000. Subsequent forecasts based on updated data mitigated this initial projection, but still predicted a significant outbreak. A catch-up vaccination campaign took place at the same time as this second forecast and measles cases quickly receded. Of note, case reports used to fit models changed significantly between forecast rounds. Model-based projections of both current population risk and future incidence can help in setting priorities and planning during an outbreak response. A swiftly changing situation on the ground, coupled with data uncertainties and the need to adjust standard analytical approaches to deal with sparse data, presents significant challenges. Appropriate presentation of results as planning scenarios, as well as presentations of uncertainty and two-way communication, is essential to the effective use of modeling studies in outbreak response.

Field experimental vaccination campaigns against myxomatosis and their effectiveness in the wild.

PubMed

Ferreira, Catarina; Ramírez, Esther; Castro, Francisca; Ferreras, Pablo; Alves, Paulo Célio; Redpath, Steve; Villafuerte, Rafael

2009-11-23

We conducted a field experiment in SW Spain to test the efficacy of a myxomatosis vaccine, a viral disease strongly affecting wild rabbit populations, by assessing individual survival and antibody seroprevalence of monthly live-trapped, vaccinated (N=466) and unvaccinated (N=558) juvenile wild rabbits, between April and October 2007. Eight percent of all juveniles caught from April to June showed maternal antibodies against myxomatosis, whereas all animals were seropositive to the disease after the outbreak. Juveniles vaccinated before the outbreak showed 17% higher survival (31% vs. 14%) and an increased mortality probability of 8% after the outbreak. Results suggest that only a costly and systematic vaccination performed before the annual myxomatosis outbreak, would improve the survival of juvenile rabbits, a premise not always accomplished that compromises its efficacy in the field.

Epidemiological Data Management during an Outbreak of Ebola Virus Disease: Key Issues and Observations from Sierra Leone.

PubMed

Owada, Kei; Eckmanns, Tim; Kamara, Kande-Bure O'Bai; Olu, Olushayo Oluseun

2016-01-01

Sierra Leone experienced intense transmission of Ebola virus disease (EVD) from May 2014 to November 2015 during which a total of 8,704 confirmed cases and over 3,589 confirmed deaths were reported. Our field observation showed many issues in the EVD data management system, which may have contributed to the magnitude and long duration of the outbreak. In this perspective article, we explain the key issues with EVD data management in the field, and the resulting obstacles in analyzing key epidemiological indicators during the outbreak response work. Our observation showed that, during the latter part of the EVD outbreak, surveillance and data management improved at all levels in the country as compared to the earlier stage. We identified incomplete filling and late arrival of the case investigation forms at data management centers, difficulties in detecting double entries and merging identified double entries in the database, and lack of clear process of how death of confirmed cases in holding, treatment, and community care centers are reported to the data centers as some of challenges to effective data management. Furthermore, there was no consolidated database that captured and linked all data sources in a structured way. We propose development of a new application tool easily adaptable to new occurrences, regular data harmonization meetings between national and district data management teams, and establishment of a data quality audit system to assure good quality data as ways to improve EVD data management during future outbreaks.

The Super Tuesday Outbreak: Forecast Sensitivities to Single-Moment Microphysics Schemes

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Case, Jonathan L.; Dembek, Scott R.; Jedlovec, Gary J.; Lapenta, William M.

2008-01-01

Forecast precipitation and radar characteristics are used by operational centers to guide the issuance of advisory products. As operational numerical weather prediction is performed at increasingly finer spatial resolution, convective precipitation traditionally represented by sub-grid scale parameterization schemes is now being determined explicitly through single- or multi-moment bulk water microphysics routines. Gains in forecasting skill are expected through improved simulation of clouds and their microphysical processes. High resolution model grids and advanced parameterizations are now available through steady increases in computer resources. As with any parameterization, their reliability must be measured through performance metrics, with errors noted and targeted for improvement. Furthermore, the use of these schemes within an operational framework requires an understanding of limitations and an estimate of biases so that forecasters and model development teams can be aware of potential errors. The National Severe Storms Laboratory (NSSL) Spring Experiments have produced daily, high resolution forecasts used to evaluate forecast skill among an ensemble with varied physical parameterizations and data assimilation techniques. In this research, high resolution forecasts of the 5-6 February 2008 Super Tuesday Outbreak are replicated using the NSSL configuration in order to evaluate two components of simulated convection on a large domain: sensitivities of quantitative precipitation forecasts to assumptions within a single-moment bulk water microphysics scheme, and to determine if these schemes accurately depict the reflectivity characteristics of well-simulated, organized, cold frontal convection. As radar returns are sensitive to the amount of hydrometeor mass and the distribution of mass among variably sized targets, radar comparisons may guide potential improvements to a single-moment scheme. In addition, object-based verification metrics are evaluated for their utility in gauging model performance and QPF variability.

Model-specification uncertainty in future forest pest outbreak.

PubMed

Boulanger, Yan; Gray, David R; Cooke, Barry J; De Grandpré, Louis

2016-04-01

Climate change will modify forest pest outbreak characteristics, although there are disagreements regarding the specifics of these changes. A large part of this variability may be attributed to model specifications. As a case study, we developed a consensus model predicting spruce budworm (SBW, Choristoneura fumiferana [Clem.]) outbreak duration using two different predictor data sets and six different correlative methods. The model was used to project outbreak duration and the uncertainty associated with using different data sets and correlative methods (=model-specification uncertainty) for 2011-2040, 2041-2070 and 2071-2100, according to three forcing scenarios (RCP 2.6, RCP 4.5 and RCP 8.5). The consensus model showed very high explanatory power and low bias. The model projected a more important northward shift and decrease in outbreak duration under the RCP 8.5 scenario. However, variation in single-model projections increases with time, making future projections highly uncertain. Notably, the magnitude of the shifts in northward expansion, overall outbreak duration and the patterns of outbreaks duration at the southern edge were highly variable according to the predictor data set and correlative method used. We also demonstrated that variation in forcing scenarios contributed only slightly to the uncertainty of model projections compared with the two sources of model-specification uncertainty. Our approach helped to quantify model-specification uncertainty in future forest pest outbreak characteristics. It may contribute to sounder decision-making by acknowledging the limits of the projections and help to identify areas where model-specification uncertainty is high. As such, we further stress that this uncertainty should be strongly considered when making forest management plans, notably by adopting adaptive management strategies so as to reduce future risks. © 2015 Her Majesty the Queen in Right of Canada Global Change Biology © 2015 Published by John Wiley & Sons Ltd Reproduced with the permission of the Minister of Natural Resources Canada.

Spatio-temporal epidemiology of the cholera outbreak in Papua New Guinea, 2009-2011.

PubMed

Horwood, Paul F; Karl, Stephan; Mueller, Ivo; Jonduo, Marinjho H; Pavlin, Boris I; Dagina, Rosheila; Ropa, Berry; Bieb, Sibauk; Rosewell, Alexander; Umezaki, Masahiro; Siba, Peter M; Greenhill, Andrew R

2014-08-20

Cholera continues to be a devastating disease in many developing countries where inadequate safe water supply and poor sanitation facilitate spread. From July 2009 until late 2011 Papua New Guinea experienced the first outbreak of cholera recorded in the country, resulting in >15,500 cases and >500 deaths. Using the national cholera database, we analysed the spatio-temporal distribution and clustering of the Papua New Guinea cholera outbreak. The Kulldorff space-time permutation scan statistic, contained in the software package SatScan v9.2 was used to describe the first 8 weeks of the outbreak in Morobe Province before cholera cases spread throughout other regions of the country. Data were aggregated at the provincial level to describe the spread of the disease to other affected provinces. Spatio-temporal and cluster analyses revealed that the outbreak was characterized by three distinct phases punctuated by explosive propagation of cases when the outbreak spread to a new region. The lack of road networks across most of Papua New Guinea is likely to have had a major influence on the slow spread of the disease during this outbreak. Identification of high risk areas and the likely mode of spread can guide government health authorities to formulate public health strategies to mitigate the spread of the disease through education campaigns, vaccination, increased surveillance in targeted areas and interventions to improve water, sanitation and hygiene.

Hot spots in a wired world: WHO surveillance of emerging and re-emerging infectious diseases.

PubMed

Heymann, D L; Rodier, G R

2001-12-01

The resurgence of the microbial threat, rooted in several recent trends, has increased the vulnerability of all nations to the risk of infectious diseases, whether newly emerging, well-established, or deliberately caused. Infectious disease intelligence, gleaned through sensitive surveillance, is the best defence. The epidemiological and laboratory techniques needed to detect, investigate, and contain a deliberate outbreak are the same as those used for natural outbreaks. In April 2000, WHO formalised an infrastructure (the Global Outbreak Alert and Response Network) for responding to the heightened need for early awareness of outbreaks and preparedness to respond. The Network, which unites 110 existing networks, is supported by several new mechanisms and a computer-driven tool for real time gathering of disease intelligence. The procedure for outbreak alert and response has four phases: systematic detection, outbreak verification, real time alerts, and rapid response. For response, the framework uses different strategies for combating known risks and unexpected events, and for improving both global and national preparedness. New forces at work in an electronically interconnected world are beginning to break down the traditional reluctance of countries to report outbreaks due to fear of the negative impact on trade and tourism. About 65% of the world's first news about infectious disease events now comes from informal sources, including press reports and the internet.

Bayesian Reconstruction of Disease Outbreaks by Combining Epidemiologic and Genomic Data

PubMed Central

Jombart, Thibaut; Cori, Anne; Didelot, Xavier; Cauchemez, Simon; Fraser, Christophe; Ferguson, Neil

2014-01-01

Recent years have seen progress in the development of statistically rigorous frameworks to infer outbreak transmission trees (“who infected whom”) from epidemiological and genetic data. Making use of pathogen genome sequences in such analyses remains a challenge, however, with a variety of heuristic approaches having been explored to date. We introduce a statistical method exploiting both pathogen sequences and collection dates to unravel the dynamics of densely sampled outbreaks. Our approach identifies likely transmission events and infers dates of infections, unobserved cases and separate introductions of the disease. It also proves useful for inferring numbers of secondary infections and identifying heterogeneous infectivity and super-spreaders. After testing our approach using simulations, we illustrate the method with the analysis of the beginning of the 2003 Singaporean outbreak of Severe Acute Respiratory Syndrome (SARS), providing new insights into the early stage of this epidemic. Our approach is the first tool for disease outbreak reconstruction from genetic data widely available as free software, the R package outbreaker. It is applicable to various densely sampled epidemics, and improves previous approaches by detecting unobserved and imported cases, as well as allowing multiple introductions of the pathogen. Because of its generality, we believe this method will become a tool of choice for the analysis of densely sampled disease outbreaks, and will form a rigorous framework for subsequent methodological developments. PMID:24465202

Learning from Ebola Virus: How to Prevent Future Epidemics

PubMed Central

Kekulé, Alexander S.

2015-01-01

The recent Ebola virus disease (EVD) epidemic in Guinea, Liberia and Sierra Leone demonstrated that the World Health Organization (WHO) is incapable to control outbreaks of infectious diseases in less developed regions of the world. This essay analyses the causes for the failure of the international response and proposes four measures to improve resilience, early detection and response to future outbreaks of infectious diseases. PMID:26184283

Norovirus genotypes causing gastroenteritis outbreaks in Finland 1998-2002.

PubMed

Maunula, L; Von Bonsdorff, C-H

2005-11-01

Outbreak investigation methods for enteric viruses were improved in 1990s when gene amplification techniques were established in viral laboratories. The objective of the study was to determine the causative agents for Finnish viral gastroenteritis outbreaks. Our aim was also to further characterise the norovirus strains, reveal the temporal occurrence of norovirus (NV) genotypes and to study some epidemiological aspects concerning the outbreaks. A total of 416 Finnish viral gastroenteritis outbreaks that occurred during 5 years (1998-2002), excluding those among hospitalised children, were investigated for enteric viruses. Stool samples were screened by electron microscopy as well as analyzed by specific noro- and astrovirus RT-PCR tests. Amplicon sequence analysis was used to find out norovirus genotypes. Noroviruses caused 252 (60.6%) of the outbreaks; other viruses, astro- or rotavirus, caused four epidemics. Norovirus epidemics occurred in all kinds of settings, most often in hospitals (30.6%) and in restaurants and canteens (14.3%). Both NV genogroups were found every year, but NV GGII outbreaks always outnumbered those of GGI. All but one outbreak at hospitals and nursing homes were of genotype GII. Polymerase sequence analysis revealed a variety of NV genotypes; six GI and at least eight GII genotypes. The GI.3 Birmingham-like and GII.4 Bristol-like genotype appeared every year, whereas the other types were circulating for shorter periods or sporadically. During the study period the genotypes GII.4 (Bristol), GII.1 (Hawaii), an emerging genotype GIIb, and a new variant of GII.4 predominated in that order. Indication for rapid genetic changes in the genotype GII.4 was also noticed. Noroviruses were the most prevalent causative agents in the outbreaks. Many NV genotypes were circulating, and a shift in the predominant genotypes was evident between epidemic seasons.

Polio infrastructure strengthened disease outbreak preparedness and response in the WHO African Region.

PubMed

Kouadio, Koffi; Okeibunor, Joseph; Nsubuga, Peter; Mihigo, Richard; Mkanda, Pascal

2016-10-10

The continuous deployments of polio resources, infrastructures and systems for responding to other disease outbreaks in many African countries has led to a number of lessons considered as best practice that need to be documented for strengthening preparedness and response activities in future outbreaks. We reviewed and documented the influence of polio best practices in outbreak preparedness and response in Angola, Nigeria and Ethiopia. Data from relevant programmes of the WHO African Region were also analyzed to demonstrate clearly the relative contributions of PEI resources and infrastructure to effective disease outbreak preparedness and response. Polio resources including, human, financial, and logistic, tool and strategies have tremendously contributed to responding to diseases outbreaks across the African region. In Angola, Nigeria and Ethiopia, many disease epidemics including Marburg Hemorrhagic fever, Dengue fever, Ebola Virus Diseases (EVD), Measles, Anthrax and Shigella have been controlled using existing polio Eradication Initiatives resources. Polio staffs are usually deployed in occasions to supports outbreak response activities (coordination, surveillance, contact tracing, case investigation, finance, data management, etc.). Polio logistics such vehicles, laboratories were also used in the response activities to other infectious diseases. Many polio tools including micro planning, dashboard, guidelines, SOPs on preparedness and response have also benefited to other epidemic-prone diseases. The Countries' preparedness and response plan to WPV importation as well as the Polio Emergency Operation Center models were successfully used to develop, strengthen and respond to many other diseases outbreak with the implication of partners and the strong leadership and ownership of governments. This review has important implications for WHO/AFRO initiative to strengthening and improving disease outbreak preparedness and responses in the African Region in respect to the international health regulations core capacities. Copyright © 2016 World Health Organization Regional Office for Africa. Published by Elsevier Ltd.. All rights reserved.

Infection prevention and control interventions in the first outbreak of methicillin-resistant Staphylococcus aureus infections in an equine hospital in Sweden.

PubMed

Bergström, Karin; Nyman, Görel; Widgren, Stefan; Johnston, Christopher; Grönlund-Andersson, Ulrika; Ransjö, Ulrika

2012-03-08

The first outbreak of methicillin-resistant Staphylococcus aureus (MRSA) infection in horses in Sweden occurred in 2008 at the University Animal Hospital and highlighted the need for improved infection prevention and control. The present study describes interventions and infection prevention control in an equine hospital setting July 2008 - April 2010. This descriptive study of interventions is based on examination of policy documents, medical records, notes from meetings and cost estimates. MRSA cases were identified through clinical sampling and telephone enquiries about horses post-surgery. Prospective sampling in the hospital environment with culture for MRSA and genotyping of isolates by spa-typing and pulsed-field gel electrophoresis (PFGE) were performed. Interventions focused on interruption of indirect contact spread of MRSA between horses via staff and equipment and included: Temporary suspension of elective surgery; and identification and isolation of MRSA-infected horses; collaboration was initiated between authorities in animal and human public health, human medicine infection control and the veterinary hospital; extensive cleaning and disinfection was performed; basic hygiene and cleaning policies, staff training, equipment modification and interior renovation were implemented over seven months.Ten (11%) of 92 surfaces sampled between July 2008 and April 2010 tested positive for MRSA spa-type 011, seven of which were from the first of nine sampling occasions. PFGE typing showed the isolates to be the outbreak strain (9 of 10) or a closely related strain. Two new cases of MRSA infection occurred 14 and 19 months later, but had no proven connections to the outbreak cases. Collaboration between relevant authorities and the veterinary hospital and formation of an infection control committee with an executive working group were required to move the intervention process forward. Support from hospital management and the dedication of staff were essential for the development and implementation of new, improved routines. Demonstration of the outbreak strain in the environment was useful for interventions such as improvement of cleaning routines and interior design, and increased compliance with basic hygienic precautions. The interventions led to a reduction in MRSA-positive samples and the outbreak was considered curbed as no new cases occurred for over a year.

Forecasting outbreaks of the Douglas-fir tussock moth from lower crown cocoon samples.

Treesearch

Richard R. Mason; Donald W. Scott; H. Gene Paul

1993-01-01

A predictive technique using a simple linear regression was developed to forecast the midcrown density of small tussock moth larvae from estimates of cocoon density in the previous generation. The regression estimator was derived from field samples of cocoons and larvae taken from a wide range of nonoutbreak tussock moth populations. The accuracy of the predictions was...

Predicting the spread of sudden oak death in California: spatial-temporal modeling of susceptible-infectious transitions

Treesearch

Richard D. Hunter; Ross K. Meentemeyer; David M. Rizzo; Christopher A. Gilligan

2008-01-01

The number of emerging infectious diseases is thought to be increasing worldwide - many of which are caused by non-native, invasive plant pathogens I n forest ecosystems. As new diseases continue to emerge, the ability to predict disease outbreaks is critical for effective management and prevention of epidemics, especially in complex spatially heterogeneous landscapes...

Agroterrorism: where are we in the ongoing war on terrorism?

PubMed

Crutchley, Tamara M; Rodgers, Joel B; Whiteside, Heustis P; Vanier, Marty; Terndrup, Thomas E

2007-03-01

The U.S. agricultural infrastructure is one of the most productive and efficient food-producing systems in the world. Many of the characteristics that contribute to its high productivity and efficiency also make this infrastructure extremely vulnerable to a terrorist attack by a biological weapon. Several experts have repeatedly stated that taking advantage of these vulnerabilities would not require a significant undertaking and that the nation's agricultural infrastructure remains highly vulnerable. As a result of continuing criticism, many initiatives at all levels of government and within the private sector have been undertaken to improve our ability to detect and respond to an agroterrorist attack. However, outbreaks, such as the 1999 West Nile outbreak, the 2001 anthrax attacks, the 2003 monkeypox outbreak, and the 2004 Escherichia coli O157:H7 outbreak, have demonstrated the need for improvements in the areas of communication, emergency response and surveillance efforts, and education for all levels of government, the agricultural community, and the private sector. We recommend establishing an interdisciplinary advisory group that consists of experts from public health, human health, and animal health communities to prioritize improvement efforts in these areas. The primary objective of this group would include establishing communication, surveillance, and education benchmarks to determine current weaknesses in preparedness and activities designed to mitigate weaknesses. We also recommend broader utilization of current food and agricultural preparedness guidelines, such as those developed by the U.S. Department of Agriculture and the U.S. Food and Drug Administration.

Bayesian data assimilation provides rapid decision support for vector-borne diseases.

PubMed

Jewell, Chris P; Brown, Richard G

2015-07-06

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host-vector-pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Detecting plague-host abundance from space: Using a spectral vegetation index to identify occupancy of great gerbil burrows

NASA Astrophysics Data System (ADS)

Wilschut, Liesbeth I.; Heesterbeek, Johan A. P.; Begon, Mike; de Jong, Steven M.; Ageyev, Vladimir; Laudisoit, Anne; Addink, Elisabeth A.

2018-02-01

In Kazakhstan, plague outbreaks occur when its main host, the great gerbil, exceeds an abundance threshold. These live in family groups in burrows, which can be mapped using remote sensing. Occupancy (percentage of burrows occupied) is a good proxy for abundance and hence the possibility of an outbreak. Here we use time series of satellite images to estimate occupancy remotely. In April and September 2013, 872 burrows were identified in the field as either occupied or empty. For satellite images acquired between April and August, 'burrow objects' were identified and matched to the field burrows. The burrow objects were represented by 25 different polygon types, then classified (using a majority vote from 10 Random Forests) as occupied or empty, using Normalized Difference Vegetation Indices (NDVI) calculated for all images. Throughout the season NDVI values were higher for empty than for occupied burrows. Occupancy status of individual burrows that were continuously occupied or empty, was classified with producer's and user's accuracy values of 63 and 64% for the optimum polygon. Occupancy level was predicted very well and differed 2% from the observed occupancy. This establishes firmly the principle that occupancy can be estimated using satellite images with the potential to predict plague outbreaks over extensive areas with much greater ease and accuracy than previously.

Continuation of Novel Analytical and Empircal Approaches to the Origin and Prediction of Pathogenicity

DTIC Science & Technology

2014-01-31

properties of strains as encoded by key mutations (e.g., E627K in PB2 in H5N1 or H7N9 , which confers increased replication in mammals) (Janies et al...the recent outbreak of H7N9 in China. We have created SUPRAMAPs and ROUTEMAPs describing the host and geographic origins and movement of each...segment. Here is a summary of our results:  The China-Taiwan H7N9 outbreak in early 2013 was caused by a reassortant virus made primarily of genetic

Using Remote Sensing, Weather, and Demographic Data to Create Risk Maps for Zika, Dengue, and Chikungunya in Brazil

NASA Astrophysics Data System (ADS)

Manore, C.; Conrad, J.; Del Valle, S.; Ziemann, A.; Fairchild, G.; Generous, E. N.

2017-12-01

Mosquito-borne diseases such as Zika, dengue, and chikungunya viruses have dynamics coupled to weather, ecology, human infrastructure, socio-economic demographics, and behavior. We use time-varying remote sensing and weather data, along with demographics and ecozones to predict risk through time for Zika, dengue, and chikungunya outbreaks in Brazil. We use distributed lag methods to quantify the lag between outbreaks and weather. Our statistical model indicates that the relationships between the variables are complex, but that quantifying risk is possible with the right data at appropriate spatio-temporal scales.

[Expected epidemic risk from the minimum drinking water content of protozoan agents of enteric parasitic diseases].

PubMed

Novosil'tsev, G I; Chernyshenko, A I; Rusanova, N A; Gracheva, M N; Mel'nikova, L I

2010-01-01

The paper presents information on lambliasis and cryptosporidiosis outbreaks associated with drinking water contamination-associated. It discusses a risk for the emergence of mass outbreaks of lambliasis and cryptosporidiosis among the population of the municipalities of administrative district centers and other human settlements, which are to exercise sanitary and parasitological control over the quality of water of its centralized drinking supply. The significance of this water contamination by lamblia cysts and cryptosporidium oocysts is considered. Calculations are given to predict an epidemic risk and possible ways of its prevention.

Remote Sensing Contributions to Prediction and Risk Assessment of Natural Disasters Caused by Large Scale Rift Valley Fever Outbreaks

NASA Technical Reports Server (NTRS)

Anyamba, Assaf; Linthicum, Kenneth J.; Small, Jennifer; Britch, S. C.; Tucker, C. J.

2012-01-01

Remotely sensed vegetation measurements for the last 30 years combined with other climate data sets such as rainfall and sea surface temperatures have come to play an important role in the study of the ecology of arthropod-borne diseases. We show that epidemics and epizootics of previously unpredictable Rift Valley fever are directly influenced by large scale flooding associated with the El Ni o/Southern Oscillation. This flooding affects the ecology of disease transmitting arthropod vectors through vegetation development and other bioclimatic factors. This information is now utilized to monitor, model, and map areas of potential Rift Valley fever outbreaks and is used as an early warning system for risk reduction of outbreaks to human and animal health, trade, and associated economic impacts. The continuation of such satellite measurements is critical to anticipating, preventing, and managing disease epidemics and epizootics and other climate-related disasters.

Complex social contagion makes networks more vulnerable to disease outbreaks.

PubMed

Campbell, Ellsworth; Salathé, Marcel

2013-01-01

Social network analysis is now widely used to investigate the dynamics of infectious disease spread. Vaccination dramatically disrupts disease transmission on a contact network, and indeed, high vaccination rates can potentially halt disease transmission altogether. Here, we build on mounting evidence that health behaviors - such as vaccination, and refusal thereof - can spread across social networks through a process of complex contagion that requires social reinforcement. Using network simulations that model health behavior and infectious disease spread, we find that under otherwise identical conditions, the process by which the health behavior spreads has a very strong effect on disease outbreak dynamics. This dynamic variability results from differences in the topology within susceptible communities that arise during the health behavior spreading process, which in turn depends on the topology of the overall social network. Our findings point to the importance of health behavior spread in predicting and controlling disease outbreaks.

Vulnerability of a killer whale social network to disease outbreaks

NASA Astrophysics Data System (ADS)

Guimarães, Paulo R., Jr.; de Menezes, Márcio Argollo; Baird, Robin W.; Lusseau, David; Guimarães, Paulo; Dos Reis, Sérgio F.

2007-10-01

Emerging infectious diseases are among the main threats to conservation of biological diversity. A crucial task facing epidemiologists is to predict the vulnerability of populations of endangered animals to disease outbreaks. In this context, the network structure of social interactions within animal populations may affect disease spreading. However, endangered animal populations are often small and to investigate the dynamics of small networks is a difficult task. Using network theory, we show that the social structure of an endangered population of mammal-eating killer whales is vulnerable to disease outbreaks. This feature was found to be a consequence of the combined effects of the topology and strength of social links among individuals. Our results uncover a serious challenge for conservation of the species and its ecosystem. In addition, this study shows that the network approach can be useful to study dynamical processes in very small networks.

Environmental Factors Influencing Epidemic Cholera

PubMed Central

Jutla, Antarpreet; Whitcombe, Elizabeth; Hasan, Nur; Haley, Bradd; Akanda, Ali; Huq, Anwar; Alam, Munir; Sack, R. Bradley; Colwell, Rita

2013-01-01

Cholera outbreak following the earthquake of 2010 in Haiti has reaffirmed that the disease is a major public health threat. Vibrio cholerae is autochthonous to aquatic environment, hence, it cannot be eradicated but hydroclimatology-based prediction and prevention is an achievable goal. Using data from the 1800s, we describe uniqueness in seasonality and mechanism of occurrence of cholera in the epidemic regions of Asia and Latin America. Epidemic regions are located near regional rivers and are characterized by sporadic outbreaks, which are likely to be initiated during episodes of prevailing warm air temperature with low river flows, creating favorable environmental conditions for growth of cholera bacteria. Heavy rainfall, through inundation or breakdown of sanitary infrastructure, accelerates interaction between contaminated water and human activities, resulting in an epidemic. This causal mechanism is markedly different from endemic cholera where tidal intrusion of seawater carrying bacteria from estuary to inland regions, results in outbreaks. PMID:23897993

Effects of human and mosquito migrations on the dynamical behavior of the spread of malaria

NASA Astrophysics Data System (ADS)

Beay, Lazarus Kalvein; Kasbawati, Toaha, Syamsuddin

2017-03-01

Malaria is one of infectious diseases which become the main public health problem especially in Indonesia. Mathematically, the spread of malaria can be modeled to predict the outbreak of the disease. This research studies about mathematical model of the spread of malaria which takes into consideration the migration of human and mosquito populations. By determining basic reproduction number of the model, we analyze effects of migration parameter with respect to the reduction of malaria outbreak. Sensitivity analysis of basic reproduction number shows that mosquito migration has greater effect in reducing the outbreak of malaria compared with human migration. Basic reproduction number of the model is monotonically decreasing as mosquito migration increasing. We then confirm the analytic result by doing numerical simulation. The results show that migrations in human and mosquito populations have big influences in eliminating and eradicating the disease from the system.

Credit market Jitters in the course of the financial crisis: A permutation entropy approach in measuring informational efficiency in financial assets

NASA Astrophysics Data System (ADS)

Siokis, Fotios M.

2018-06-01

We explore the evolution of the informational efficiency for specific instruments of the U.S. money, bond and stock exchange markets, prior and after the outbreak of the Great Recession. We utilize the permutation entropy and the complexity-entropy causality plane to rank the time series and measure the degree of informational efficiency. We find that after the credit crunch and the collapse of Lehman Brothers the efficiency level of specific money market instruments' yield falls considerably. This is an evidence of less uncertainty included in predicting the related yields throughout the financial disarray. Similar trend is depicted in the indices of the stock exchange markets but efficiency remains in much higher levels. On the other hand, bond market instruments maintained their efficiency levels even after the outbreak of the crisis, which could be interpreted into greater randomness and less predictability of their yields.

Diagnostic schemes for reducing epidemic size of African viral hemorrhagic fever outbreaks.

PubMed

Okeke, Iruka N; Manning, Robert S; Pfeiffer, Thomas

2014-09-12

Viral hemorrhagic fever (VHF) outbreaks, with high mortality rates, have often been amplified in African health institutions due to person-to-person transmission via infected body fluids.  By collating and analyzing epidemiological data from documented outbreaks, we observed that diagnostic delay contributes to epidemic size for Ebola and Marburg hemorrhagic fever outbreaks. We used a susceptible-exposed-infectious-removed (SEIR) model and data from the 1995 outbreak in Kikwit, Democratic Republic of Congo, to simulate Ebola hemorrhagic fever epidemics. Our model allows us to describe the dynamics for hospital staff separately from that for the general population, and to implement health worker-specific interventions. The model illustrates that implementing World Health Organization/US Centers for Disease Control and Prevention guidelines of isolating patients who do not respond to antimalarial and antibacterial chemotherapy reduces total outbreak size, from a median of 236, by 90% or more. Routinely employing diagnostic testing in post-mortems of patients that died of refractory fevers reduces the median outbreak size by a further 60%. Even greater reductions in outbreak size were seen when all febrile patients were tested for endemic infections or when febrile health-care workers were tested.  The effect of testing strategies was not impaired by the 1-3 day delay that would occur if testing were performed by a reference laboratory. In addition to improving the quality of care for common causes of febrile infections, increased and strategic use of laboratory diagnostics for fever could reduce the chance of hospital amplification of VHFs in resource-limited African health systems.

Assessing Different Causes of Crown-of-Thorns Starfish Outbreaks and Appropriate Responses for Management on the Great Barrier Reef

PubMed Central

Babcock, Russell C.; Dambacher, Jeffrey M.; Morello, Elisabetta B.; Plagányi, Éva E.; Hayes, Keith R.; Sweatman, Hugh P. A.; Pratchett, Morgan S.

2016-01-01

The crown-of-thorns starfish Acanthaster planci (COTS) has contributed greatly to declines in coral cover on Australia’s Great Barrier Reef, and remains one of the major acute disturbances on Indo-Pacific coral reefs. Despite uncertainty about the underlying causes of outbreaks and the management responses that might address them, few studies have critically and directly compared competing hypotheses. This study uses qualitative modelling to compare hypotheses relating to outbreak initiation, explicitly considering the potential role of positive feedbacks, elevated nutrients, and removal of starfish predators by fishing. When nutrients and fishing are considered in isolation, the models indicate that a range of alternative hypotheses are capable of explaining outbreak initiation with similar levels of certainty. The models also suggest that outbreaks may be caused by multiple factors operating simultaneously, rather than by single proximal causes. As the complexity and realism of the models increased, the certainty of outcomes decreased, but key areas that require further research to improve the structure of the models were identified. Nutrient additions were likely to result in outbreaks only when COTS larvae alone benefitted from nutrients. Similarly, the effects of fishing on the decline of corals depended on the complexity of interactions among several categories of fishes. Our work suggests that management approaches which seek to be robust to model structure uncertainty should allow for multiple potential causes of outbreaks. Monitoring programs can provide tests of alternative potential causes of outbreaks if they specifically monitor all key taxa at reefs that are exposed to appropriate combinations of potential causal factors. PMID:28036360

Decreasing influenza impact in lodges: 1997-2000 Calgary Regional Health Authority.

PubMed

McLeod, L; Lau, W W

2001-01-01

Influenza causes high morbidity and hospitalization rates in residents of seniors lodges, I causing increased pressure on emergency departments and hospital beds every winter. This quasi-experimental study assessed the prevention of influenza outbreaks and their consequences in Calgary lodges. A multidisciplinary team worked to improve communication between health professionals, increase resident and staff immunization coverage, obtain weights and creatinines prior to influenza season, and facilitate amantadine prophylaxis during influenza A outbreaks. We had an increase in standing orders for amantadine and up to 56% of residents from one lodge had documented creatinine levels. Amantadine was administered to residents within two days of outbreak notification. Influenza morbidity in lodge outbreaks decreased from a rate of 37% to 9% over the three years and hospitalization rates decreased from 9% to 1%. We recommend that other regions consider a similar approach to decreasing influenza morbidity and hospitalization in lodge residents.

Revisiting Ebola, a quiet river in the heart of Africa.

PubMed

Gonzalez, J P; Wauquier, N; Vincent, T

2018-02-01

In 1995, 20 years after the first known Ebola outbreak, one of us (JPG) wrote an editorial about Ebola Virus Disease that captured the knowledge and attitudes toward viral diseases of that time and discussed the future of viruses in our world. Now, 21 years later, in the wake of the West African Ebola pandemic of 2013-2016, and after 22 other Ebola outbreaks, we revisit the article to determine if knowledge, attitudes, and practices have changed. We conclude that the necessary infrastructures (surveillance, financial, treatment/preventative health) have improved with each outbreak, and knowledge of the virus (vaccines, therapies, diagnostics) has increased. However, the global reach of the virus has also increased due to expanded means of global transportation. Furthermore, the knowledge of the virus that has increased with each outbreak has not translated into the necessary marginal increase in preparedness; we do not seem to be learning from our mistakes.

Rethinking the Response to Emerging Microbes: Vaccines and Therapeutics in the Ebola Era--a Conference at Harvard Medical School.

PubMed

Knipe, David M; Whelan, Sean P

2015-08-01

Harvard Medical School convened a meeting of biomedical and clinical experts on 5 March 2015 on the topic of "Rethinking the Response to Emerging Microbes: Vaccines and Therapeutics in the Ebola Era," with the goals of discussing the lessons from the recent Ebola outbreak and using those lessons as a case study to aid preparations for future emerging infections. The speakers and audience discussed the special challenges in combatting an infectious agent that causes sporadic outbreaks in resource-poor countries. The meeting led to a call for improved basic medical care for all and continued support of basic discovery research to provide the foundation for preparedness for future outbreaks in addition to the targeted emergency response to outbreaks and targeted research programs against Ebola virus and other specific emerging pathogens. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Ebola: is the response justified?

PubMed Central

Lewis, Hannah; Chaudry, Aisha; Ndow, Gibril; Crossey, Mary ME; Garside, Debbie; Njie, Ramou; Taylor-Robinson, Simon D

2015-01-01

Ebola virus disease is a viral hemorrhagic fever, first discovered in 1976 in Sudan, where the outbreak infected over 284 people with a 53% case fatality ratio. There have been 34 further epidemics, the current major incident in West Africa having recorded more cases and deaths than all previous outbreaks combined. To date there have been over 27, 000 confirmed, probable and suspected cases and 11,000 reported deaths in Liberia, Guinea and Sierra Leone. With total funding and pledges to help control the outbreak amounting to more than US$2.4billion, many question how the disease has continued to spread in Sierra Leone and Guinea Conakry, and whether the response to the outbreak has been justified. This article aims to analyze the effectiveness of the responses to the outbreak in terms of economic, social, cultural and, to an extent, political impact. We argue that the response has been justified due to the awareness raised, the infrastructure and staffing improvements, the success in receiving financial aid and the minimal spread to other countries outside the main transmission zone. Despite this, some failures in communication and a slow early response were noted. PMID:26740851

Hepatitis A outbreak in Ba subdivision, Fiji, October-December 2013.

PubMed

Getahun, Aneley; Rafai, Eric; Tolosa, Maria Ximena; Dawainavesi, Akanisi; Tabua, Anaseini Maisema; Tabua, Josefa

2015-01-01

A cluster of suspected hepatitis A cases was notified to the Fiji Ministry of Health on 22 October 2013. An outbreak investigation team was mobilized to confirm the existence of an outbreak of hepatitis A and advise appropriate public health interventions. A case definition for the outbreak investigation was established, and standardized data collection tools were used to collect information on clinical presentation and risk factors. An environmental assessment was also conducted. There were 160 clinical cases of hepatitis A of which 15 were laboratory-confirmed. The attack rate was 349 per 10,000 population in the Nukuloa nursing zone; there were no reported deaths. Residents of the Nukuloa settlement were 6.6 times more likely to present with symptomatic hepatitis A infection (95% confidence interval: 3.8-12.6) compared with residents of another village with a different water supply. This is the first significant hepatitis A outbreak documented in Ba subdivision and possibly in Fiji. Enhanced surveillance of hepatitis A may reveal other clusters in the country. Improving the primary water source dramatically reduced the occurance of disease in the affected community and adjacent areas.

Analytical report of the 2016 dengue outbreak in Córdoba city, Argentina.

PubMed

Rotela, Camilo; Lopez, Laura; Frías Céspedes, María; Barbas, Gabriela; Lighezzolo, Andrés; Porcasi, Ximena; Lanfri, Mario A; Scavuzzo, Carlos M; Gorla, David E

2017-11-06

After elimination of the Aedes aegypti vector in South America in the 1960s, dengue outbreaks started to reoccur during the 1990s; strongly in Argentina since 1998. In 2016, Córdoba City had the largest dengue outbreak in its history. In this article we report this outbreak including spatio-temporal analysis of cases and vectors in the city. A total of 653 dengue cases were recorded by the laboratory-based dengue surveillance system and georeferenced by their residential addresses. Case maps were generated from the epidemiological week 1 (beginning of January) to week 19 (mid-May). Dengue outbreak temporal evolution was analysed globally and three specific, high-incidence zones were detected using Knox analysis to characterising its spatio-temporal attributes. Field and remotely sensed data were collected and analysed in real time and a vector presence map based on the MaxEnt approach was generated to define hotspots, towards which the pesticide- based strategy was then targeted. The recorded pattern of cases evolution within the community suggests that dengue control measures should be improved.

Ebola: is the response justified?

PubMed

Lewis, Hannah; Chaudry, Aisha; Ndow, Gibril; Crossey, Mary Me; Garside, Debbie; Njie, Ramou; Taylor-Robinson, Simon D

2015-01-01

Ebola virus disease is a viral hemorrhagic fever, first discovered in 1976 in Sudan, where the outbreak infected over 284 people with a 53% case fatality ratio. There have been 34 further epidemics, the current major incident in West Africa having recorded more cases and deaths than all previous outbreaks combined. To date there have been over 27, 000 confirmed, probable and suspected cases and 11,000 reported deaths in Liberia, Guinea and Sierra Leone. With total funding and pledges to help control the outbreak amounting to more than US $2.4 billion, many question how the disease has continued to spread in Sierra Leone and Guinea Conakry, and whether the response to the outbreak has been justified. This article aims to analyze the effectiveness of the responses to the outbreak in terms of economic, social, cultural and, to an extent, political impact. We argue that the response has been justified due to the awareness raised, the infrastructure and staffing improvements, the success in receiving financial aid and the minimal spread to other countries outside the main transmission zone. Despite this, some failures in communication and a slow early response were noted.

Genotypic and epidemiologic trends of norovirus outbreaks in the United States, 2009 to 2013.

PubMed

Vega, Everardo; Barclay, Leslie; Gregoricus, Nicole; Shirley, S Hannah; Lee, David; Vinjé, Jan

2014-01-01

Noroviruses are the leading cause of epidemic acute gastroenteritis in the United States. From September 2009 through August 2013, 3,960 norovirus outbreaks were reported to CaliciNet. Of the 2,895 outbreaks with a known transmission route, person-to-person and food-borne transmissions were reported for 2,425 (83.7%) and 465 (16.1%) of the outbreaks, respectively. A total of 2,475 outbreaks (62.5%) occurred in long-term care facilities (LTCF), 389 (9.8%) in restaurants, and 227 (5.7%) in schools. A total of 435 outbreaks (11%) were typed as genogroup I (GI) and 3,525 (89%) as GII noroviruses. GII.4 viruses caused 2,853 (72%) of all outbreaks, of which 94% typed as either GII.4 New Orleans or GII.4 Sydney. In addition, three non-GII.4 viruses, i.e., GII.12, GII.1, and GI.6, caused 528 (13%) of all outbreaks. Several non-GII.4 genotypes (GI.3, GI.6, GI.7, GII.3, GII.6, and GII.12) were significantly more associated with food-borne transmission (odds ratio, 1.9 to 7.1; P < 0.05). Patients in LTCF and people ≥65 years of age were at higher risk for GII.4 infections than those in other settings and with other genotypes (P < 0.05). Phylogeographic analysis identified three major dispersions from two geographic locations that were responsible for the GI.6 outbreaks from 2011 to 2013. In conclusion, our data demonstrate the cyclic emergence of new (non-GII.4) norovirus strains, and several genotypes are more often associated with food-borne outbreaks. These surveillance data can be used to improve viral food-borne surveillance and to help guide studies to develop and evaluate targeted prevention methods such as norovirus vaccines, antivirals, and environmental decontamination methods.

Genotypic and Epidemiologic Trends of Norovirus Outbreaks in the United States, 2009 to 2013

PubMed Central

Barclay, Leslie; Gregoricus, Nicole; Shirley, S. Hannah; Lee, David

2014-01-01

Noroviruses are the leading cause of epidemic acute gastroenteritis in the United States. From September 2009 through August 2013, 3,960 norovirus outbreaks were reported to CaliciNet. Of the 2,895 outbreaks with a known transmission route, person-to-person and food-borne transmissions were reported for 2,425 (83.7%) and 465 (16.1%) of the outbreaks, respectively. A total of 2,475 outbreaks (62.5%) occurred in long-term care facilities (LTCF), 389 (9.8%) in restaurants, and 227 (5.7%) in schools. A total of 435 outbreaks (11%) were typed as genogroup I (GI) and 3,525 (89%) as GII noroviruses. GII.4 viruses caused 2,853 (72%) of all outbreaks, of which 94% typed as either GII.4 New Orleans or GII.4 Sydney. In addition, three non-GII.4 viruses, i.e., GII.12, GII.1, and GI.6, caused 528 (13%) of all outbreaks. Several non-GII.4 genotypes (GI.3, GI.6, GI.7, GII.3, GII.6, and GII.12) were significantly more associated with food-borne transmission (odds ratio, 1.9 to 7.1; P < 0.05). Patients in LTCF and people ≥65 years of age were at higher risk for GII.4 infections than those in other settings and with other genotypes (P < 0.05). Phylogeographic analysis identified three major dispersions from two geographic locations that were responsible for the GI.6 outbreaks from 2011 to 2013. In conclusion, our data demonstrate the cyclic emergence of new (non-GII.4) norovirus strains, and several genotypes are more often associated with food-borne outbreaks. These surveillance data can be used to improve viral food-borne surveillance and to help guide studies to develop and evaluate targeted prevention methods such as norovirus vaccines, antivirals, and environmental decontamination methods. PMID:24172151

Contributing factors to disease outbreaks associated with untreated groundwater.

PubMed

Wallender, Erika K; Ailes, Elizabeth C; Yoder, Jonathan S; Roberts, Virginia A; Brunkard, Joan M

2014-01-01

Disease outbreaks associated with drinking water drawn from untreated groundwater sources represent a substantial proportion (30.3%) of the 818 drinking water outbreaks reported to CDC's Waterborne Disease and Outbreak Surveillance System (WBDOSS) during 1971 to 2008. The objectives of this study were to identify underlying contributing factors, suggest improvements for data collection during outbreaks, and inform outbreak prevention efforts. Two researchers independently reviewed all qualifying outbreak reports (1971 to 2008), assigned contributing factors and abstracted additional information (e.g., cases, etiology, and water system attributes). The 248 outbreaks resulted in at least 23,478 cases of illness, 390 hospitalizations, and 13 deaths. The majority of outbreaks had an unidentified etiology (n = 135, 54.4%). When identified, the primary etiologies were hepatitis A virus (n = 21, 8.5%), Shigella spp. (n = 20, 8.1%), and Giardia intestinalis (n = 14, 5.7%). Among the 172 (69.4%) outbreaks with contributing factor data available, the leading contamination sources included human sewage (n = 57, 33.1%), animal contamination (n = 16, 9.3%), and contamination entering via the distribution system (n = 12, 7.0%). Groundwater contamination was most often facilitated by improper design, maintenance or location of the water source or nearby waste water disposal system (i.e., septic tank; n = 116, 67.4%). Other contributing factors included rapid pathogen transport through hydrogeologic formations (e.g., karst limestone; n = 45, 26.2%) and preceding heavy rainfall or flooding (n = 36, 20.9%). This analysis underscores the importance of identifying untreated groundwater system vulnerabilities through frequent inspection and routine maintenance, as recommended by protective regulations such as Environmental Protection Agency's (EPA's) Groundwater Rule, and the need for special consideration of the local hydrogeology. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

The Ebola-effect in Guinea 2014-15: Tangled trends of malaria care in children under-five.

PubMed

Kolie, Delphin; Camara, Bienvenu S; Delamou, Alexandre; Béavogui, Abdoul H; Hermans, Veerle; Edwards, Jeffrey K; Benedetti, Guido; Muller, Claude P; Griensven, Johan van; Zachariah, Rony

2018-01-01

The 2014-15 Ebola outbreak in West Africa was disruptive for the general health services in the affected countries. This study assessed the impact of the outbreak on the reported number and management of malaria in children under-five in rural Guinea. A retrospective cross-sectional study was conducted in nineteen health centres in two rural, malaria-endemic health districts, one at the epicentre of the outbreak (Guéckédou) and one (Koubia) spared by Ebola. Routine surveillance data at health facility level were compared over similar periods of high malaria transmission in both districts before, during and after the outbreak. There were significant declines in the number of visits during the Ebola outbreak (3,700) in Guéckédou compared to before (4,616) and after it (4,195), while this trend remained more stable within the three periods for Koubia. Differences were nonetheless significant in both districts (p<0.001). In 2014, during the peak of the outbreak, the overall number of malaria cases treated exceeded the number of confirmed malaria cases in Guéckédou. There were decreases in antimalarial treatment provision in August and November 2014. In contrast, during 2015 and 2016, the proportion of malaria positive cases and those treated were closely aligned. During the peak of the Ebola outbreak, there was a significant decrease in oral antimalarial drug administration, which corresponded to an increase in injectable antimalarial treatments. Stock-outs in rapid diagnostic tests were evident and prolonged in Guéckédou during the outbreak, while more limited in Koubia. The Ebola outbreak of 2014-15 in Guinea had a significant impact on the admission and management of malaria in children under-five. This study identifies potential challenges in the delivery of care for those at highest risk for malaria mortality during an Ebola outbreak and the need to improve preparedness strategies pre-Ebola and health systems recovery post-Ebola.

Key Role of Sequencing to Trace Hepatitis A Viruses Circulating in Italy During a Large Multi-Country European Foodborne Outbreak in 2013

PubMed Central

Bruni, Roberto; Taffon, Stefania; Equestre, Michele; Chionne, Paola; Madonna, Elisabetta; Rizzo, Caterina; Tosti, Maria Elena; Alfonsi, Valeria; Ricotta, Lara; De Medici, Dario; Di Pasquale, Simona; Scavia, Gaia; Pavoni, Enrico; Losio, Marina Nadia; Romanò, Luisa; Zanetti, Alessandro Remo; Morea, Anna; Pacenti, Monia; Palù, Giorgio; Capobianchi, Maria Rosaria; Chironna, Maria; Pompa, Maria Grazia; Ciccaglione, Anna Rita

2016-01-01

Background Foodborne Hepatitis A Virus (HAV) outbreaks are being recognized as an emerging public health problem in industrialized countries. In 2013 three foodborne HAV outbreaks occurred in Europe and one in USA. During the largest of the three European outbreaks, most cases occurred in Italy (>1,200 cases as of March 31, 2014). A national Task Force was established at the beginning of the outbreak by the Ministry of Health. Mixed frozen berries were early demonstrated to be the source of infection by the identity of viral sequences in patients and in food. In the present study the molecular characterization of HAV isolates from 355 Italian cases is reported. Methods Molecular characterization was carried out by PCR/sequencing (VP1/2A region), comparison with reference strains and phylogenetic analysis. Results A unique strain was responsible for most characterized cases (235/355, 66.1%). Molecular data had a key role in tracing this outbreak, allowing 110 out of the 235 outbreak cases (46.8%) to be recognized in absence of any other link. The data also showed background circulation of further unrelated strains, both autochthonous and travel related, whose sequence comparison highlighted minor outbreaks and small clusters, most of them unrecognized on the basis of epidemiological data. Phylogenetic analysis showed most isolates from travel related cases clustering with reference strains originating from the same geographical area of travel. Conclusions In conclusion, the study documents, in a real outbreak context, the crucial role of molecular analysis in investigating an old but re-emerging pathogen. Improving the molecular knowledge of HAV strains, both autochthonous and circulating in countries from which potentially contaminated foods are imported, will become increasingly important to control outbreaks by supporting trace back activities, aiming to identify the geographical source(s) of contaminated food, as well as public health interventions. PMID:26901877

Key Role of Sequencing to Trace Hepatitis A Viruses Circulating in Italy During a Large Multi-Country European Foodborne Outbreak in 2013.

PubMed

Bruni, Roberto; Taffon, Stefania; Equestre, Michele; Chionne, Paola; Madonna, Elisabetta; Rizzo, Caterina; Tosti, Maria Elena; Alfonsi, Valeria; Ricotta, Lara; De Medici, Dario; Di Pasquale, Simona; Scavia, Gaia; Pavoni, Enrico; Losio, Marina Nadia; Romanò, Luisa; Zanetti, Alessandro Remo; Morea, Anna; Pacenti, Monia; Palù, Giorgio; Capobianchi, Maria Rosaria; Chironna, Maria; Pompa, Maria Grazia; Ciccaglione, Anna Rita

2016-01-01

Foodborne Hepatitis A Virus (HAV) outbreaks are being recognized as an emerging public health problem in industrialized countries. In 2013 three foodborne HAV outbreaks occurred in Europe and one in USA. During the largest of the three European outbreaks, most cases occurred in Italy (>1,200 cases as of March 31, 2014). A national Task Force was established at the beginning of the outbreak by the Ministry of Health. Mixed frozen berries were early demonstrated to be the source of infection by the identity of viral sequences in patients and in food. In the present study the molecular characterization of HAV isolates from 355 Italian cases is reported. Molecular characterization was carried out by PCR/sequencing (VP1/2A region), comparison with reference strains and phylogenetic analysis. A unique strain was responsible for most characterized cases (235/355, 66.1%). Molecular data had a key role in tracing this outbreak, allowing 110 out of the 235 outbreak cases (46.8%) to be recognized in absence of any other link. The data also showed background circulation of further unrelated strains, both autochthonous and travel related, whose sequence comparison highlighted minor outbreaks and small clusters, most of them unrecognized on the basis of epidemiological data. Phylogenetic analysis showed most isolates from travel related cases clustering with reference strains originating from the same geographical area of travel. In conclusion, the study documents, in a real outbreak context, the crucial role of molecular analysis in investigating an old but re-emerging pathogen. Improving the molecular knowledge of HAV strains, both autochthonous and circulating in countries from which potentially contaminated foods are imported, will become increasingly important to control outbreaks by supporting trace back activities, aiming to identify the geographical source(s) of contaminated food, as well as public health interventions.

Prevalence of avian influenza virus in wild birds before and after the HPAI H5N8 outbreak in 2014 in South Korea.

PubMed

Shin, Jeong-Hwa; Woo, Chanjin; Wang, Seung-Jun; Jeong, Jipseol; An, In-Jung; Hwang, Jong-Kyung; Jo, Seong-Deok; Yu, Seung Do; Choi, Kyunghee; Chung, Hyen-Mi; Suh, Jae-Hwa; Kim, Seol-Hee

2015-07-01

Since 2003, highly pathogenic avian influenza (HPAI) virus outbreaks have occurred five times in Korea, with four HPAI H5N1 outbreaks and one HPAI H5N8 outbreak. Migratory birds have been suggested to be the first source of HPAI in Korea. Here, we surveyed migratory wild birds for the presence of AI and compared regional AI prevalence in wild birds from September 2012 to April 2014 for birds having migratory pathways in South Korea. Finally, we investigated the prevalence of AI in migratory birds before and after HPAI H5N8 outbreaks. Overall, we captured 1617 migratory wild birds, while 18,817 feces samples and 74 dead birds were collected from major wild bird habitats. A total of 21 HPAI viruses were isolated from dead birds, and 86 low pathogenic AI (LPAI) viruses were isolated from captured birds and from feces samples. Spatiotemporal distribution analysis revealed that AI viruses were spread southward until December, but tended to shift north after January, consistent with the movement of migratory birds in South Korea. Furthermore, we found that LPAI virus prevalences within wild birds were notably higher in 2013-2014 than the previous prevalence during the northward migration season. The data from our study demonstrate the importance of the surveillance of AI in wild birds. Future studies including in-depth genetic analysis in combination with evaluation of the movement and ecology of migratory birds might help us to bridge the gaps in our knowledge and better explain, predict, and ultimately prevent future HPAI outbreaks.

Characterization of the temporal and spatial distribution and reproductive ratio of vesicular stomatitis outbreaks in Mexico in 2008.

PubMed

Arroyo, Montserrat; Perez, Andres M; Rodriguez, Luis L

2011-02-01

To characterize the temporal and spatial distribution and reproductive ratio of vesicular stomatitis (VS) outbreaks reported in Mexico in 2008. Bovine herds in Mexico in which VS outbreaks were officially reported and confirmed from January 1 through December 31, 2008. The Poisson model of the space-time scan statistic was used to identify periods and geographical locations at highest risk for VS in Mexico in 2008. The herd reproductive ratio (R(h)) of the epidemic was computed by use of the doubling-time method. 1 significant space-time cluster of VS was detected in the state of Michoacan from September 4 through December 10, 2008. The temporal extent of the VS outbreaks and the value and pattern of decrease of the R(h) were different in the endemic zone of Tabasco and Chiapas, compared with findings in the region included in the space-time cluster. The large number of VS outbreaks reported in Mexico in 2008 was associated with the spread of the disease from the endemic zone in southern Mexico to areas sporadically affected by the disease. Results suggested that implementation of a surveillance system in the endemic zone of Mexico aimed at early detection of changes in the value of R(h) and space-time clustering of the disease could help predict occurrence of future VS outbreaks originating from this endemic zone. This information will help prevent VS spread into regions of Mexico and neighboring countries that are only sporadically affected by the disease.

Selective Impact of Disease on Coral Communities: Outbreak of White Syndrome Causes Significant Total Mortality of Acropora Plate Corals

PubMed Central

Hobbs, Jean-Paul A.; Frisch, Ashley J.; Newman, Stephen J.; Wakefield, Corey B.

2015-01-01

Coral diseases represent a significant and increasing threat to coral reefs. Among the most destructive diseases is White Syndrome (WS), which is increasing in distribution and prevalence throughout the Indo-Pacific. The aim of this study was to determine taxonomic and spatial patterns in mortality rates of corals following the 2008 outbreak of WS at Christmas Island in the eastern Indian Ocean. WS mainly affected Acropora plate corals and caused total mortality of 36% of colonies across all surveyed sites and depths. Total mortality varied between sites but was generally much greater in the shallows (0–96% of colonies at 5 m depth) compared to deeper waters (0–30% of colonies at 20 m depth). Site-specific mortality rates were a reflection of the proportion of corals affected by WS at each site during the initial outbreak and were predicted by the initial cover of live Acropora plate cover. The WS outbreak had a selective impact on the coral community. Following the outbreak, live Acropora plate coral cover at 5 m depth decreased significantly from 7.0 to 0.8%, while the cover of other coral taxa remained unchanged. Observations five years after the initial outbreak revealed that total Acropora plate cover remained low and confirmed that corals that lost all their tissue due to WS did not recover. These results demonstrate that WS represents a significant and selective form of coral mortality and highlights the serious threat WS poses to coral reefs in the Indo-Pacific. PMID:26147291

Effects of human dynamics on epidemic spreading in Côte d'Ivoire

NASA Astrophysics Data System (ADS)

Li, Ruiqi; Wang, Wenxu; Di, Zengru

2017-02-01

Understanding and predicting outbreaks of contagious diseases are crucial to the development of society and public health, especially for underdeveloped countries. However, challenging problems are encountered because of complex epidemic spreading dynamics influenced by spatial structure and human dynamics (including both human mobility and human interaction intensity). We propose a systematical model to depict nationwide epidemic spreading in Côte d'Ivoire, which integrates multiple factors, such as human mobility, human interaction intensity, and demographic features. We provide insights to aid in modeling and predicting the epidemic spreading process by data-driven simulation and theoretical analysis, which is otherwise beyond the scope of local evaluation and geometrical views. We show that the requirement that the average local basic reproductive number to be greater than unity is not necessary for outbreaks of epidemics. The observed spreading phenomenon can be roughly explained as a heterogeneous diffusion-reaction process by redefining mobility distance according to the human mobility volume between nodes, which is beyond the geometrical viewpoint. However, the heterogeneity of human dynamics still poses challenges to precise prediction.

Genome sequence analyses of two isolates from the recent Escherichia coli outbreak in Germany reveal the emergence of a new pathotype: Entero-Aggregative-Haemorrhagic Escherichia coli (EAHEC).

PubMed

Brzuszkiewicz, Elzbieta; Thürmer, Andrea; Schuldes, Jörg; Leimbach, Andreas; Liesegang, Heiko; Meyer, Frauke-Dorothee; Boelter, Jürgen; Petersen, Heiko; Gottschalk, Gerhard; Daniel, Rolf

2011-12-01

The genome sequences of two Escherichia coli O104:H4 strains derived from two different patients of the 2011 German E. coli outbreak were determined. The two analyzed strains were designated E. coli GOS1 and GOS2 (German outbreak strain). Both isolates comprise one chromosome of approximately 5.31 Mbp and two putative plasmids. Comparisons of the 5,217 (GOS1) and 5,224 (GOS2) predicted protein-encoding genes with various E. coli strains, and a multilocus sequence typing analysis revealed that the isolates were most similar to the entero-aggregative E. coli (EAEC) strain 55989. In addition, one of the putative plasmids of the outbreak strain is similar to pAA-type plasmids of EAEC strains, which contain aggregative adhesion fimbrial operons. The second putative plasmid harbors genes for extended-spectrum β-lactamases. This type of plasmid is widely distributed in pathogenic E. coli strains. A significant difference of the E. coli GOS1 and GOS2 genomes to those of EAEC strains is the presence of a prophage encoding the Shiga toxin, which is characteristic for enterohemorrhagic E. coli (EHEC) strains. The unique combination of genomic features of the German outbreak strain, containing characteristics from pathotypes EAEC and EHEC, suggested that it represents a new pathotype Entero-Aggregative-Haemorrhagic E scherichia c oli (EAHEC).

Transformation of leaf litter by insect herbivory in the Subarctic: Consequences for soil biogeochemistry under global change

NASA Astrophysics Data System (ADS)

Kristensen, J. A.; Metcalfe, D. B.; Rousk, J.

2017-12-01

Climate warming may increase insect herbivore ranges and outbreak intensities in arctic ecosystems. Thorough understanding of the implications of these changes for ecosystem processes is essential to make accurate predictions of surface-atmosphere carbon (C) feedbacks. Yet, we lack a comprehensive understanding of the impacts of herbivore outbreaks on soil microbial underpinnings of C and nitrogen (N) fluxes. Here, we investigate the growth responses of heterotrophic soil decomposers and C and N mineralisation to simulated defoliator outbreaks in Subarctic birch forests. In microcosms, topsoil was incubated with leaf litter, insect frass, mineral N and combinations of the three; all was added in equal amounts of N. A higher fraction of added C and N was mineralised during outbreaks (frass addition) relative to non-outbreak years (litter addition). However, under high mineral N-availability in the soil of the kind likely under longer periods of enhanced insect herbivory (litter+mineral N), the mineralised fraction of added C decreased while the mineralised fraction of N increased substantially, which suggest a shift towards more N-mining of the organic substrates. This shift was accompanied by higher fungal dominance, and may facilitate soil C-accumulation assuming constant quality of C-inputs. Thus, long-term increases of insect herbivory, of the kind observed in some areas and projected by some models, may facilitate higher ecosystem C-sink capacity in this Subarctic ecosystem.

Timeliness of Surveillance during Outbreak of Shiga Toxin–producing Escherichia coli Infection, Germany, 2011

PubMed Central

Wadl, Maria; Altmann, Doris; Benzler, Justus; Eckmanns, Tim; Krause, Gérard; Spode, Anke; an der Heiden, Matthias

2011-01-01

In the context of a large outbreak of Shiga toxin–producing Escherichia coli O104:H4 in Germany, we quantified the timeliness of the German surveillance system for hemolytic uremic syndrome and Shiga toxin–producing E. coli notifiable diseases during 2003–2011. Although reporting occurred faster than required by law, potential for improvement exists at all levels of the information chain. PMID:22000368

Quantifying the Value of Perfect Information in Emergency Vaccination Campaigns.

PubMed

Bradbury, Naomi V; Probert, William J M; Shea, Katriona; Runge, Michael C; Fonnesbeck, Christopher J; Keeling, Matt J; Ferrari, Matthew J; Tildesley, Michael J

2017-02-01

Foot-and-mouth disease outbreaks in non-endemic countries can lead to large economic costs and livestock losses but the use of vaccination has been contentious, partly due to uncertainty about emergency FMD vaccination. Value of information methods can be applied to disease outbreak problems such as FMD in order to investigate the performance improvement from resolving uncertainties. Here we calculate the expected value of resolving uncertainty about vaccine efficacy, time delay to immunity after vaccination and daily vaccination capacity for a hypothetical FMD outbreak in the UK. If it were possible to resolve all uncertainty prior to the introduction of control, we could expect savings of £55 million in outbreak cost, 221,900 livestock culled and 4.3 days of outbreak duration. All vaccination strategies were found to be preferable to a culling only strategy. However, the optimal vaccination radius was found to be highly dependent upon vaccination capacity for all management objectives. We calculate that by resolving the uncertainty surrounding vaccination capacity we would expect to return over 85% of the above savings, regardless of management objective. It may be possible to resolve uncertainty about daily vaccination capacity before an outbreak, and this would enable decision makers to select the optimal control action via careful contingency planning.

Estimating true hospital morbidity of complications associated with mumps outbreak, England, 2004/05.

PubMed

Yung, C F; Ramsay, M

2016-08-18

Mumps outbreaks in highly vaccinated populations continue to be reported globally. Therefore, quantifying the burden of mumps morbidity accurately will be necessary to better assess the impact of mumps vaccination programmes. We aim to estimate the true morbidity resulting from mumps complications in terms of hospitalised orchitis, meningitis, oophoritis and pancreatitis in England during the outbreak in 2004/05. This outbreak in England led to a clear increase in hospitalisations coded to mumps for complications of orchitis in those born in the 1970s and 1980s and possibly for meningitis in those born in the 1980s. A simple statistical model, based on analysing time trends for diagnosed complications in hospital databases with routine laboratory surveillance data, found that the actual morbidity was much higher. There were 2.5 times (166 cases) more mumps orchitis cases in the 1970s cohort and 2.0 times (708 cases) more mumps orchitis cases in the 1980s cohort than complications coded to mumps in hospital databases. Our study demonstrated that the mumps outbreak in England 2004/05 resulted in a substantial increase in hospitalised mumps complications, and the model we used can improve the ascertainment of morbidity from a mumps outbreak. This article is copyright of The Authors, 2016.

Cryptosporidiosis outbreaks associated with recreational water use--five states, 2006.

PubMed

2007-07-27

Cryptosporidiosis is a gastrointestinal illness caused by parasitic protozoa of the genus Cryptosporidium and can produce watery diarrhea lasting 1-3 weeks; one or two cases per 100,000 population are reported annually in the United States. Fecal-oral transmission of Cryptosporidium oocysts occurs through ingestion of contaminated drinking or recreational water, consumption of contaminated food, and contact with infected persons or animals (e.g., cattle or sheep). Unlike bacterial pathogens, Cryptosporidium oocysts are resistant to chlorine disinfection and can survive for days in treated recreational water venues (e.g., public and residential swimming pools and community and commercial water parks) despite adherence to recommended residual chlorine levels (1-3 ppm). For 2006, a total of 18 cryptosporidiosis outbreaks have been reported (as of July 24, 2007) to CDC's U.S. Waterborne Disease and Outbreak Surveillance System, compared with five outbreaks reported for 2003 and seven for 2004; data for 2005 and 2006 are not yet final. This report describes five laboratory-confirmed cryptosporidiosis outbreaks in 2006 that involved public recreational water use. The popularity of recreational water venues, the number and geographic distribution of recent cryptosporidiosis outbreaks, and the resistance of Cryptosporidium to chlorination suggest that treatment strategies for recreational water facilities need to be improved.

Quantifying the Value of Perfect Information in Emergency Vaccination Campaigns

PubMed Central

Probert, William J. M.; Shea, Katriona; Fonnesbeck, Christopher J.; Ferrari, Matthew J.; Tildesley, Michael J.

2017-01-01

Foot-and-mouth disease outbreaks in non-endemic countries can lead to large economic costs and livestock losses but the use of vaccination has been contentious, partly due to uncertainty about emergency FMD vaccination. Value of information methods can be applied to disease outbreak problems such as FMD in order to investigate the performance improvement from resolving uncertainties. Here we calculate the expected value of resolving uncertainty about vaccine efficacy, time delay to immunity after vaccination and daily vaccination capacity for a hypothetical FMD outbreak in the UK. If it were possible to resolve all uncertainty prior to the introduction of control, we could expect savings of £55 million in outbreak cost, 221,900 livestock culled and 4.3 days of outbreak duration. All vaccination strategies were found to be preferable to a culling only strategy. However, the optimal vaccination radius was found to be highly dependent upon vaccination capacity for all management objectives. We calculate that by resolving the uncertainty surrounding vaccination capacity we would expect to return over 85% of the above savings, regardless of management objective. It may be possible to resolve uncertainty about daily vaccination capacity before an outbreak, and this would enable decision makers to select the optimal control action via careful contingency planning. PMID:28207777

Outbreak or illusion: consequences of 'improved' diagnostics for gonorrhoea.

PubMed

Bennett, Amy; Jeffery, Katie; O'Neill, Eunan; Sherrard, Jackie

2017-06-01

The sexual health service in Oxford introduced gonorrhoea nucleic amplification acid testing using the BD Viper XTR™ System. For practical reasons, a confirmatory nucleic amplification acid testing using a different platform was not used initially. Following the introduction of nucleic amplification acid testing, the rates of gonorrhoea increased threefold. Concerns were raised that this increase represented an outbreak. A retrospective review of cases over six months suggested that there may have been a number of false-positive results. A prospective study was then undertaken over six months, where all gonorrhoea positive samples were sent for confirmatory testing. This evaluation of all gonorrhoea cases in an English county found that the overall presumptive false-positive rates for gonorrhoea nucleic amplification acid testing using BD Viper XTR™ in our population are significant at 27% of female samples, 13.2% of heterosexual male samples, 3.5% of anogenital multiple site men who have sex with men samples and 62.8% of pharyngeal only men who have sex with men samples. The data demonstrate the need for confirmatory testing using a second nucleic acid target, as per BASHH/Public Health England guidelines, especially in low-prevalence settings and extragenital sites, due to cross-reactivity with commensal Neisseria species and low positive predictive values.

Foodborne Infectious Diseases Mediated by Inappropriate Infection Control in Food Service Businesses and Relevant Countermeasures in Korea.

PubMed

Park, Jong Myong; You, Young-Hyun; Cho, Hyun-Min; Hong, Ji Won; Ghim, Sa-Youl

2017-06-01

The objective of this review is to propose an appropriate course of action for improving the guidelines followed by food handlers for control of infection. For this purpose, previous epidemiological reports related to acute gastroenteritis in food service businesses mediated by food handlers were intensively analyzed. Relevant studies were identified in international databases. We selected eligible papers reporting foodborne infectious disease outbreaks. Among primary literature collection, the abstract of each article was investigated to find cases that absolutely identified a causative factor to be food handlers' inappropriate infection control and the taxon of causative microbial agents by epidemiological methodologies. Information about the sites (type of food business) where the outbreaks occurred was investigated. A wide variety of causative microbial agents has been investigated, using several epidemiological methods. These agents have shown diverse propagation pathways based on their own molecular pathogenesis, physiology, taxonomy, and etiology. Depending on etiology, transmission, propagation, and microbiological traits, we can predict the transmission characteristics of pathogens in food preparation areas. The infected food workers have a somewhat different ecological place in infection epidemiology as compared to the general population. However, the current Korean Food Safety Act cannot propose detailed guidelines. Therefore, different methodologies have to be made available to prevent further infections.

Foodborne Infectious Diseases Mediated by Inappropriate Infection Control in Food Service Businesses and Relevant Countermeasures in Korea

PubMed Central

Park, Jong Myong; You, Young-Hyun; Cho, Hyun-Min; Hong, Ji Won; Ghim, Sa-Youl

2017-01-01

Objectives The objective of this review is to propose an appropriate course of action for improving the guidelines followed by food handlers for control of infection. For this purpose, previous epidemiological reports related to acute gastroenteritis in food service businesses mediated by food handlers were intensively analyzed. Methods Relevant studies were identified in international databases. We selected eligible papers reporting foodborne infectious disease outbreaks. Among primary literature collection, the abstract of each article was investigated to find cases that absolutely identified a causative factor to be food handlers’ inappropriate infection control and the taxon of causative microbial agents by epidemiological methodologies. Information about the sites (type of food business) where the outbreaks occurred was investigated. Results A wide variety of causative microbial agents has been investigated, using several epidemiological methods. These agents have shown diverse propagation pathways based on their own molecular pathogenesis, physiology, taxonomy, and etiology. Conclusion Depending on etiology, transmission, propagation, and microbiological traits, we can predict the transmission characteristics of pathogens in food preparation areas. The infected food workers have a somewhat different ecological place in infection epidemiology as compared to the general population. However, the current Korean Food Safety Act cannot propose detailed guidelines. Therefore, different methodologies have to be made available to prevent further infections. PMID:28781938

Applying the zero-inflated Poisson model with random effects to detect abnormal rises in school absenteeism indicating infectious diseases outbreak.

PubMed

Song, X X; Zhao, Q; Tao, T; Zhou, C M; Diwan, V K; Xu, B

2018-05-30

Records of absenteeism from primary schools are valuable data for infectious diseases surveillance. However, the analysis of the absenteeism is complicated by the data features of clustering at zero, non-independence and overdispersion. This study aimed to generate an appropriate model to handle the absenteeism data collected in a European Commission granted project for infectious disease surveillance in rural China and to evaluate the validity and timeliness of the resulting model for early warnings of infectious disease outbreak. Four steps were taken: (1) building a 'well-fitting' model by the zero-inflated Poisson model with random effects (ZIP-RE) using the absenteeism data from the first implementation year; (2) applying the resulting model to predict the 'expected' number of absenteeism events in the second implementation year; (3) computing the differences between the observations and the expected values (O-E values) to generate an alternative series of data; (4) evaluating the early warning validity and timeliness of the observational data and model-based O-E values via the EARS-3C algorithms with regard to the detection of real cluster events. The results indicate that ZIP-RE and its corresponding O-E values could improve the detection of aberrations, reduce the false-positive signals and are applicable to the zero-inflated data.

[The 2011 HUS epidemic in Germany. Challenges for disease control: what should be improved?].

PubMed

Krause, G; Frank, C; Gilsdorf, A; Mielke, M; Schaade, L; Stark, K; Burger, R

2013-01-01

From May to July 2011 [corrected] the world's largest outbreak of hemolytic uremic syndrome (HUS) occurred in northern Germany with dramatic consequences for the population, the health care system and the food industry. In the following we examine the detection of the outbreak, epidemic management and related public communication aspects based on scientific publications, media reports as well as own and new data analyses. The subsequent 17 recommendations concern issues such as participation in and implementation of existing and new surveillance systems particularly with respect to physicians, broad application of finely tuned microbiological typing, improved personnel capacity and crisis management structures within the public health service and evidence-based communication by administrations and scientific associations. Outbreaks of similar dimensions can inevitably occur again and result in costs which will far exceed investments needed for early detection and control. This societal balance should be taken into account in spite of limited resources in the public health sector.

How Does Seasonal Flu Differ From Pandemic Flu?

MedlinePlus

... Home Current Issue Past Issues How Does Seasonal Flu Differ From Pandemic Flu? Past Issues / Fall 2006 Table of Contents For ... of this page please turn Javascript on. Seasonal Flu Pandemic Flu Outbreaks follow predictable seasonal patterns; occurs ...

Enterovirus D68

MedlinePlus

... EV-D68 outbreak this year or in future years? Enteroviruses are ever-present in the community. We can’t predict whether EV-D68 will be a common type of enterovirus detected this year or in other future seasons. A mix of ...

The effect of model resolution in predicting meteorological parameters used in fire danger rating.

Treesearch

Jeanne L. Hoadley; Ken Westrick; Sue A. Ferguson; Scott L. Goodrick; Larry Bradshaw; Paul Werth

2004-01-01

Previous studies of model performance at varying resolutions have focused on winter storms or isolated convective events. Little attention has been given to the static high pressure situations that may lead to severe wildfire outbreaks. This study focuses on such an event so as to evaluate the value of increased model resolution for prediction of fire danger. The...

The effect of model resolution in predicting meteorological parameters used in fire danger rating

Treesearch

Jeanne L. Hoadley; Ken Westrick; Sue a. Ferguson; Scott L. Goodrick; Larry Bradshaw; Paul Wreth

2004-01-01

Previous studies of model perfonnance at varying resolutions have focused on winter stonns or isolated convective events. Little attention has been given to the static high pressure situations that may lead to severe wildfire outbreaks. This study focuses on such an event so as to evaluate the value of increased model resolution for prediction of fire danger. The...

Heavy rainfall and waterborne disease outbreaks: the Walkerton example.

PubMed

Auld, Heather; MacIver, D; Klaassen, J

Recent research indicates that excessive rainfall has been a significant contributor to historical waterborne disease outbreaks. The Meteorological Service of Canada, Environment Canada, provided an analysis and testimony to the Walkerton Inquiry on the excessive rainfall events, including an assessment of the historical significance and expected return periods of the rainfall amounts. While the onset of the majority of the Walkerton, Ontario, Escherichia coli O157:H7 and Campylobacter outbreak occurred several days after a heavy rainfall on May 12, the accumulated 5-d rainfall amounts from 8-12 May were particularly significant. These 5-d accumulations could, on average, only be expected once every 60 yr or more in Walkerton and once every 100 yr or so in the heaviest rainfall area to the south of Walkerton. The significant link between excess rainfall and waterborne disease outbreaks, in conjunction with other multiple risk factors, indicates that meteorological and climatological conditions need to be considered by water managers, public health officials, and private citizens as a significant risk factor for water contamination. A system to identify and project the impacts of such challenging or extreme weather conditions on water supply systems could be developed using a combination of weather/climate monitoring information and weather prediction or quantitative precipitation forecast information. The use of weather monitoring and forecast information or a "wellhead alert system" could alert water system and water supply managers on the potential response of their systems to challenging weather conditions and additional requirements to protect health. Similar approaches have recently been used by beach managers in parts of the United States to predict day-to-day water quality for beach advisories.

Outbreak of Occupational Brucellosis at a Pharmaceutical Factory in Southeast China.

PubMed

Zhan, B D; Wang, S Q; Lai, S M; Lu, Y; Shi, X G; Cao, G P; Hu, X L; Zheng, C J; Yu, Z Y; Zhang, J M; Fang, C F; Gong, Z Y

2017-09-01

Brucellosis is an occupational disease affecting workers in butcher shops, the milking and dairy product industry, causing more than 500 000 new cases around the world. As a national statutory B infectious disease in China, morbidity of brucellosis is rapidly increasing in recent years. We report an occupational outbreak of brucellosis infection in a pharmaceutical factory. Exposure was a result of manual operation in the process line, close contact with sheep placentas, insufficient disinfection and repeated using of protective suits and infected by aerosol dissemination. Improved preventive methods, appropriate public health measures and spread of health education would be helpful to prevent the occupational outbreak of brucellosis in future. © 2016 Blackwell Verlag GmbH.

Estimating effects of improved drinking water and sanitation on cholera.

PubMed

Leidner, Andrew J; Adusumilli, Naveen C

2013-12-01

Demand for adequate provision of drinking-water and sanitation facilities to promote public health and economic growth is increasing in the rapidly urbanizing countries of the developing world. With a panel of data on Asia and Africa from 1990 to 2008, associations are estimated between the occurrence of cholera outbreaks, the case rates in given outbreaks, the mortality rates associated with cholera and two disease control mechanisms, drinking-water and sanitation services. A statistically significant and negative effect is found between drinking-water services and both cholera case rates as well as cholera-related mortality rates. A relatively weak statistical relationship is found between the occurrence of cholera outbreaks and sanitation services.

Improving risk models for avian influenza: the role of intensive poultry farming and flooded land during the 2004 Thailand epidemic.

PubMed

Van Boeckel, Thomas P; Thanapongtharm, Weerapong; Robinson, Timothy; Biradar, Chandrashekhar M; Xiao, Xiangming; Gilbert, Marius

2012-01-01

Since 1996 when Highly Pathogenic Avian Influenza type H5N1 first emerged in southern China, numerous studies sought risk factors and produced risk maps based on environmental and anthropogenic predictors. However little attention has been paid to the link between the level of intensification of poultry production and the risk of outbreak. This study revised H5N1 risk mapping in Central and Western Thailand during the second wave of the 2004 epidemic. Production structure was quantified using a disaggregation methodology based on the number of poultry per holding. Population densities of extensively- and intensively-raised ducks and chickens were derived both at the sub-district and at the village levels. LandSat images were used to derive another previously neglected potential predictor of HPAI H5N1 risk: the proportion of water in the landscape resulting from floods. We used Monte Carlo simulation of Boosted Regression Trees models of predictor variables to characterize the risk of HPAI H5N1. Maps of mean risk and uncertainty were derived both at the sub-district and the village levels. The overall accuracy of Boosted Regression Trees models was comparable to that of logistic regression approaches. The proportion of area flooded made the highest contribution to predicting the risk of outbreak, followed by the densities of intensively-raised ducks, extensively-raised ducks and human population. Our results showed that as little as 15% of flooded land in villages is sufficient to reach the maximum level of risk associated with this variable. The spatial pattern of predicted risk is similar to previous work: areas at risk are mainly located along the flood plain of the Chao Phraya river and to the south-east of Bangkok. Using high-resolution village-level poultry census data, rather than sub-district data, the spatial accuracy of predictions was enhanced to highlight local variations in risk. Such maps provide useful information to guide intervention.

Improving Risk Models for Avian Influenza: The Role of Intensive Poultry Farming and Flooded Land during the 2004 Thailand Epidemic

PubMed Central

Van Boeckel, Thomas P.; Thanapongtharm, Weerapong; Robinson, Timothy; Biradar, Chandrashekhar M.; Xiao, Xiangming; Gilbert, Marius

2012-01-01

Since 1996 when Highly Pathogenic Avian Influenza type H5N1 first emerged in southern China, numerous studies sought risk factors and produced risk maps based on environmental and anthropogenic predictors. However little attention has been paid to the link between the level of intensification of poultry production and the risk of outbreak. This study revised H5N1 risk mapping in Central and Western Thailand during the second wave of the 2004 epidemic. Production structure was quantified using a disaggregation methodology based on the number of poultry per holding. Population densities of extensively- and intensively-raised ducks and chickens were derived both at the sub-district and at the village levels. LandSat images were used to derive another previously neglected potential predictor of HPAI H5N1 risk: the proportion of water in the landscape resulting from floods. We used Monte Carlo simulation of Boosted Regression Trees models of predictor variables to characterize the risk of HPAI H5N1. Maps of mean risk and uncertainty were derived both at the sub-district and the village levels. The overall accuracy of Boosted Regression Trees models was comparable to that of logistic regression approaches. The proportion of area flooded made the highest contribution to predicting the risk of outbreak, followed by the densities of intensively-raised ducks, extensively-raised ducks and human population. Our results showed that as little as 15% of flooded land in villages is sufficient to reach the maximum level of risk associated with this variable. The spatial pattern of predicted risk is similar to previous work: areas at risk are mainly located along the flood plain of the Chao Phraya river and to the south-east of Bangkok. Using high-resolution village-level poultry census data, rather than sub-district data, the spatial accuracy of predictions was enhanced to highlight local variations in risk. Such maps provide useful information to guide intervention. PMID:23185352

Outbreaks of health care-associated influenza-like illness in France: Impact of electronic notification.

PubMed

Munier-Marion, Elodie; Bénet, Thomas; Dananché, Cédric; Soing-Altach, Sophan; Maugat, Sylvie; Vaux, Sophie; Vanhems, Philippe

2017-11-01

Mandatory notification of health care-associated (HA) infections, including influenza-like illness (ILI) outbreaks, has been implemented in France since 2001. In 2012, the system moved to online electronic notification of HA infections (e-SIN). The objectives of this study are to describe ILI outbreak notifications to Santé publique France (SPF), the French national public health agency, and to evaluate the impact of notification dematerialization. All notifications of HA ILI outbreaks between July 2001 and June 2015 were included. Notifications before and after e-SIN implementation were compared regarding notification delay and information exhaustiveness. Overall, 506 HA ILI outbreaks were reported, accounting for 7,861 patients and health care professionals. Median delay between occurrence of the first case and notification was, respectively, 32 and 13 days before and after e-SIN utilization (P < .001). Information exhaustiveness was improved by electronic notification regarding HA status (8.5% of missing data before and 2.3% after e-SIN, P = .003), hypotheses of cause (25.4% of missing data before vs 8.0% after e-SIN, P < .001), and level of event control (23.7% of missing data before vs 7.5% after e-SIN, P < .001). HA influenza notifications, including HA ILI or influenza, to health authorities are essential to guide decisional instances and health care practices. Electronic notifications have improved the timeliness and quality of information transmitted. Copyright © 2017 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Using social media for actionable disease surveillance and outbreak management. A systematic literature review

DOE Office of Scientific and Technical Information (OSTI.GOV)

Charles-Smith, Lauren E.; Reynolds, Tera L.; Cameron, Mark A.

Here, research studies show that social media may be valuable tools in the disease surveillance toolkit used for improving public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. A social media work group, consisting of surveillance practitioners, academic researchers, and other subject matter experts convened by the International Society for Disease Surveillance, conducted a systematic primary literature review using the PRISMA framework to identify research, published through February 2013, answering either of the following questions: 1) Can social media be integrated into disease surveillance practice and outbreak management to support and improvemore » public health? 2) Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes? Examples of social media included are Facebook, MySpace, microblogs (e.g., Twitter), blogs, and discussion forums. For Question 1, 33 manuscripts were identified, starting in 2009 with topics on Influenza-like Illnesses (n=15), Infectious Diseases (n = 6), Non-infectious Diseases (n=4), Medication and Vaccines (n=3), and Other (n=5). For Question 2, 32 manuscripts were identified, the first in 2000 with topics on Health Risk Behaviors (n=10), Infectious Diseases (n = 3), Non-infectious Diseases (n=9), and Other (n=10). The literature on the use of social media to support public health practice has identified many gaps and biases in current knowledge. Despite the potential for success identified in exploratory studies, there are limited studies on interventions and little use of social media in practice. However, information gleaned from the articles demonstrates the effectiveness of social media in supporting and improving public health and in identifying target populations for intervention. A primary recommendation resulting from the review is to identify opportunities that enable public health professionals to integrate social media analytics into disease surveillance and outbreak management practice.« less

Using social media for actionable disease surveillance and outbreak management. A systematic literature review

DOE PAGES

Charles-Smith, Lauren E.; Reynolds, Tera L.; Cameron, Mark A.; ...

2015-10-05

Here, research studies show that social media may be valuable tools in the disease surveillance toolkit used for improving public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. A social media work group, consisting of surveillance practitioners, academic researchers, and other subject matter experts convened by the International Society for Disease Surveillance, conducted a systematic primary literature review using the PRISMA framework to identify research, published through February 2013, answering either of the following questions: 1) Can social media be integrated into disease surveillance practice and outbreak management to support and improvemore » public health? 2) Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes? Examples of social media included are Facebook, MySpace, microblogs (e.g., Twitter), blogs, and discussion forums. For Question 1, 33 manuscripts were identified, starting in 2009 with topics on Influenza-like Illnesses (n=15), Infectious Diseases (n = 6), Non-infectious Diseases (n=4), Medication and Vaccines (n=3), and Other (n=5). For Question 2, 32 manuscripts were identified, the first in 2000 with topics on Health Risk Behaviors (n=10), Infectious Diseases (n = 3), Non-infectious Diseases (n=9), and Other (n=10). The literature on the use of social media to support public health practice has identified many gaps and biases in current knowledge. Despite the potential for success identified in exploratory studies, there are limited studies on interventions and little use of social media in practice. However, information gleaned from the articles demonstrates the effectiveness of social media in supporting and improving public health and in identifying target populations for intervention. A primary recommendation resulting from the review is to identify opportunities that enable public health professionals to integrate social media analytics into disease surveillance and outbreak management practice.« less

Knowledge, attitudes, and practices of school personnel regarding influenza, vaccinations, and school outbreaks.

PubMed

Ha, Chrysanthy; Rios, Lenoa M; Pannaraj, Pia S

2013-08-01

School personnel are important for communicating with parents about school vaccination programs and recognizing influenza outbreaks. This study examined knowledge, attitudes, and practices of school personnel regarding seasonal and 2009 H1N1 influenza, vaccinations, and school outbreak investigations. Data were analyzed from survey interviews of 58 elementary and middle school personnel in 2010. Principals, assistant principals, and nurses have higher knowledge than front office clerks regarding seasonal (odds ratio [OR]: 2.50, 95% confidence interval [CI]: 1.15-5.42) and 2009 H1N1 influenza (OR: 2.04, 95% CI: 1.19-3.71). During 2009-2010, 63.8 and 19.0% of school personnel received seasonal and 2009 H1N1 influenza vaccine, respectively. Personnel were more likely to be vaccinated against seasonal influenza if they believed the vaccine was safe (OR: 2.26, 95% CI: 1.21-4.19). Of those unvaccinated against 2009 H1N1, 48.9% also cited safety concerns. While every principal, assistant principal, and nurse received both infectious diseases and outbreak trainings, only 42.5 and 27.5% of clerks received these trainings, respectively (p < .001), and 30% of clerks believed outbreak recognition was not their responsibility. The level of knowledge regarding influenza illness, vaccination, and outbreaks among subjects was low overall. Education of school personnel may improve school vaccination programs and control of influenza outbreaks. © 2013, American School Health Association.

Detecting Ebola with limited laboratory access in the Democratic Republic of Congo: evaluation of a clinical passive surveillance reporting system.

PubMed

Ashbaugh, Hayley R; Kuang, Brandon; Gadoth, Adva; Alfonso, Vivian H; Mukadi, Patrick; Doshi, Reena H; Hoff, Nicole A; Sinai, Cyrus; Mossoko, Mathias; Kebela, Benoit Ilunga; Muyembe, Jean-Jacques; Wemakoy, Emile Okitolonda; Rimoin, Anne W

2017-09-01

Ebola virus disease (EVD) can be clinically severe and highly fatal, making surveillance efforts for early disease detection of paramount importance. In areas with limited access to laboratory testing, the Integrated Disease Surveillance and Response (IDSR) strategy in the Democratic Republic of Congo (DRC) may be a vital tool in improving outbreak response. Using DRC IDSR data from the nation's four EVD outbreak periods from 2007-2014, we assessed trends of Viral Hemorrhagic Fever (VHF) and EVD differential diagnoses reportable through IDSR. With official case counts from active surveillance of EVD outbreaks, we assessed accuracy of reporting through the IDSR passive surveillance system. Although the active and passive surveillance represent distinct sets of data, the two were correlated, suggesting that passive surveillance based only on clinical evaluation may be a useful predictor of true cases prior to laboratory confirmation. There were 438 suspect VHF cases reported through the IDSR system and 416 EVD cases officially recorded across the outbreaks examined. Although collected prior to official active surveillance cases, case reporting through the IDSR during the 2007, 2008 and 2012 outbreaks coincided with official EVD epidemic curves. Additionally, all outbreak areas experienced increases in suspected cases for both malaria and typhoid fever during EVD outbreaks, underscoring the importance of training health care workers in recognising EVD differential diagnoses and the potential for co-morbidities. © 2017 John Wiley & Sons Ltd.

Trends of major disease outbreaks in the African region, 2003-2007.

PubMed

Kebede, Senait; Duales, Sambe; Yokouide, Allarangar; Alemu, Wondimagegnehu

2010-03-01

Communicable disease outbreaks cause millions of deaths throughout Sub-Saharan Africa each year. Most of the diseases causing epidemics in the region have been nearly eradicated or brought under control in other parts of the world. In recent years, considerable effort has been directed toward public health initiatives and strategies with a potential for significant impact in the fight against infectious diseases. In 1998, the World Health Organization African Regional Office (WHO/AFRO) launched the Integrated Disease Surveillance and Response (IDSR) strategy aimed at mitigating the impact of communicable diseases, including epidemic-prone diseases, through improving surveillance, laboratory confirmation and appropriate and timely public health interventions. Over the past decade, WHO and its partners have been providing technical and financial resources to African countries to strengthen epidemic preparedness and response (EPR) activities. This review examined the major epidemics reported to WHO/AFRO from 2003 to 2007. we conduct a review of documents and reports obtained from WHO/AFRO, WHO inter-country team, and partners and held meeting and discussions with key stakeholders to elicit the experiences of local, regional and international efforts against these epidemics to evaluate the lessons learned and to stimulate discussion on the future course for enhancing EPR. The most commonly reported epidemic outbreaks in Africa include: cholera, dysentery, malaria and hemorrhagic fevers (e.g. Ebola, Rift Valley fever, Crimean-Congo fever and yellow fever). The cyclic meningococcal meningitis outbreak that affects countries along the "meningitis belt" (spanning Sub-Saharan Africa from Senegal and The Gambia to Kenya and Ethiopia) accounts for other major epidemics in the region. The reporting of disease outbreaks to WHO/AFRO has improved since the launch of the IDSR strategy in 1998. Although the epidemic trends for cholera showed a decline in case fatality rate (CFR) suggesting improvement in detection and quality of response by the health sector, the number of countries affected has increased. Major epidemic diseases continue to occur in most countries in the region. Among the major challenges to overcome are: poor coordination of EPR, weak public health infrastructure, lack of trained workers and inconsistent supply of diagnostic, treatment and prevention commodities. To successfully reduce the levels of morbidity and mortality resulting from epidemic outbreaks, urgent and long-term investments are needed to strengthen capacities for early detection and timely and effective response. Effective advocacy, collaboration and resource mobilization efforts involving local health officials, governments and the international community are critically needed to reduce the heavy burden of disease outbreaks on African populations.

Community Attitudes toward Mass Drug Administration for Control and Elimination of Neglected Tropical Diseases after the 2014 Outbreak of Ebola Virus Disease in Lofa County, Liberia

PubMed Central

Bogus, Joshua; Gankpala, Lincoln; Fischer, Kerstin; Krentel, Alison; Weil, Gary J.; Fischer, Peter U.; Kollie, Karsor; Bolay, Fatorma K.

2016-01-01

The recent outbreak of Ebola virus disease (EVD) interrupted mass drug administration (MDA) programs to control and eliminate neglected tropical diseases in Liberia. MDA programs treat entire communities with medication regardless of infection status to interrupt transmission and eliminate lymphatic filariasis and onchocerciasis. Following reports of hostilities toward health workers and fear that they might be spreading EVD, it was important to determine whether attitudes toward MDA might have changed after the outbreak. We surveyed 140 community leaders from 32 villages in Lofa County, Liberia, that had previously participated in MDA and are located in an area that was an early epicenter of the EVD outbreak. Survey respondents reported a high degree of community trust in the MDA program, and 97% thought their communities were ready to resume MDA. However, respondents predicted that fewer people would comply with MDA after the EVD epidemic than before. The survey also uncovered fears in the community that EVD and MDA might be linked. Respondents suggested that MDA programs emphasize to people that the medications are identical to those previously distributed and that MDA programs have nothing to do with EVD. PMID:26666700