Penalized unsupervised learning with outliers

PubMed Central

Witten, Daniela M.

2013-01-01

We consider the problem of performing unsupervised learning in the presence of outliers – that is, observations that do not come from the same distribution as the rest of the data. It is known that in this setting, standard approaches for unsupervised learning can yield unsatisfactory results. For instance, in the presence of severe outliers, K-means clustering will often assign each outlier to its own cluster, or alternatively may yield distorted clusters in order to accommodate the outliers. In this paper, we take a new approach to extending existing unsupervised learning techniques to accommodate outliers. Our approach is an extension of a recent proposal for outlier detection in the regression setting. We allow each observation to take on an “error” term, and we penalize the errors using a group lasso penalty in order to encourage most of the observations’ errors to exactly equal zero. We show that this approach can be used in order to develop extensions of K-means clustering and principal components analysis that result in accurate outlier detection, as well as improved performance in the presence of outliers. These methods are illustrated in a simulation study and on two gene expression data sets, and connections with M-estimation are explored. PMID:23875057

Remote photoplethysmography system for unsupervised monitoring regional anesthesia effectiveness

NASA Astrophysics Data System (ADS)

Rubins, U.; Miscuks, A.; Marcinkevics, Z.; Lange, M.

2017-12-01

Determining the level of regional anesthesia (RA) is vitally important to both an anesthesiologist and surgeon, also knowing the RA level can protect the patient and reduce the time of surgery. Normally to detect the level of RA, usually a simple subjective (sensitivity test) and complicated quantitative methods (thermography, neuromyography, etc.) are used, but there is not yet a standardized method for objective RA detection and evaluation. In this study, the advanced remote photoplethysmography imaging (rPPG) system for unsupervised monitoring of human palm RA is demonstrated. The rPPG system comprises compact video camera with green optical filter, surgical lamp as a light source and a computer with custom-developed software. The algorithm implemented in Matlab software recognizes the palm and two dermatomes (Medial and Ulnar innervation), calculates the perfusion map and perfusion changes in real-time to detect effect of RA. Seven patients (aged 18-80 years) undergoing hand surgery received peripheral nerve brachial plexus blocks during the measurements. Clinical experiments showed that our rPPG system is able to perform unsupervised monitoring of RA.

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.

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.

Supervised segmentation of microelectrode recording artifacts using power spectral density.

PubMed

Bakstein, Eduard; Schneider, Jakub; Sieger, Tomas; Novak, Daniel; Wild, Jiri; Jech, Robert

2015-08-01

Appropriate detection of clean signal segments in extracellular microelectrode recordings (MER) is vital for maintaining high signal-to-noise ratio in MER studies. Existing alternatives to manual signal inspection are based on unsupervised change-point detection. We present a method of supervised MER artifact classification, based on power spectral density (PSD) and evaluate its performance on a database of 95 labelled MER signals. The proposed method yielded test-set accuracy of 90%, which was close to the accuracy of annotation (94%). The unsupervised methods achieved accuracy of about 77% on both training and testing data.

Exploring supervised and unsupervised methods to detect topics in biomedical text

PubMed Central

Lee, Minsuk; Wang, Weiqing; Yu, Hong

2006-01-01

Background Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature. Results We have explored the methods of Topic Spotting, a task of text categorization that applies the supervised machine-learning technique naïve Bayes to assign automatically a document into one or more predefined topics; and Topic Clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. We have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the Online Mendelian Inheritance in Man (OMIM) database. We have explored bag of words as the features. Additionally, we have explored semantic features; namely, the Medical Subject Headings (MeSH) that are assigned to the MEDLINE records, and the Unified Medical Language System (UMLS) semantic types that correspond to the MeSH terms, in addition to bag of words, to facilitate the tasks of topic detection. Our results indicate that incorporating the MeSH terms and the UMLS semantic types as additional features enhances the performance of topic detection and the naïve Bayes has the highest accuracy, 66.4%, for predicting the topic of an OMIM article as one of the total twenty-five topics. Conclusion Our results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings. PMID:16539745

VHR satellite multitemporal data to extract cultural landscape changes in the roman site of Grumentum

NASA Astrophysics Data System (ADS)

masini, nicola; Lasaponara, Rosa

2013-04-01

The papers deals with the use of VHR satellite multitemporal data set to extract cultural landscape changes in the roman site of Grumentum Grumentum is an ancient town, 50 km south of Potenza, located near the roman road of Via Herculea which connected the Venusia, in the north est of Basilicata, with Heraclea in the Ionian coast. The first settlement date back to the 6th century BC. It was resettled by the Romans in the 3rd century BC. Its urban fabric which evidences a long history from the Republican age to late Antiquity (III BC-V AD) is composed of the typical urban pattern of cardi and decumani. Its excavated ruins include a large amphitheatre, a theatre, the thermae, the Forum and some temples. There are many techniques nowadays available to capture and record differences in two or more images. In this paper we focus and apply the two main approaches which can be distinguished into : (i) unsupervised and (ii) supervised change detection methods. Unsupervised change detection methods are generally based on the transformation of the two multispectral images in to a single band or multiband image which are further analyzed to identify changes Unsupervised change detection techniques are generally based on three basic steps (i) the preprocessing step, (ii) a pixel-by-pixel comparison is performed, (iii). Identification of changes according to the magnitude an direction (positive /negative). Unsupervised change detection are generally based on the transformation of the two multispectral images into a single band or multiband image which are further analyzed to identify changes. Than the separation between changed and unchanged classes is obtained from the magnitude of the resulting spectral change vectors by means of empirical or theoretical well founded approaches Supervised change detection methods are generally based on supervised classification methods, which require the availability of a suitable training set for the learning process of the classifiers. Unsupervised change detection techniques are generally based on three basic steps (i) the preprocessing step, (ii) supervised classification is performed on the single dates or on the map obtained as the difference of two dates, (iii). Identification of changes according to the magnitude an direction (positive /negative). Supervised change detection are generally based on supervised classification methods, which require the availability of a suitable training set for the learning process of the classifiers, therefore these algorithms require a preliminary knowledge necessary: (i) to generate representative parameters for each class of interest; and (ii) to carry out the training stage Advantages and disadvantages of the supervised and unsupervised approaches are discuss. Finally results from the the satellite multitemporal dataset was also integrated with aerial photos from historical archive in order to expand the time window of the investigation and capture landscape changes occurred from the Agrarian Reform, in the 50s, up today.

Infrared vehicle recognition using unsupervised feature learning based on K-feature

NASA Astrophysics Data System (ADS)

Lin, Jin; Tan, Yihua; Xia, Haijiao; Tian, Jinwen

2018-02-01

Subject to the complex battlefield environment, it is difficult to establish a complete knowledge base in practical application of vehicle recognition algorithms. The infrared vehicle recognition is always difficult and challenging, which plays an important role in remote sensing. In this paper we propose a new unsupervised feature learning method based on K-feature to recognize vehicle in infrared images. First, we use the target detection algorithm which is based on the saliency to detect the initial image. Then, the unsupervised feature learning based on K-feature, which is generated by Kmeans clustering algorithm that extracted features by learning a visual dictionary from a large number of samples without label, is calculated to suppress the false alarm and improve the accuracy. Finally, the vehicle target recognition image is finished by some post-processing. Large numbers of experiments demonstrate that the proposed method has satisfy recognition effectiveness and robustness for vehicle recognition in infrared images under complex backgrounds, and it also improve the reliability of it.

Shadow detection and removal in RGB VHR images for land use unsupervised classification

NASA Astrophysics Data System (ADS)

Movia, A.; Beinat, A.; Crosilla, F.

2016-09-01

Nowadays, high resolution aerial images are widely available thanks to the diffusion of advanced technologies such as UAVs (Unmanned Aerial Vehicles) and new satellite missions. Although these developments offer new opportunities for accurate land use analysis and change detection, cloud and terrain shadows actually limit benefits and possibilities of modern sensors. Focusing on the problem of shadow detection and removal in VHR color images, the paper proposes new solutions and analyses how they can enhance common unsupervised classification procedures for identifying land use classes related to the CO2 absorption. To this aim, an improved fully automatic procedure has been developed for detecting image shadows using exclusively RGB color information, and avoiding user interaction. Results show a significant accuracy enhancement with respect to similar methods using RGB based indexes. Furthermore, novel solutions derived from Procrustes analysis have been applied to remove shadows and restore brightness in the images. In particular, two methods implementing the so called "anisotropic Procrustes" and the "not-centered oblique Procrustes" algorithms have been developed and compared with the linear correlation correction method based on the Cholesky decomposition. To assess how shadow removal can enhance unsupervised classifications, results obtained with classical methods such as k-means, maximum likelihood, and self-organizing maps, have been compared to each other and with a supervised clustering procedure.

An Unsupervised kNN Method to Systematically Detect Changes in Protein Localization in High-Throughput Microscopy Images.

PubMed

Lu, Alex Xijie; Moses, Alan M

2016-01-01

Despite the importance of characterizing genes that exhibit subcellular localization changes between conditions in proteome-wide imaging experiments, many recent studies still rely upon manual evaluation to assess the results of high-throughput imaging experiments. We describe and demonstrate an unsupervised k-nearest neighbours method for the detection of localization changes. Compared to previous classification-based supervised change detection methods, our method is much simpler and faster, and operates directly on the feature space to overcome limitations in needing to manually curate training sets that may not generalize well between screens. In addition, the output of our method is flexible in its utility, generating both a quantitatively ranked list of localization changes that permit user-defined cut-offs, and a vector for each gene describing feature-wise direction and magnitude of localization changes. We demonstrate that our method is effective at the detection of localization changes using the Δrpd3 perturbation in Saccharomyces cerevisiae, where we capture 71.4% of previously known changes within the top 10% of ranked genes, and find at least four new localization changes within the top 1% of ranked genes. The results of our analysis indicate that simple unsupervised methods may be able to identify localization changes in images without laborious manual image labelling steps.

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.

Unsupervised Spatial Event Detection in Targeted Domains with Applications to Civil Unrest Modeling

PubMed Central

Zhao, Liang; Chen, Feng; Dai, Jing; Hua, Ting; Lu, Chang-Tien; Ramakrishnan, Naren

2014-01-01

Twitter has become a popular data source as a surrogate for monitoring and detecting events. Targeted domains such as crime, election, and social unrest require the creation of algorithms capable of detecting events pertinent to these domains. Due to the unstructured language, short-length messages, dynamics, and heterogeneity typical of Twitter data streams, it is technically difficult and labor-intensive to develop and maintain supervised learning systems. We present a novel unsupervised approach for detecting spatial events in targeted domains and illustrate this approach using one specific domain, viz. civil unrest modeling. Given a targeted domain, we propose a dynamic query expansion algorithm to iteratively expand domain-related terms, and generate a tweet homogeneous graph. An anomaly identification method is utilized to detect spatial events over this graph by jointly maximizing local modularity and spatial scan statistics. Extensive experiments conducted in 10 Latin American countries demonstrate the effectiveness of the proposed approach. PMID:25350136

Unsupervised Pattern Classifier for Abnormality-Scaling of Vibration Features for Helicopter Gearbox Fault Diagnosis

NASA Technical Reports Server (NTRS)

Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.

1996-01-01

A new unsupervised pattern classifier is introduced for on-line detection of abnormality in features of vibration that are used for fault diagnosis of helicopter gearboxes. This classifier compares vibration features with their respective normal values and assigns them a value in (0, 1) to reflect their degree of abnormality. Therefore, the salient feature of this classifier is that it does not require feature values associated with faulty cases to identify abnormality. In order to cope with noise and changes in the operating conditions, an adaptation algorithm is incorporated that continually updates the normal values of the features. The proposed classifier is tested using experimental vibration features obtained from an OH-58A main rotor gearbox. The overall performance of this classifier is then evaluated by integrating the abnormality-scaled features for detection of faults. The fault detection results indicate that the performance of this classifier is comparable to the leading unsupervised neural networks: Kohonen's Feature Mapping and Adaptive Resonance Theory (AR72). This is significant considering that the independence of this classifier from fault-related features makes it uniquely suited to abnormality-scaling of vibration features for fault diagnosis.

Predicting protein complexes using a supervised learning method combined with local structural information.

PubMed

Dong, Yadong; Sun, Yongqi; Qin, Chao

2018-01-01

The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein interaction (PPI) networks even though many true complexes are not dense subgraphs. Supervised learning methods utilize the informative properties of known complexes; they often extract features from existing complexes and then use the features to train a classification model. The trained model is used to guide the search process for new complexes. However, insufficient extracted features, noise in the PPI data and the incompleteness of complex data make the classification model imprecise. Consequently, the classification model is not sufficient for guiding the detection of complexes. Therefore, we propose a new robust score function that combines the classification model with local structural information. Based on the score function, we provide a search method that works both forwards and backwards. The results from experiments on six benchmark PPI datasets and three protein complex datasets show that our approach can achieve better performance compared with the state-of-the-art supervised, semi-supervised and unsupervised methods for protein complex detection, occasionally significantly outperforming such methods.

Unsupervised detection of salt marsh platforms: a topographic method

NASA Astrophysics Data System (ADS)

Goodwin, Guillaume C. H.; Mudd, Simon M.; Clubb, Fiona J.

2018-03-01

Salt marshes filter pollutants, protect coastlines against storm surges, and sequester carbon, yet are under threat from sea level rise and anthropogenic modification. The sustained existence of the salt marsh ecosystem depends on the topographic evolution of marsh platforms. Quantifying marsh platform topography is vital for improving the management of these valuable landscapes. The determination of platform boundaries currently relies on supervised classification methods requiring near-infrared data to detect vegetation, or demands labour-intensive field surveys and digitisation. We propose a novel, unsupervised method to reproducibly isolate salt marsh scarps and platforms from a digital elevation model (DEM), referred to as Topographic Identification of Platforms (TIP). Field observations and numerical models show that salt marshes mature into subhorizontal platforms delineated by subvertical scarps. Based on this premise, we identify scarps as lines of local maxima on a slope raster, then fill landmasses from the scarps upward, thus isolating mature marsh platforms. We test the TIP method using lidar-derived DEMs from six salt marshes in England with varying tidal ranges and geometries, for which topographic platforms were manually isolated from tidal flats. Agreement between manual and unsupervised classification exceeds 94 % for DEM resolutions of 1 m, with all but one site maintaining an accuracy superior to 90 % for resolutions up to 3 m. For resolutions of 1 m, platforms detected with the TIP method are comparable in surface area to digitised platforms and have similar elevation distributions. We also find that our method allows for the accurate detection of local block failures as small as 3 times the DEM resolution. Detailed inspection reveals that although tidal creeks were digitised as part of the marsh platform, unsupervised classification categorises them as part of the tidal flat, causing an increase in false negatives and overall platform perimeter. This suggests our method may benefit from combination with existing creek detection algorithms. Fallen blocks and high tidal flat portions, associated with potential pioneer zones, can also lead to differences between our method and supervised mapping. Although pioneer zones prove difficult to classify using a topographic method, we suggest that these transition areas should be considered when analysing erosion and accretion processes, particularly in the case of incipient marsh platforms. Ultimately, we have shown that unsupervised classification of marsh platforms from high-resolution topography is possible and sufficient to monitor and analyse topographic evolution.

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.

Unsupervised Anomaly Detection Based on Clustering and Multiple One-Class SVM

NASA Astrophysics Data System (ADS)

Song, Jungsuk; Takakura, Hiroki; Okabe, Yasuo; Kwon, Yongjin

Intrusion detection system (IDS) has played an important role as a device to defend our networks from cyber attacks. However, since it is unable to detect unknown attacks, i.e., 0-day attacks, the ultimate challenge in intrusion detection field is how we can exactly identify such an attack by an automated manner. Over the past few years, several studies on solving these problems have been made on anomaly detection using unsupervised learning techniques such as clustering, one-class support vector machine (SVM), etc. Although they enable one to construct intrusion detection models at low cost and effort, and have capability to detect unforeseen attacks, they still have mainly two problems in intrusion detection: a low detection rate and a high false positive rate. In this paper, we propose a new anomaly detection method based on clustering and multiple one-class SVM in order to improve the detection rate while maintaining a low false positive rate. We evaluated our method using KDD Cup 1999 data set. Evaluation results show that our approach outperforms the existing algorithms reported in the literature; especially in detection of unknown attacks.

Unsupervised Online Classifier in Sleep Scoring for Sleep Deprivation Studies

PubMed Central

Libourel, Paul-Antoine; Corneyllie, Alexandra; Luppi, Pierre-Hervé; Chouvet, Guy; Gervasoni, Damien

2015-01-01

Study Objective: This study was designed to evaluate an unsupervised adaptive algorithm for real-time detection of sleep and wake states in rodents. Design: We designed a Bayesian classifier that automatically extracts electroencephalogram (EEG) and electromyogram (EMG) features and categorizes non-overlapping 5-s epochs into one of the three major sleep and wake states without any human supervision. This sleep-scoring algorithm is coupled online with a new device to perform selective paradoxical sleep deprivation (PSD). Settings: Controlled laboratory settings for chronic polygraphic sleep recordings and selective PSD. Participants: Ten adult Sprague-Dawley rats instrumented for chronic polysomnographic recordings Measurements: The performance of the algorithm is evaluated by comparison with the score obtained by a human expert reader. Online detection of PS is then validated with a PSD protocol with duration of 72 hours. Results: Our algorithm gave a high concordance with human scoring with an average κ coefficient > 70%. Notably, the specificity to detect PS reached 92%. Selective PSD using real-time detection of PS strongly reduced PS amounts, leaving only brief PS bouts necessary for the detection of PS in EEG and EMG signals (4.7 ± 0.7% over 72 h, versus 8.9 ± 0.5% in baseline), and was followed by a significant PS rebound (23.3 ± 3.3% over 150 minutes). Conclusions: Our fully unsupervised data-driven algorithm overcomes some limitations of the other automated methods such as the selection of representative descriptors or threshold settings. When used online and coupled with our sleep deprivation device, it represents a better option for selective PSD than other methods like the tedious gentle handling or the platform method. Citation: Libourel PA, Corneyllie A, Luppi PH, Chouvet G, Gervasoni D. Unsupervised online classifier in sleep scoring for sleep deprivation studies. SLEEP 2015;38(5):815–828. PMID:25325478

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.

Detection of infectious disease outbreaks in twenty-two fragile states, 2000-2010: a systematic review

PubMed Central

2011-01-01

Fragile states are home to a sixth of the world's population, and their populations are particularly vulnerable to infectious disease outbreaks. Timely surveillance and control are essential to minimise the impact of these outbreaks, but little evidence is published about the effectiveness of existing surveillance systems. We did a systematic review of the circumstances (mode) of detection of outbreaks occurring in 22 fragile states in the decade 2000-2010 (i.e. all states consistently meeting fragility criteria during the timeframe of the review), as well as time lags from onset to detection of these outbreaks, and from detection to further events in their timeline. The aim of this review was to enhance the evidence base for implementing infectious disease surveillance in these complex, resource-constrained settings, and to assess the relative importance of different routes whereby outbreak detection occurs. We identified 61 reports concerning 38 outbreaks. Twenty of these were detected by existing surveillance systems, but 10 detections occurred following formal notifications by participating health facilities rather than data analysis. A further 15 outbreaks were detected by informal notifications, including rumours. There were long delays from onset to detection (median 29 days) and from detection to further events (investigation, confirmation, declaration, control). Existing surveillance systems yielded the shortest detection delays when linked to reduced barriers to health care and frequent analysis and reporting of incidence data. Epidemic surveillance and control appear to be insufficiently timely in fragile states, and need to be strengthened. Greater reliance on formal and informal notifications is warranted. Outbreak reports should be more standardised and enable monitoring of surveillance systems' effectiveness. PMID:21861869

On the robustness of EC-PC spike detection method for online neural recording.

PubMed

Zhou, Yin; Wu, Tong; Rastegarnia, Amir; Guan, Cuntai; Keefer, Edward; Yang, Zhi

2014-09-30

Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13 μm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

Unsupervised frequency-recognition method of SSVEPs using a filter bank implementation of binary subband CCA.

PubMed

Rabiul Islam, Md; Khademul Islam Molla, Md; Nakanishi, Masaki; Tanaka, Toshihisa

2017-04-01

Recently developed effective methods for detection commands of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) that need calibration for visual stimuli, which cause more time and fatigue prior to the use, as the number of commands increases. This paper develops a novel unsupervised method based on canonical correlation analysis (CCA) for accurate detection of stimulus frequency. A novel unsupervised technique termed as binary subband CCA (BsCCA) is implemented in a multiband approach to enhance the frequency recognition performance of SSVEP. In BsCCA, two subbands are used and a CCA-based correlation coefficient is computed for the individual subbands. In addition, a reduced set of artificial reference signals is used to calculate CCA for the second subband. The analyzing SSVEP is decomposed into multiple subband and the BsCCA is implemented for each one. Then, the overall recognition score is determined by a weighted sum of the canonical correlation coefficients obtained from each band. A 12-class SSVEP dataset (frequency range: 9.25-14.75 Hz with an interval of 0.5 Hz) for ten healthy subjects are used to evaluate the performance of the proposed method. The results suggest that BsCCA significantly improves the performance of SSVEP-based BCI compared to the state-of-the-art methods. The proposed method is an unsupervised approach with averaged information transfer rate (ITR) of 77.04 bits min -1 across 10 subjects. The maximum individual ITR is 107.55 bits min -1 for 12-class SSVEP dataset, whereas, the ITR of 69.29 and 69.44 bits min -1 are achieved with CCA and NCCA respectively. The statistical test shows that the proposed unsupervised method significantly improves the performance of the SSVEP-based BCI. It can be usable in real world applications.

Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images.

PubMed

Bergeles, Christos; Dubis, Adam M; Davidson, Benjamin; Kasilian, Melissa; Kalitzeos, Angelos; Carroll, Joseph; Dubra, Alfredo; Michaelides, Michel; Ourselin, Sebastien

2017-06-01

Precise measurements of photoreceptor numerosity and spatial arrangement are promising biomarkers for the early detection of retinal pathologies and may be valuable in the evaluation of retinal therapies. Adaptive optics scanning light ophthalmoscopy (AOSLO) is a method of imaging that corrects for aberrations of the eye to acquire high-resolution images that reveal the photoreceptor mosaic. These images are typically graded manually by experienced observers, obviating the robust, large-scale use of the technology. This paper addresses unsupervised automated detection of cones in non-confocal, split-detection AOSLO images. Our algorithm leverages the appearance of split-detection images to create a cone model that is used for classification. Results show that it compares favorably to the state-of-the-art, both for images of healthy retinas and for images from patients affected by Stargardt disease. The algorithm presented also compares well to manual annotation while excelling in speed.

Methods for automatic detection of artifacts in microelectrode recordings.

PubMed

Bakštein, Eduard; Sieger, Tomáš; Wild, Jiří; Novák, Daniel; Schneider, Jakub; Vostatek, Pavel; Urgošík, Dušan; Jech, Robert

2017-10-01

Extracellular microelectrode recording (MER) is a prominent technique for studies of extracellular single-unit neuronal activity. In order to achieve robust results in more complex analysis pipelines, it is necessary to have high quality input data with a low amount of artifacts. We show that noise (mainly electromagnetic interference and motion artifacts) may affect more than 25% of the recording length in a clinical MER database. We present several methods for automatic detection of noise in MER signals, based on (i) unsupervised detection of stationary segments, (ii) large peaks in the power spectral density, and (iii) a classifier based on multiple time- and frequency-domain features. We evaluate the proposed methods on a manually annotated database of 5735 ten-second MER signals from 58 Parkinson's disease patients. The existing methods for artifact detection in single-channel MER that have been rigorously tested, are based on unsupervised change-point detection. We show on an extensive real MER database that the presented techniques are better suited for the task of artifact identification and achieve much better results. The best-performing classifiers (bagging and decision tree) achieved artifact classification accuracy of up to 89% on an unseen test set and outperformed the unsupervised techniques by 5-10%. This was close to the level of agreement among raters using manual annotation (93.5%). We conclude that the proposed methods are suitable for automatic MER denoising and may help in the efficient elimination of undesirable signal artifacts. Copyright © 2017 Elsevier B.V. All rights reserved.

Detection of molecular signatures of oral squamous cell carcinoma and normal epithelium - application of a novel methodology for unsupervised segmentation of imaging mass spectrometry data.

PubMed

Widlak, Piotr; Mrukwa, Grzegorz; Kalinowska, Magdalena; Pietrowska, Monika; Chekan, Mykola; Wierzgon, Janusz; Gawin, Marta; Drazek, Grzegorz; Polanska, Joanna

2016-06-01

Intra-tumor heterogeneity is a vivid problem of molecular oncology that could be addressed by imaging mass spectrometry. Here we aimed to assess molecular heterogeneity of oral squamous cell carcinoma and to detect signatures discriminating normal and cancerous epithelium. Tryptic peptides were analyzed by MALDI-IMS in tissue specimens from five patients with oral cancer. Novel algorithm of IMS data analysis was developed and implemented, which included Gaussian mixture modeling for detection of spectral components and iterative k-means algorithm for unsupervised spectra clustering performed in domain reduced to a subset of the most dispersed components. About 4% of the detected peptides showed significantly different abundances between normal epithelium and tumor, and could be considered as a molecular signature of oral cancer. Moreover, unsupervised clustering revealed two major sub-regions within expert-defined tumor areas. One of them showed molecular similarity with histologically normal epithelium. The other one showed similarity with connective tissue, yet was markedly different from normal epithelium. Pathologist's re-inspection of tissue specimens confirmed distinct features in both tumor sub-regions: foci of actual cancer cells or cancer microenvironment-related cells prevailed in corresponding areas. Hence, molecular differences detected during automated segmentation of IMS data had an apparent reflection in real structures present in tumor. © 2016 The Authors. Proteomics Published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

An assessment of public health surveillance of Zika virus infection and potentially associated outcomes in Latin America.

PubMed

Bautista, Leonelo E; Herrera, Víctor M

2018-05-24

We evaluated whether outbreaks of Zika virus (ZIKV) infection, newborn microcephaly, and Guillain-Barré syndrome (GBS) in Latin America may be detected through current surveillance systems, and how cases detected through surveillance may increase health care burden. We estimated the sensitivity and specificity of surveillance case definitions using published data. We assumed a 10% ZIKV infection risk during a non-outbreak period and hypothetical increases in risk during an outbreak period. We used sensitivity and specificity estimates to correct for non-differential misclassification, and calculated a misclassification-corrected relative risk comparing both periods. To identify the smallest hypothetical increase in risk resulting in a detectable outbreak we compared the misclassification-corrected relative risk to the relative risk corresponding to the upper limit of the endemic channel (mean + 2 SD). We also estimated the proportion of false positive cases detected during the outbreak. We followed the same approach for microcephaly and GBS, but assumed the risk of ZIKV infection doubled during the outbreak, and ZIKV infection increased the risk of both diseases. ZIKV infection outbreaks were not detectable through non-serological surveillance. Outbreaks were detectable through serologic surveillance if infection risk increased by at least 10%, but more than 50% of all cases were false positive. Outbreaks of severe microcephaly were detected if ZIKV infection increased prevalence of this condition by at least 24.0 times. When ZIKV infection did not increase the prevalence of severe microcephaly, 34.7 to 82.5% of all cases were false positive, depending on diagnostic accuracy. GBS outbreaks were detected if ZIKV infection increased the GBS risk by at least seven times. For optimal GBS diagnosis accuracy, the proportion of false positive cases ranged from 29 to 54% and from 45 to 56% depending on the incidence of GBS mimics. Current surveillance systems have a low probability of detecting outbreaks of ZIKV infection, severe microcephaly, and GBS, and could result in significant increases in health care burden, due to the detection of large numbers of false positive cases. In view of these limitations, Latin American countries should consider alternative options for surveillance.

Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks.

PubMed

Azcorra, A; Chiroque, L F; Cuevas, R; Fernández Anta, A; Laniado, H; Lillo, R E; Romo, J; Sguera, C

2018-05-03

Billions of users interact intensively every day via Online Social Networks (OSNs) such as Facebook, Twitter, or Google+. This makes OSNs an invaluable source of information, and channel of actuation, for sectors like advertising, marketing, or politics. To get the most of OSNs, analysts need to identify influential users that can be leveraged for promoting products, distributing messages, or improving the image of companies. In this report we propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), based on outliers detection, for providing support in the identification of influential users. MUOD is scalable, and can hence be used in large OSNs. Moreover, it labels the outliers as of shape, magnitude, or amplitude, depending of their features. This allows classifying the outlier users in multiple different classes, which are likely to include different types of influential users. Applying MUOD to a subset of roughly 400 million Google+ users, it has allowed identifying and discriminating automatically sets of outlier users, which present features associated to different definitions of influential users, like capacity to attract engagement, capacity to attract a large number of followers, or high infection capacity.

A single-layer network unsupervised feature learning method for white matter hyperintensity segmentation

NASA Astrophysics Data System (ADS)

Vijverberg, Koen; Ghafoorian, Mohsen; van Uden, Inge W. M.; de Leeuw, Frank-Erik; Platel, Bram; Heskes, Tom

2016-03-01

Cerebral small vessel disease (SVD) is a disorder frequently found among the old people and is associated with deterioration in cognitive performance, parkinsonism, motor and mood impairments. White matter hyperintensities (WMH) as well as lacunes, microbleeds and subcortical brain atrophy are part of the spectrum of image findings, related to SVD. Accurate segmentation of WMHs is important for prognosis and diagnosis of multiple neurological disorders such as MS and SVD. Almost all of the published (semi-)automated WMH detection models employ multiple complex hand-crafted features, which require in-depth domain knowledge. In this paper we propose to apply a single-layer network unsupervised feature learning (USFL) method to avoid hand-crafted features, but rather to automatically learn a more efficient set of features. Experimental results show that a computer aided detection system with a USFL system outperforms a hand-crafted approach. Moreover, since the two feature sets have complementary properties, a hybrid system that makes use of both hand-crafted and unsupervised learned features, shows a significant performance boost compared to each system separately, getting close to the performance of an independent human expert.

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning

PubMed Central

Wu, Jiayi; Ma, Yong-Bei; Congdon, Charles; Brett, Bevin; Chen, Shuobing; Xu, Yaofang; Ouyang, Qi

2017-01-01

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization. PMID:28786986

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

PubMed

Wu, Jiayi; Ma, Yong-Bei; Congdon, Charles; Brett, Bevin; Chen, Shuobing; Xu, Yaofang; Ouyang, Qi; Mao, Youdong

2017-01-01

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

Unsupervised algorithms for intrusion detection and identification in wireless ad hoc sensor networks

NASA Astrophysics Data System (ADS)

Hortos, William S.

2009-05-01

In previous work by the author, parameters across network protocol layers were selected as features in supervised algorithms that detect and identify certain intrusion attacks on wireless ad hoc sensor networks (WSNs) carrying multisensor data. The algorithms improved the residual performance of the intrusion prevention measures provided by any dynamic key-management schemes and trust models implemented among network nodes. The approach of this paper does not train algorithms on the signature of known attack traffic, but, instead, the approach is based on unsupervised anomaly detection techniques that learn the signature of normal network traffic. Unsupervised learning does not require the data to be labeled or to be purely of one type, i.e., normal or attack traffic. The approach can be augmented to add any security attributes and quantified trust levels, established during data exchanges among nodes, to the set of cross-layer features from the WSN protocols. A two-stage framework is introduced for the security algorithms to overcome the problems of input size and resource constraints. The first stage is an unsupervised clustering algorithm which reduces the payload of network data packets to a tractable size. The second stage is a traditional anomaly detection algorithm based on a variation of support vector machines (SVMs), whose efficiency is improved by the availability of data in the packet payload. In the first stage, selected algorithms are adapted to WSN platforms to meet system requirements for simple parallel distributed computation, distributed storage and data robustness. A set of mobile software agents, acting like an ant colony in securing the WSN, are distributed at the nodes to implement the algorithms. The agents move among the layers involved in the network response to the intrusions at each active node and trustworthy neighborhood, collecting parametric values and executing assigned decision tasks. This minimizes the need to move large amounts of audit-log data through resource-limited nodes and locates routines closer to that data. Performance of the unsupervised algorithms is evaluated against the network intrusions of black hole, flooding, Sybil and other denial-of-service attacks in simulations of published scenarios. Results for scenarios with intentionally malfunctioning sensors show the robustness of the two-stage approach to intrusion anomalies.

[Study of tuberculosis outbreaks reported in Catalonia, 1998-2002].

PubMed

Bran, Carlos M; Caylá, Joan A; Domínguez, Angela; Camps, Neus; Godoy, Pere; Orcau, Angels; Barrabeig, Irene; Alcaide, José; Altet, Neus; Alvarez, Pep

2006-06-01

To analyze the characteristics of tuberculosis outbreaks declared under vigilance programs in Catalonia. Descriptive study of outbreaks from 1998 through 2002 for which reports were available. An outbreak was defined as 3 or more associated cases appearing within a year. For 2 health care regions, outbreaks for which there were full surveillance reports with contact tracing were compared to outbreaks identified but which had not been fully reported. Twenty-seven outbreaks were analyzed. Nineteen (70%) occurred within families. A total of 22 outbreaks were declared upon identification of the true index case and 5 upon detection of secondary cases. The mean annual incidence of outbreaks was 0.40/100,100 inhabitants. Most cases were in males 16 to 40 years of age and involved cavitary lesions and a clinically significant diagnostic delay. Twenty-seven outbreaks caused 69 secondary cases. A longer diagnostic delay was seen to correspond to a larger number of secondary cases (P=.08). In the 2 health care regions analyzed, full surveillance reports with contact tracing were issued for 2 of the 14 outbreaks detected (14.4%). Tuberculosis outbreaks are common but investigative follow-up is scarce. The size of the outbreak is related to the length of diagnostic delay. Rapid diagnosis, contact tracing, and the issuance of a public health report should be priorities in all outbreaks detected.

A Bayesian system to detect and characterize overlapping outbreaks.

PubMed

Aronis, John M; Millett, Nicholas E; Wagner, Michael M; Tsui, Fuchiang; Ye, Ye; Ferraro, Jeffrey P; Haug, Peter J; Gesteland, Per H; Cooper, Gregory F

2017-09-01

Outbreaks of infectious diseases such as influenza are a significant threat to human health. Because there are different strains of influenza which can cause independent outbreaks, and influenza can affect demographic groups at different rates and times, there is a need to recognize and characterize multiple outbreaks of influenza. This paper describes a Bayesian system that uses data from emergency department patient care reports to create epidemiological models of overlapping outbreaks of influenza. Clinical findings are extracted from patient care reports using natural language processing. These findings are analyzed by a case detection system to create disease likelihoods that are passed to a multiple outbreak detection system. We evaluated the system using real and simulated outbreaks. The results show that this approach can recognize and characterize overlapping outbreaks of influenza. We describe several extensions that appear promising. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

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.

Unsupervised change detection in a particular vegetation land cover type using spectral angle mapper

NASA Astrophysics Data System (ADS)

Renza, Diego; Martinez, Estibaliz; Molina, Iñigo; Ballesteros L., Dora M.

2017-04-01

This paper presents a new unsupervised change detection methodology for multispectral images applied to specific land covers. The proposed method involves comparing each image against a reference spectrum, where the reference spectrum is obtained from the spectral signature of the type of coverage you want to detect. In this case the method has been tested using multispectral images (SPOT5) of the community of Madrid (Spain), and multispectral images (Quickbird) of an area over Indonesia that was impacted by the December 26, 2004 tsunami; here, the tests have focused on the detection of changes in vegetation. The image comparison is obtained by applying Spectral Angle Mapper between the reference spectrum and each multitemporal image. Then, a threshold to produce a single image of change is applied, which corresponds to the vegetation zones. The results for each multitemporal image are combined through an exclusive or (XOR) operation that selects vegetation zones that have changed over time. Finally, the derived results were compared against a supervised method based on classification with the Support Vector Machine. Furthermore, the NDVI-differencing and the Spectral Angle Mapper techniques were selected as unsupervised methods for comparison purposes. The main novelty of the method consists in the detection of changes in a specific land cover type (vegetation), therefore, for comparison purposes, the best scenario is to compare it with methods that aim to detect changes in a specific land cover type (vegetation). This is the main reason to select NDVI-based method and the post-classification method (SVM implemented in a standard software tool). To evaluate the improvements using a reference spectrum vector, the results are compared with the basic-SAM method. In SPOT5 image, the overall accuracy was 99.36% and the κ index was 90.11%; in Quickbird image, the overall accuracy was 97.5% and the κ index was 82.16%. Finally, the precision results of the method are comparable to those of a supervised method, supported by low detection of false positives and false negatives, along with a high overall accuracy and a high kappa index. On the other hand, the execution times were comparable to those of unsupervised methods of low computational load.

A methodological framework for the evaluation of syndromic surveillance systems: a case study of England.

PubMed

Colón-González, Felipe J; Lake, Iain R; Morbey, Roger A; Elliot, Alex J; Pebody, Richard; Smith, Gillian E

2018-04-24

Syndromic surveillance complements traditional public health surveillance by collecting and analysing health indicators in near real time. The rationale of syndromic surveillance is that it may detect health threats faster than traditional surveillance systems permitting more timely, and hence potentially more effective public health action. The effectiveness of syndromic surveillance largely relies on the methods used to detect aberrations. Very few studies have evaluated the performance of syndromic surveillance systems and consequently little is known about the types of events that such systems can and cannot detect. We introduce a framework for the evaluation of syndromic surveillance systems that can be used in any setting based upon the use of simulated scenarios. For a range of scenarios this allows the time and probability of detection to be determined and uncertainty is fully incorporated. In addition, we demonstrate how such a framework can model the benefits of increases in the number of centres reporting syndromic data and also determine the minimum size of outbreaks that can or cannot be detected. Here, we demonstrate its utility using simulations of national influenza outbreaks and localised outbreaks of cryptosporidiosis. Influenza outbreaks are consistently detected with larger outbreaks being detected in a more timely manner. Small cryptosporidiosis outbreaks (<1000 symptomatic individuals) are unlikely to be detected. We also demonstrate the advantages of having multiple syndromic data streams (e.g. emergency attendance data, telephone helpline data, general practice consultation data) as different streams are able to detect different outbreak types with different efficacy (e.g. emergency attendance data are useful for the detection of pandemic influenza but not for outbreaks of cryptosporidiosis). We also highlight that for any one disease, the utility of data streams may vary geographically, and that the detection ability of syndromic surveillance varies seasonally (e.g. an influenza outbreak starting in July is detected sooner than one starting later in the year). We argue that our framework constitutes a useful tool for public health emergency preparedness in multiple settings. The proposed framework allows the exhaustive evaluation of any syndromic surveillance system and constitutes a useful tool for emergency preparedness and response.

Efficient Personalized Mispronunciation Detection of Taiwanese-Accented English Speech Based on Unsupervised Model Adaptation and Dynamic Sentence Selection

ERIC Educational Resources Information Center

Wu, Chung-Hsien; Su, Hung-Yu; Liu, Chao-Hong

2013-01-01

This study presents an efficient approach to personalized mispronunciation detection of Taiwanese-accented English. The main goal of this study was to detect frequently occurring mispronunciation patterns of Taiwanese-accented English instead of scoring English pronunciations directly. The proposed approach quickly identifies personalized…

A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images.

PubMed

Liu, Jia; Gong, Maoguo; Qin, Kai; Zhang, Puzhao

2018-03-01

We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.

A method for detecting and characterizing outbreaks of infectious disease from clinical reports.

PubMed

Cooper, Gregory F; Villamarin, Ricardo; Rich Tsui, Fu-Chiang; Millett, Nicholas; Espino, Jeremy U; Wagner, Michael M

2015-02-01

Outbreaks of infectious disease can pose a significant threat to human health. Thus, detecting and characterizing outbreaks quickly and accurately remains an important problem. This paper describes a Bayesian framework that links clinical diagnosis of individuals in a population to epidemiological modeling of disease outbreaks in the population. Computer-based diagnosis of individuals who seek healthcare is used to guide the search for epidemiological models of population disease that explain the pattern of diagnoses well. We applied this framework to develop a system that detects influenza outbreaks from emergency department (ED) reports. The system diagnoses influenza in individuals probabilistically from evidence in ED reports that are extracted using natural language processing. These diagnoses guide the search for epidemiological models of influenza that explain the pattern of diagnoses well. Those epidemiological models with a high posterior probability determine the most likely outbreaks of specific diseases; the models are also used to characterize properties of an outbreak, such as its expected peak day and estimated size. We evaluated the method using both simulated data and data from a real influenza outbreak. The results provide support that the approach can detect and characterize outbreaks early and well enough to be valuable. We describe several extensions to the approach that appear promising. Copyright © 2014 Elsevier Inc. All rights reserved.

A Method for Detecting and Characterizing Outbreaks of Infectious Disease from Clinical Reports

PubMed Central

Cooper, Gregory F.; Villamarin, Ricardo; Tsui, Fu-Chiang (Rich); Millett, Nicholas; Espino, Jeremy U.; Wagner, Michael M.

2014-01-01

Outbreaks of infectious disease can pose a significant threat to human health. Thus, detecting and characterizing outbreaks quickly and accurately remains an important problem. This paper describes a Bayesian framework that links clinical diagnosis of individuals in a population to epidemiological modeling of disease outbreaks in the population. Computer-based diagnosis of individuals who seek healthcare is used to guide the search for epidemiological models of population disease that explain the pattern of diagnoses well. We applied this framework to develop a system that detects influenza outbreaks from emergency department (ED) reports. The system diagnoses influenza in individuals probabilistically from evidence in ED reports that are extracted using natural language processing. These diagnoses guide the search for epidemiological models of influenza that explain the pattern of diagnoses well. Those epidemiological models with a high posterior probability determine the most likely outbreaks of specific diseases; the models are also used to characterize properties of an outbreak, such as its expected peak day and estimated size. We evaluated the method using both simulated data and data from a real influenza outbreak. The results provide support that the approach can detect and characterize outbreaks early and well enough to be valuable. We describe several extensions to the approach that appear promising. PMID:25181466

Computational efficient unsupervised coastline detection from single-polarization 1-look SAR images of complex coastal environments

NASA Astrophysics Data System (ADS)

Garzelli, Andrea; Zoppetti, Claudia; Pinelli, Gianpaolo

2017-10-01

Coastline detection in synthetic aperture radar (SAR) images is crucial in many application fields, from coastal erosion monitoring to navigation, from damage assessment to security planning for port facilities. The backscattering difference between land and sea is not always documented in SAR imagery, due to the severe speckle noise, especially in 1-look data with high spatial resolution, high sea state, or complex coastal environments. This paper presents an unsupervised, computationally efficient solution to extract the coastline acquired by only one single-polarization 1-look SAR image. Extensive tests on Spotlight COSMO-SkyMed images of complex coastal environments and objective assessment demonstrate the validity of the proposed procedure which is compared to state-of-the-art methods through visual results and with an objective evaluation of the distance between the detected and the true coastline provided by regional authorities.

Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation

NASA Technical Reports Server (NTRS)

Sharma, Manali; Das, Kamalika; Bilgic, Mustafa; Matthews, Bryan; Nielsen, David Lynn; Oza, Nikunj C.

2016-01-01

A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.

Automated detection of hospital outbreaks: A systematic review of methods.

PubMed

Leclère, Brice; Buckeridge, David L; Boëlle, Pierre-Yves; Astagneau, Pascal; Lepelletier, Didier

2017-01-01

Several automated algorithms for epidemiological surveillance in hospitals have been proposed. However, the usefulness of these methods to detect nosocomial outbreaks remains unclear. The goal of this review was to describe outbreak detection algorithms that have been tested within hospitals, consider how they were evaluated, and synthesize their results. We developed a search query using keywords associated with hospital outbreak detection and searched the MEDLINE database. To ensure the highest sensitivity, no limitations were initially imposed on publication languages and dates, although we subsequently excluded studies published before 2000. Every study that described a method to detect outbreaks within hospitals was included, without any exclusion based on study design. Additional studies were identified through citations in retrieved studies. Twenty-nine studies were included. The detection algorithms were grouped into 5 categories: simple thresholds (n = 6), statistical process control (n = 12), scan statistics (n = 6), traditional statistical models (n = 6), and data mining methods (n = 4). The evaluation of the algorithms was often solely descriptive (n = 15), but more complex epidemiological criteria were also investigated (n = 10). The performance measures varied widely between studies: e.g., the sensitivity of an algorithm in a real world setting could vary between 17 and 100%. Even if outbreak detection algorithms are useful complementary tools for traditional surveillance, the heterogeneity in results among published studies does not support quantitative synthesis of their performance. A standardized framework should be followed when evaluating outbreak detection methods to allow comparison of algorithms across studies and synthesis of results.

Environmental swabs as a tool in norovirus outbreak investigation, including outbreaks on cruise ships.

PubMed

Boxman, Ingeborg L A; Dijkman, Remco; te Loeke, Nathalie A J M; Hägele, Geke; Tilburg, Jeroen J H C; Vennema, Harry; Koopmans, Marion

2009-01-01

In this study, we investigated whether environmental swabs can be used to demonstrate the presence of norovirus in outbreak settings. First, a procedure was set up based on viral RNA extraction using guanidium isothiocyanate buffer and binding of nucleic acids to silica. Subsequently, environmental swabs were taken at 23 Dutch restaurants and four cruise ships involved in outbreaks of gastroenteritis. Outbreaks were selected based on clinical symptoms consistent with viral gastroenteritis and time between consumption of suspected food and onset of clinical symptoms (>12 h). Norovirus RNA was demonstrated by real-time reverse transcriptase PCR in 51 of 86 (59%) clinical specimens from 12 of 14 outbreaks (86%), in 13 of 90 (14%) food specimens from 4 of 18 outbreaks (22%), and in 48 of 119 (40%) swab specimens taken from 14 of 27 outbreaks (52%). Positive swab samples agreed with positive clinical samples in seven outbreaks, showing identical sequences. Furthermore, norovirus was detected on swabs taken from kitchen and bathroom surfaces in five outbreaks in which no clinical samples were collected and two outbreaks with negative fecal samples. The detection rate was highest for outbreaks associated with catered meals and lowest for restaurant-associated outbreaks. The use of environmental swabs may be a useful tool in addition to testing of food and clinical specimens, particularlywhen viral RNA is detected on surfaces used for food preparation.

Automated biosurveillance data from England and Wales, 1991-2011.

PubMed

Enki, Doyo G; Noufaily, Angela; Garthwaite, Paul H; Andrews, Nick J; Charlett, André; Lane, Chris; Farrington, C Paddy

2013-01-01

Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. To inform the development of more effective outbreak detection algorithms, we analyzed 20 years of data (1991-2011) from a large laboratory surveillance database used for outbreak detection in England and Wales. The data relate to 3,303 distinct types of infectious pathogens, with a frequency range spanning 6 orders of magnitude. Several hundred organism types were reported each week. We describe the diversity of seasonal patterns, trends, artifacts, and extra-Poisson variability to which an effective multiple laboratory-based outbreak detection system must adjust. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms, in the light of the key requirements of such outbreak detection systems, namely, robustness, flexibility, and sensitivity.

Automated Biosurveillance Data from England and Wales, 1991–2011

PubMed Central

Enki, Doyo G.; Noufaily, Angela; Garthwaite, Paul H.; Andrews, Nick J.; Charlett, André; Lane, Chris

2013-01-01

Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. To inform the development of more effective outbreak detection algorithms, we analyzed 20 years of data (1991–2011) from a large laboratory surveillance database used for outbreak detection in England and Wales. The data relate to 3,303 distinct types of infectious pathogens, with a frequency range spanning 6 orders of magnitude. Several hundred organism types were reported each week. We describe the diversity of seasonal patterns, trends, artifacts, and extra-Poisson variability to which an effective multiple laboratory-based outbreak detection system must adjust. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms, in the light of the key requirements of such outbreak detection systems, namely, robustness, flexibility, and sensitivity. PMID:23260848

Case study of early detection and intervention of infectious disease outbreaks in an institution using Nursery School Absenteeism Surveillance Systems (NSASSy) of the Public Health Center.

PubMed

Matsumoto, Kayo; Hirayama, Chifumi; Sakuma, Yoko; Itoi, Yoichi; Sunadori, Asami; Kitamura, Junko; Nakahashi, Takeshi; Sugawara, Tamie; Ohkusa, Yasushi

2016-01-01

Objectives Detecting outbreaks early and then activating countermeasures based on such information is extremely important for infection control at childcare facilities. The Sumida ward began operating the Nursery School Absenteeism Surveillance System (NSASSy) in August 2013, and has since conducted real-time monitoring at nursery schools. The Public Health Center can detect outbreaks early and support appropriate intervention. This paper describes the experiences of Sumida Public Health Center related to early detection and intervention since the initiation of the system.Methods In this study, we investigated infectious disease outbreaks detected at 62 nursery schools in the Sumida ward, which were equipped with NSASSy from early November 2013 through late March 2015. We classified the information sources of the detected outbreak and responses of the public health center. The sources were (1) direct contact from some nursery schools, (2) messages from public officers with jurisdiction over nursery schools, (3) automatic detection by NSASSy, and (4) manual detection by public health center officers using NSASSy. The responses made by the health center were described and classified into 11 categories including verification of outbreak and advice for caregivers.Results The number of outbreaks detected by the aforementioned four information sources was zero, 25, 15, and 7 events, respectively, during the first 5 months after beginning NSASSy. These numbers became 5, 7, 53, and 25 events, respectively, during the subsequent 12 months. The number of outbreaks detected increased by 47% during the first 5 months, and by 87% in the following 12 months. The responses were primarily confirming the situation and offering advice to caregivers.Conclusion The Sumida Public Health Center ward could achieve early detection with automatic or manual detection of NSASSy. This system recently has become an important detection resource, and has contributed greatly to early detection. Because the Public Health Center can use it to achieve real-time monitoring, they can recognize emergent situations and intervene earlier, and thereby give feedback to the nursery schools. The system can contribute to providing effective countermeasures in these settings.

Unsupervised frequency-recognition method of SSVEPs using a filter bank implementation of binary subband CCA

NASA Astrophysics Data System (ADS)

Rabiul Islam, Md; Khademul Islam Molla, Md; Nakanishi, Masaki; Tanaka, Toshihisa

2017-04-01

Objective. Recently developed effective methods for detection commands of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) that need calibration for visual stimuli, which cause more time and fatigue prior to the use, as the number of commands increases. This paper develops a novel unsupervised method based on canonical correlation analysis (CCA) for accurate detection of stimulus frequency. Approach. A novel unsupervised technique termed as binary subband CCA (BsCCA) is implemented in a multiband approach to enhance the frequency recognition performance of SSVEP. In BsCCA, two subbands are used and a CCA-based correlation coefficient is computed for the individual subbands. In addition, a reduced set of artificial reference signals is used to calculate CCA for the second subband. The analyzing SSVEP is decomposed into multiple subband and the BsCCA is implemented for each one. Then, the overall recognition score is determined by a weighted sum of the canonical correlation coefficients obtained from each band. Main results. A 12-class SSVEP dataset (frequency range: 9.25-14.75 Hz with an interval of 0.5 Hz) for ten healthy subjects are used to evaluate the performance of the proposed method. The results suggest that BsCCA significantly improves the performance of SSVEP-based BCI compared to the state-of-the-art methods. The proposed method is an unsupervised approach with averaged information transfer rate (ITR) of 77.04 bits min-1 across 10 subjects. The maximum individual ITR is 107.55 bits min-1 for 12-class SSVEP dataset, whereas, the ITR of 69.29 and 69.44 bits min-1 are achieved with CCA and NCCA respectively. Significance. The statistical test shows that the proposed unsupervised method significantly improves the performance of the SSVEP-based BCI. It can be usable in real world applications.

Unsupervised detection and removal of muscle artifacts from scalp EEG recordings using canonical correlation analysis, wavelets and random forests.

PubMed

Anastasiadou, Maria N; Christodoulakis, Manolis; Papathanasiou, Eleftherios S; Papacostas, Savvas S; Mitsis, Georgios D

2017-09-01

This paper proposes supervised and unsupervised algorithms for automatic muscle artifact detection and removal from long-term EEG recordings, which combine canonical correlation analysis (CCA) and wavelets with random forests (RF). The proposed algorithms first perform CCA and continuous wavelet transform of the canonical components to generate a number of features which include component autocorrelation values and wavelet coefficient magnitude values. A subset of the most important features is subsequently selected using RF and labelled observations (supervised case) or synthetic data constructed from the original observations (unsupervised case). The proposed algorithms are evaluated using realistic simulation data as well as 30min epochs of non-invasive EEG recordings obtained from ten patients with epilepsy. We assessed the performance of the proposed algorithms using classification performance and goodness-of-fit values for noisy and noise-free signal windows. In the simulation study, where the ground truth was known, the proposed algorithms yielded almost perfect performance. In the case of experimental data, where expert marking was performed, the results suggest that both the supervised and unsupervised algorithm versions were able to remove artifacts without affecting noise-free channels considerably, outperforming standard CCA, independent component analysis (ICA) and Lagged Auto-Mutual Information Clustering (LAMIC). The proposed algorithms achieved excellent performance for both simulation and experimental data. Importantly, for the first time to our knowledge, we were able to perform entirely unsupervised artifact removal, i.e. without using already marked noisy data segments, achieving performance that is comparable to the supervised case. Overall, the results suggest that the proposed algorithms yield significant future potential for improving EEG signal quality in research or clinical settings without the need for marking by expert neurophysiologists, EMG signal recording and user visual inspection. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Compliance with 14-day primaquine therapy for radical cure of vivax malaria--a randomized placebo-controlled trial comparing unsupervised with supervised treatment.

PubMed

Leslie, Toby; Rab, Mohammad Abdur; Ahmadzai, Hayat; Durrani, Naeem; Fayaz, Mohammad; Kolaczinski, Jan; Rowland, Mark

2004-03-01

The only available treatment that can eliminate the latent hypnozoite reservoir of vivax malaria is a 14 d course of primaquine (PQ). A potential problem with long-course chemotherapy is the issue of compliance after clinical symptoms have subsided. The present study, carried out at an Afghan refugee camp in Pakistan, between June 2000 and August 2001, compared 14 d treatment in supervised and unsupervised groups in which compliance was monitored by comparison of relapse rates. Clinical cases recruited by passive case detection were randomised by family to placebo, supervised, or unsupervised groups, and treated with chloroquine (25 mg/kg) over 3 days to eliminate erythrocytic stages. Individuals with glucose-6-phosphate dehydrogenase (G6PD) deficiency were excluded from the trial. Cases allocated to supervision were given directly observed treatment (0.25 mg PQ/kg body weight) once per day for 14 days. Cases allocated to the unsupervised group were provided with 14 PQ doses upon enrollment and strongly advised to complete the course. A total of 595 cases were enrolled. After 9 months of follow up PQ proved equally protective against further episodes of P. vivax in supervised (odds ratio 0.35, 95% CI 0.21-0.57) and unsupervised (odds ratio 0.37, 95% CI 0.23-0.59) groups as compared to placebo. All age groups on supervised or unsupervised treatment showed a similar degree of protection even though the risk of relapse decreased with age. The study showed that a presumed problem of poor compliance may be overcome with simple health messages even when the majority of individuals are illiterate and without formal education. Unsupervised treatment with 14-day PQ when combined with simple instruction can avert a significant amount of the morbidity associated with relapse in populations where G6PD deficiency is either absent or readily diagnosable.

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.

Review of syndromic surveillance: implications for waterborne disease detection

PubMed Central

Berger, Magdalena; Shiau, Rita; Weintraub, June M

2006-01-01

Syndromic surveillance is the gathering of data for public health purposes before laboratory or clinically confirmed information is available. Interest in syndromic surveillance has increased because of concerns about bioterrorism. In addition to bioterrorism detection, syndromic surveillance may be suited to detecting waterborne disease outbreaks. Theoretical benefits of syndromic surveillance include potential timeliness, increased response capacity, ability to establish baseline disease burdens, and ability to delineate the geographical reach of an outbreak. This review summarises the evidence gathered from retrospective, prospective, and simulation studies to assess the efficacy of syndromic surveillance for waterborne disease detection. There is little evidence that syndromic surveillance mitigates the effects of disease outbreaks through earlier detection and response. Syndromic surveillance should not be implemented at the expense of traditional disease surveillance, and should not be relied upon as a principal outbreak detection tool. The utility of syndromic surveillance is dependent on alarm thresholds that can be evaluated in practice. Syndromic data sources such as over the counter drug sales for detection of waterborne outbreaks should be further evaluated. PMID:16698988

Automated detection of hospital outbreaks: A systematic review of methods

PubMed Central

Buckeridge, David L.; Lepelletier, Didier

2017-01-01

Objectives Several automated algorithms for epidemiological surveillance in hospitals have been proposed. However, the usefulness of these methods to detect nosocomial outbreaks remains unclear. The goal of this review was to describe outbreak detection algorithms that have been tested within hospitals, consider how they were evaluated, and synthesize their results. Methods We developed a search query using keywords associated with hospital outbreak detection and searched the MEDLINE database. To ensure the highest sensitivity, no limitations were initially imposed on publication languages and dates, although we subsequently excluded studies published before 2000. Every study that described a method to detect outbreaks within hospitals was included, without any exclusion based on study design. Additional studies were identified through citations in retrieved studies. Results Twenty-nine studies were included. The detection algorithms were grouped into 5 categories: simple thresholds (n = 6), statistical process control (n = 12), scan statistics (n = 6), traditional statistical models (n = 6), and data mining methods (n = 4). The evaluation of the algorithms was often solely descriptive (n = 15), but more complex epidemiological criteria were also investigated (n = 10). The performance measures varied widely between studies: e.g., the sensitivity of an algorithm in a real world setting could vary between 17 and 100%. Conclusion Even if outbreak detection algorithms are useful complementary tools for traditional surveillance, the heterogeneity in results among published studies does not support quantitative synthesis of their performance. A standardized framework should be followed when evaluating outbreak detection methods to allow comparison of algorithms across studies and synthesis of results. PMID:28441422

Multiple outbreaks of Norwalk-like virus gastro-enteritis associated with a Mediterranean-style restaurant.

PubMed

Marshall, J A; Yuen, L K; Catton, M G; Gunesekere, I C; Wright, P J; Bettelheim, K A; Griffith, J M; Lightfoot, D; Hogg, G G; Gregory, J; Wilby, R; Gaston, J

2001-02-01

The role of diverse infectious agents, particularly Norwalk-like viruses (NLV), in three successive gastro-enteritis outbreaks in one setting (a restaurant) was evaluated. Methods included standard bacteriological tests, specific tests for Escherichia coli, tests for verocytotoxins, electron microscopy (EM) for viruses and reverse transcription-PCR (RT-PCR) methodology for NLV. No pathogenic bacteria were detected. Verocytotoxin genes, although detected by PCR in the first outbreak, could not be confirmed in the E. coli isolated, so they did not appear to be of significance. NLV was the main agent detected in each of the three outbreaks. DNA sequencing and phylogenetic analysis of the amplified products obtained from the RT-PCR positive specimens indicated that only one NLV strain was involved in each outbreak, but the NLV strains responsible for the three outbreaks were different from each other. PCR technology for detection of NLV proved highly sensitive, but failed to detect one specimen which was positive by EM. The restaurant associated with the outbreaks is a Mediterranean-style restaurant where food from a common platter is typically eaten with fingers. The findings indicate that NLV was introduced by guests or staff and was not due to a long-term reservoir within the setting.

Segmentation of fluorescence microscopy cell images using unsupervised mining.

PubMed

Du, Xian; Dua, Sumeet

2010-05-28

The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu's threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu's threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

PubMed

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

NASA Astrophysics Data System (ADS)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Unsupervised online classifier in sleep scoring for sleep deprivation studies.

PubMed

Libourel, Paul-Antoine; Corneyllie, Alexandra; Luppi, Pierre-Hervé; Chouvet, Guy; Gervasoni, Damien

2015-05-01

This study was designed to evaluate an unsupervised adaptive algorithm for real-time detection of sleep and wake states in rodents. We designed a Bayesian classifier that automatically extracts electroencephalogram (EEG) and electromyogram (EMG) features and categorizes non-overlapping 5-s epochs into one of the three major sleep and wake states without any human supervision. This sleep-scoring algorithm is coupled online with a new device to perform selective paradoxical sleep deprivation (PSD). Controlled laboratory settings for chronic polygraphic sleep recordings and selective PSD. Ten adult Sprague-Dawley rats instrumented for chronic polysomnographic recordings. The performance of the algorithm is evaluated by comparison with the score obtained by a human expert reader. Online detection of PS is then validated with a PSD protocol with duration of 72 hours. Our algorithm gave a high concordance with human scoring with an average κ coefficient > 70%. Notably, the specificity to detect PS reached 92%. Selective PSD using real-time detection of PS strongly reduced PS amounts, leaving only brief PS bouts necessary for the detection of PS in EEG and EMG signals (4.7 ± 0.7% over 72 h, versus 8.9 ± 0.5% in baseline), and was followed by a significant PS rebound (23.3 ± 3.3% over 150 minutes). Our fully unsupervised data-driven algorithm overcomes some limitations of the other automated methods such as the selection of representative descriptors or threshold settings. When used online and coupled with our sleep deprivation device, it represents a better option for selective PSD than other methods like the tedious gentle handling or the platform method. © 2015 Associated Professional Sleep Societies, LLC.

Strategies for Early Outbreak Detection of Malaria in the Amhara Region of Ethiopia

NASA Astrophysics Data System (ADS)

Nekorchuk, D.; Gebrehiwot, T.; Mihretie, A.; Awoke, W.; Wimberly, M. C.

2017-12-01

Traditional epidemiological approaches to early detection of disease outbreaks are based on relatively straightforward thresholds (e.g. 75th percentile, standard deviations) estimated from historical case data. For diseases with strong seasonality, these can be modified to create separate thresholds for each seasonal time step. However, for disease processes that are non-stationary, more sophisticated techniques are needed to more accurately estimate outbreak threshold values. Early detection for geohealth-related diseases that also have environmental drivers, such as vector-borne diseases, may also benefit from the integration of time-lagged environmental data and disease ecology models into the threshold calculations. The Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) project has been integrating malaria case surveillance with remotely-sensed environmental data for early detection, warning, and forecasting of malaria epidemics in the Amhara region of Ethiopia, and has five years of weekly time series data from 47 woredas (districts). Efforts to reduce the burden of malaria in Ethiopia has been met with some notable success in the past two decades with major reduction in cases and deaths. However, malaria remains a significant public health threat as 60% of the population live in malarious areas, and due to the seasonal and unstable transmission patterns with cyclic outbreaks, protective immunity is generally low which could cause high morbidity and mortality during the epidemics. This study compared several approaches for defining outbreak thresholds and for identifying a potential outbreak based on deviations from these thresholds. We found that model-based approaches that accounted for climate-driven seasonality in malaria transmission were most effective, and that incorporating a trend component improved outbreak detection in areas with active malaria elimination efforts. An advantage of these early detection techniques is that they can detect climate-driven outbreaks as well as outbreaks driven by social factors such as human migration.

Outbreak detection and evaluation of a school-based influenza-like-illness syndromic surveillance in Tianjin, China.

PubMed

Xu, Wenti; Chen, Tianmu; Dong, Xiaochun; Kong, Mei; Lv, Xiuzhi; Li, Lin

2017-01-01

School-based influenza-like-illness (ILI) syndromic surveillance can be an important part of influenza community surveillance by providing early warnings for outbreaks and leading to a fast response. From September 2012 to December 2014, syndromic surveillance of ILI was carried out in 4 county-level schools. The cumulative sum methods(CUSUM) was used to detect abnormal signals. A susceptible-exposed-infectious/asymptomatic-recovered (SEIAR) model was fit to the influenza outbreak without control measures and compared with the actual influenza outbreak to evaluate the effectiveness of early control efforts. The ILI incidence rates in 2014 (14.51%) was higher than the incidence in 2013 (5.27%) and 2012 (3.59%). Ten school influenza outbreaks were detected by CUSUM. Each outbreak had high transmissibility with a median Runc of 4.62. The interventions in each outbreak had high effectiveness and all Rcon were 0. The early intervention had high effectiveness within the school-based ILI syndromic surveillance. Syndromic surveillance within schools can play an important role in controlling influenza outbreaks.

Automatic early warning of tail biting in pigs: 3D cameras can detect lowered tail posture before an outbreak

PubMed Central

Jack, Mhairi; Futro, Agnieszka; Talbot, Darren; Zhu, Qiming; Barclay, David; Baxter, Emma M.

2018-01-01

Tail biting is a major welfare and economic problem for indoor pig producers worldwide. Low tail posture is an early warning sign which could reduce tail biting unpredictability. Taking a precision livestock farming approach, we used Time-of-flight 3D cameras, processing data with machine vision algorithms, to automate the measurement of pig tail posture. Validation of the 3D algorithm found an accuracy of 73.9% at detecting low vs. not low tails (Sensitivity 88.4%, Specificity 66.8%). Twenty-three groups of 29 pigs per group were reared with intact (not docked) tails under typical commercial conditions over 8 batches. 15 groups had tail biting outbreaks, following which enrichment was added to pens and biters and/or victims were removed and treated. 3D data from outbreak groups showed the proportion of low tail detections increased pre-outbreak and declined post-outbreak. Pre-outbreak, the increase in low tails occurred at an increasing rate over time, and the proportion of low tails was higher one week pre-outbreak (-1) than 2 weeks pre-outbreak (-2). Within each batch, an outbreak and a non-outbreak control group were identified. Outbreak groups had more 3D low tail detections in weeks -1, +1 and +2 than their matched controls. Comparing 3D tail posture and tail injury scoring data, a greater proportion of low tails was associated with more injured pigs. Low tails might indicate more than just tail biting as tail posture varied between groups and over time and the proportion of low tails increased when pigs were moved to a new pen. Our findings demonstrate the potential for a 3D machine vision system to automate tail posture detection and provide early warning of tail biting on farm. PMID:29617403

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.

An Improved Unsupervised Image Segmentation Evaluation Approach Based on - and Over-Segmentation Aware

NASA Astrophysics Data System (ADS)

Su, Tengfei

2018-04-01

In this paper, an unsupervised evaluation scheme for remote sensing image segmentation is developed. Based on a method called under- and over-segmentation aware (UOA), the new approach is improved by overcoming the defect in the part of estimating over-segmentation error. Two cases of such error-prone defect are listed, and edge strength is employed to devise a solution to this issue. Two subsets of high resolution remote sensing images were used to test the proposed algorithm, and the experimental results indicate its superior performance, which is attributed to its improved OSE detection model.

Novel Hyperspectral Anomaly Detection Methods Based on Unsupervised Nearest Regularized Subspace

NASA Astrophysics Data System (ADS)

Hou, Z.; Chen, Y.; Tan, K.; Du, P.

2018-04-01

Anomaly detection has been of great interest in hyperspectral imagery analysis. Most conventional anomaly detectors merely take advantage of spectral and spatial information within neighboring pixels. In this paper, two methods of Unsupervised Nearest Regularized Subspace-based with Outlier Removal Anomaly Detector (UNRSORAD) and Local Summation UNRSORAD (LSUNRSORAD) are proposed, which are based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. Using a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination. The existence of outliers in the dual window will affect detection accuracy. Proposed detectors remove outlier pixels that are significantly different from majority of pixels. In order to make full use of various local spatial distributions information with the neighboring pixels of the pixels under test, we take the local summation dual-window sliding strategy. The residual image is constituted by subtracting the predicted background from the original hyperspectral imagery, and anomalies can be detected in the residual image. Experimental results show that the proposed methods have greatly improved the detection accuracy compared with other traditional detection method.

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

Recombinant Temporal Aberration Detection Algorithms for Enhanced Biosurveillance

PubMed Central

Murphy, Sean Patrick; Burkom, Howard

2008-01-01

Objective Broadly, this research aims to improve the outbreak detection performance and, therefore, the cost effectiveness of automated syndromic surveillance systems by building novel, recombinant temporal aberration detection algorithms from components of previously developed detectors. Methods This study decomposes existing temporal aberration detection algorithms into two sequential stages and investigates the individual impact of each stage on outbreak detection performance. The data forecasting stage (Stage 1) generates predictions of time series values a certain number of time steps in the future based on historical data. The anomaly measure stage (Stage 2) compares features of this prediction to corresponding features of the actual time series to compute a statistical anomaly measure. A Monte Carlo simulation procedure is then used to examine the recombinant algorithms’ ability to detect synthetic aberrations injected into authentic syndromic time series. Results New methods obtained with procedural components of published, sometimes widely used, algorithms were compared to the known methods using authentic datasets with plausible stochastic injected signals. Performance improvements were found for some of the recombinant methods, and these improvements were consistent over a range of data types, outbreak types, and outbreak sizes. For gradual outbreaks, the WEWD MovAvg7+WEWD Z-Score recombinant algorithm performed best; for sudden outbreaks, the HW+WEWD Z-Score performed best. Conclusion This decomposition was found not only to yield valuable insight into the effects of the aberration detection algorithms but also to produce novel combinations of data forecasters and anomaly measures with enhanced detection performance. PMID:17947614

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

Early outbreak detection by linking health advice line calls to water distribution areas retrospectively demonstrated in a large waterborne outbreak of cryptosporidiosis in Sweden.

PubMed

Bjelkmar, Pär; Hansen, Anette; Schönning, Caroline; Bergström, Jakob; Löfdahl, Margareta; Lebbad, Marianne; Wallensten, Anders; Allestam, Görel; Stenmark, Stephan; Lindh, Johan

2017-04-18

In the winter and spring of 2011 a large outbreak of cryptosporidiosis occurred in Skellefteå municipality, Sweden. This study summarizes the outbreak investigation in terms of outbreak size, duration, clinical characteristics, possible source(s) and the potential for earlier detection using calls to a health advice line. The investigation included two epidemiological questionnaires and microbial analysis of samples from patients, water and other environmental sources. In addition, a retrospective study based on phone calls to a health advice line was performed by comparing patterns of phone calls between different water distribution areas. Our analyses showed that approximately 18,500 individuals were affected by a waterborne outbreak of cryptosporidiosis in Skellefteå in 2011. This makes it the second largest outbreak of cryptosporidiosis in Europe to date. Cryptosporidium hominis oocysts of subtype IbA10G2 were found in patient and sewage samples, but not in raw water or in drinking water, and the initial contamination source could not be determined. The outbreak went unnoticed to authorities for several months. The analysis of the calls to the health advice line provides strong indications early in the outbreak that it was linked to a particular water treatment plant. We conclude that an earlier detection of the outbreak by linking calls to a health advice line to water distribution areas could have limited the outbreak substantially.

Detection of Pediatric Respiratory and Diarrheal Outbreaks from Sales of Over-the-counter Electrolyte Products

PubMed Central

Hogan, William R.; Tsui, Fu-Chiang; Ivanov, Oleg; Gesteland, Per H.; Grannis, Shaun; Overhage, J. Marc; Robinson, J. Michael; Wagner, Michael M.

2003-01-01

Objective: To determine whether sales of electrolyte products contain a signal of outbreaks of respiratory and diarrheal disease in children and, if so, how much earlier a signal relative to hospital diagnoses. Design: Retrospective analysis was conducted of sales of electrolyte products and hospital diagnoses for six urban regions in three states for the period 1998 through 2001. Measurements: Presence of signal was ascertained by measuring correlation between electrolyte sales and hospital diagnoses and the temporal relationship that maximized correlation. Earliness was the difference between the date that the exponentially weighted moving average (EWMA) method first detected an outbreak from sales and the date it first detected the outbreak from diagnoses. The coefficient of determination (r2) measured how much variance in earliness resulted from differences in sales' and diagnoses' signal strengths. Results: The correlation between electrolyte sales and hospital diagnoses was 0.90 (95% CI, 0.87–0.93) at a time offset of 1.7 weeks (95% CI, 0.50–2.9), meaning that sales preceded diagnoses by 1.7 weeks. EWMA with a nine-sigma threshold detected the 18 outbreaks on average 2.4 weeks (95% CI, 0.1–4.8 weeks) earlier from sales than from diagnoses. Twelve outbreaks were first detected from sales, four were first detected from diagnoses, and two were detected simultaneously. Only 26% of variance in earliness was explained by the relative strength of the sales and diagnoses signals (r2 = 0.26). Conclusion: Sales of electrolyte products contain a signal of outbreaks of respiratory and diarrheal diseases in children and usually are an earlier signal than hospital diagnoses. PMID:12925542

Social media posts and online search behaviour as early-warning system for MRSA outbreaks.

PubMed

van de Belt, Tom H; van Stockum, Pieter T; Engelen, Lucien J L P G; Lancee, Jules; Schrijver, Remco; Rodríguez-Baño, Jesús; Tacconelli, Evelina; Saris, Katja; van Gelder, Marleen M H J; Voss, Andreas

2018-01-01

Despite many preventive measures, outbreaks with multi-drug resistant micro-organisms (MDROs) still occur. Moreover, current alert systems from healthcare organizations have shortcomings due to delayed or incomplete notifications, which may amplify the spread of MDROs by introducing infected patients into a new healthcare setting and institutions. Additional sources of information about upcoming and current outbreaks, may help to prevent further spread of MDROs.The study objective was to evaluate whether methicillin-resistant Staphylococcus aureus (MRSA) outbreaks could be detected via social media posts or online search behaviour; if so, this might allow earlier detection than the official notifications by healthcare organizations. We conducted an exploratory study in which we compared information about MRSA outbreaks in the Netherlands derived from two online sources, Coosto for Social Media, and Google Trends for search behaviour, to the mandatory Dutch outbreak notification system (SO-ZI/AMR). The latter provides information on MDRO outbreaks including the date of the outbreak, micro-organism involved, the region/location, and the type of health care organization. During the research period of 15 months (455 days), 49 notifications of outbreaks were recorded in SO-ZI/AMR. For Coosto, the number of unique potential outbreaks was 37 and for Google Trends 24. The use of social media and online search behaviour missed many of the hospital outbreaks that were reported to SO-ZI/AMR, but detected additional outbreaks in long-term care facilities. Despite several limitations, using information from social media and online search behaviour allows rapid identification of potential MRSA outbreaks, especially in healthcare settings with a low notification compliance. When combined in an automated system with real-time updates, this approach might increase early discovery and subsequent implementation of preventive measures.

An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3.

PubMed

Liu, Wensong; Yang, Jie; Zhao, Jinqi; Shi, Hongtao; Yang, Le

2018-02-12

The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by R j test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient.

Change detection and classification in brain MR images using change vector analysis.

PubMed

Simões, Rita; Slump, Cornelis

2011-01-01

The automatic detection of longitudinal changes in brain images is valuable in the assessment of disease evolution and treatment efficacy. Most existing change detection methods that are currently used in clinical research to monitor patients suffering from neurodegenerative diseases--such as Alzheimer's--focus on large-scale brain deformations. However, such patients often have other brain impairments, such as infarcts, white matter lesions and hemorrhages, which are typically overlooked by the deformation-based methods. Other unsupervised change detection algorithms have been proposed to detect tissue intensity changes. The outcome of these methods is typically a binary change map, which identifies changed brain regions. However, understanding what types of changes these regions underwent is likely to provide equally important information about lesion evolution. In this paper, we present an unsupervised 3D change detection method based on Change Vector Analysis. We compute and automatically threshold the Generalized Likelihood Ratio map to obtain a binary change map. Subsequently, we perform histogram-based clustering to classify the change vectors. We obtain a Kappa Index of 0.82 using various types of simulated lesions. The classification error is 2%. Finally, we are able to detect and discriminate both small changes and ventricle expansions in datasets from Mild Cognitive Impairment patients.

A space-time scan statistic for detecting emerging outbreaks.

PubMed

Tango, Toshiro; Takahashi, Kunihiko; Kohriyama, Kazuaki

2011-03-01

As a major analytical method for outbreak detection, Kulldorff's space-time scan statistic (2001, Journal of the Royal Statistical Society, Series A 164, 61-72) has been implemented in many syndromic surveillance systems. Since, however, it is based on circular windows in space, it has difficulty correctly detecting actual noncircular clusters. Takahashi et al. (2008, International Journal of Health Geographics 7, 14) proposed a flexible space-time scan statistic with the capability of detecting noncircular areas. It seems to us, however, that the detection of the most likely cluster defined in these space-time scan statistics is not the same as the detection of localized emerging disease outbreaks because the former compares the observed number of cases with the conditional expected number of cases. In this article, we propose a new space-time scan statistic which compares the observed number of cases with the unconditional expected number of cases, takes a time-to-time variation of Poisson mean into account, and implements an outbreak model to capture localized emerging disease outbreaks more timely and correctly. The proposed models are illustrated with data from weekly surveillance of the number of absentees in primary schools in Kitakyushu-shi, Japan, 2006. © 2010, The International Biometric Society.

Unsupervised individual tree crown detection in high-resolution satellite imagery

DOE PAGES

Skurikhin, Alexei N.; McDowell, Nate G.; Middleton, Richard S.

2016-01-26

Rapidly and accurately detecting individual tree crowns in satellite imagery is a critical need for monitoring and characterizing forest resources. We present a two-stage semiautomated approach for detecting individual tree crowns using high spatial resolution (0.6 m) satellite imagery. First, active contours are used to recognize tree canopy areas in a normalized difference vegetation index image. Given the image areas corresponding to tree canopies, we then identify individual tree crowns as local extrema points in the Laplacian of Gaussian scale-space pyramid. The approach simultaneously detects tree crown centers and estimates tree crown sizes, parameters critical to multiple ecosystem models. Asmore » a demonstration, we used a ground validated, 0.6 m resolution QuickBird image of a sparse forest site. The two-stage approach produced a tree count estimate with an accuracy of 78% for a naturally regenerating forest with irregularly spaced trees, a success rate equivalent to or better than existing approaches. In addition, our approach detects tree canopy areas and individual tree crowns in an unsupervised manner and helps identify overlapping crowns. Furthermore, the method also demonstrates significant potential for further improvement.« less

Unsupervised change detection of multispectral images based on spatial constraint chi-squared transform and Markov random field model

NASA Astrophysics Data System (ADS)

Shi, Aiye; Wang, Chao; Shen, Shaohong; Huang, Fengchen; Ma, Zhenli

2016-10-01

Chi-squared transform (CST), as a statistical method, can describe the difference degree between vectors. The CST-based methods operate directly on information stored in the difference image and are simple and effective methods for detecting changes in remotely sensed images that have been registered and aligned. However, the technique does not take spatial information into consideration, which leads to much noise in the result of change detection. An improved unsupervised change detection method is proposed based on spatial constraint CST (SCCST) in combination with a Markov random field (MRF) model. First, the mean and variance matrix of the difference image of bitemporal images are estimated by an iterative trimming method. In each iteration, spatial information is injected to reduce scattered changed points (also known as "salt and pepper" noise). To determine the key parameter confidence level in the SCCST method, a pseudotraining dataset is constructed to estimate the optimal value. Then, the result of SCCST, as an initial solution of change detection, is further improved by the MRF model. The experiments on simulated and real multitemporal and multispectral images indicate that the proposed method performs well in comprehensive indices compared with other methods.

Unsupervised individual tree crown detection in high-resolution satellite imagery

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

Skurikhin, Alexei N.; McDowell, Nate G.; Middleton, Richard S.

Rapidly and accurately detecting individual tree crowns in satellite imagery is a critical need for monitoring and characterizing forest resources. We present a two-stage semiautomated approach for detecting individual tree crowns using high spatial resolution (0.6 m) satellite imagery. First, active contours are used to recognize tree canopy areas in a normalized difference vegetation index image. Given the image areas corresponding to tree canopies, we then identify individual tree crowns as local extrema points in the Laplacian of Gaussian scale-space pyramid. The approach simultaneously detects tree crown centers and estimates tree crown sizes, parameters critical to multiple ecosystem models. Asmore » a demonstration, we used a ground validated, 0.6 m resolution QuickBird image of a sparse forest site. The two-stage approach produced a tree count estimate with an accuracy of 78% for a naturally regenerating forest with irregularly spaced trees, a success rate equivalent to or better than existing approaches. In addition, our approach detects tree canopy areas and individual tree crowns in an unsupervised manner and helps identify overlapping crowns. Furthermore, the method also demonstrates significant potential for further improvement.« less

A primitive study on unsupervised anomaly detection with an autoencoder in emergency head CT volumes

NASA Astrophysics Data System (ADS)

Sato, Daisuke; Hanaoka, Shouhei; Nomura, Yukihiro; Takenaga, Tomomi; Miki, Soichiro; Yoshikawa, Takeharu; Hayashi, Naoto; Abe, Osamu

2018-02-01

Purpose: The target disorders of emergency head CT are wide-ranging. Therefore, people working in an emergency department desire a computer-aided detection system for general disorders. In this study, we proposed an unsupervised anomaly detection method in emergency head CT using an autoencoder and evaluated the anomaly detection performance of our method in emergency head CT. Methods: We used a 3D convolutional autoencoder (3D-CAE), which contains 11 layers in the convolution block and 6 layers in the deconvolution block. In the training phase, we trained the 3D-CAE using 10,000 3D patches extracted from 50 normal cases. In the test phase, we calculated abnormalities of each voxel in 38 emergency head CT volumes (22 abnormal cases and 16 normal cases) for evaluation and evaluated the likelihood of lesion existence. Results: Our method achieved a sensitivity of 68% and a specificity of 88%, with an area under the curve of the receiver operating characteristic curve of 0.87. It shows that this method has a moderate accuracy to distinguish normal CT cases to abnormal ones. Conclusion: Our method has potentialities for anomaly detection in emergency head CT.

Genomics-enabled sensor platform for rapid detection of viruses related to disease outbreak.

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

Brozik, Susan M; Manginell, Ronald P; Moorman, Matthew W

2013-09-01

Bioweapons and emerging infectious diseases pose growing threats to our national security. Both natural disease outbreak and outbreaks due to a bioterrorist attack are a challenge to detect, taking days after the outbreak to identify since most outbreaks are only recognized through reportable diseases by health departments and reports of unusual diseases by clinicians. In recent decades, arthropod-borne viruses (arboviruses) have emerged as some of the most significant threats to human health. They emerge, often unexpectedly, from cryptic transmission foci causing localized outbreaks that can rapidly spread to multiple continents due to increased human travel and trade. Currently, diagnosis ofmore » acute infections requires amplification of viral nucleic acids, which can be costly, highly specific, technically challenging and time consuming. No diagnostic devices suitable for use at the bedside or in an outbreak setting currently exist. The original goals of this project were to 1) develop two highly sensitive and specific diagnostic assays for detecting RNA from a wide range of arboviruses; one based on an electrochemical approach and the other a fluorescent based assay and 2) develop prototype microfluidic diagnostic platforms for preclinical and field testing that utilize the assays developed in goal 1. We generated and characterized suitable primers for West Nile Virus RNA detection. Both optical and electrochemical transduction technologies were developed for DNA-RNA hybridization detection and were implemented in microfluidic diagnostic sensing platforms that were developed in this project.« less

Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-occurrence Data.

PubMed

Schouten, Kim; van der Weijde, Onne; Frasincar, Flavius; Dekker, Rommert

2018-04-01

Using online consumer reviews as electronic word of mouth to assist purchase-decision making has become increasingly popular. The Web provides an extensive source of consumer reviews, but one can hardly read all reviews to obtain a fair evaluation of a product or service. A text processing framework that can summarize reviews, would therefore be desirable. A subtask to be performed by such a framework would be to find the general aspect categories addressed in review sentences, for which this paper presents two methods. In contrast to most existing approaches, the first method presented is an unsupervised method that applies association rule mining on co-occurrence frequency data obtained from a corpus to find these aspect categories. While not on par with state-of-the-art supervised methods, the proposed unsupervised method performs better than several simple baselines, a similar but supervised method, and a supervised baseline, with an -score of 67%. The second method is a supervised variant that outperforms existing methods with an -score of 84%.

Characterization of Foodborne Outbreaks of Salmonella enterica Serovar Enteritidis with Whole-Genome Sequencing Single Nucleotide Polymorphism-Based Analysis for Surveillance and Outbreak Detection.

PubMed

Taylor, Angela J; Lappi, Victoria; Wolfgang, William J; Lapierre, Pascal; Palumbo, Michael J; Medus, Carlota; Boxrud, David

2015-10-01

Salmonella enterica serovar Enteritidis is a significant cause of gastrointestinal illness in the United States; however, current molecular subtyping methods lack resolution for this highly clonal serovar. Advances in next-generation sequencing technologies have made it possible to examine whole-genome sequencing (WGS) as a potential molecular subtyping tool for outbreak detection and source trace back. Here, we conducted a retrospective analysis of S. Enteritidis isolates from seven epidemiologically confirmed foodborne outbreaks and sporadic isolates (not epidemiologically linked) to determine the utility of WGS to identify outbreaks. A collection of 55 epidemiologically characterized clinical and environmental S. Enteritidis isolates were sequenced. Single nucleotide polymorphism (SNP)-based cluster analysis of the S. Enteritidis genomes revealed well supported clades, with less than four-SNP pairwise diversity, that were concordant with epidemiologically defined outbreaks. Sporadic isolates were an average of 42.5 SNPs distant from the outbreak clusters. Isolates collected from the same patient over several weeks differed by only two SNPs. Our findings show that WGS provided greater resolution between outbreak, sporadic, and suspect isolates than the current gold standard subtyping method, pulsed-field gel electrophoresis (PFGE). Furthermore, results could be obtained in a time frame suitable for surveillance activities, supporting the use of WGS as an outbreak detection and characterization method for S. Enteritidis. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Characterizing Interference in Radio Astronomy Observations through Active and Unsupervised Learning

NASA Technical Reports Server (NTRS)

Doran, G.

2013-01-01

In the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of manmade radio sources such as cell phones, satellites, aircraft, and observatory equipment. Radio frequency interference (RFI) often occurs as short bursts (< 1 ms) across a broad range of frequencies, and can be confused with signals from sources of interest such as pulsars. With ever-increasing volumes of data being produced by observatories, automated strategies are required to detect, classify, and characterize these short "transient" RFI events. We investigate an active learning approach in which an astronomer labels events that are most confusing to a classifier, minimizing the human effort required for classification. We also explore the use of unsupervised clustering techniques, which automatically group events into classes without user input. We apply these techniques to data from the Parkes Multibeam Pulsar Survey to characterize several million detected RFI events from over a thousand hours of observation.

Early breast tumor and late SARS detections using space-variant multispectral infrared imaging at a single pixel

NASA Astrophysics Data System (ADS)

Szu, Harold H.; Buss, James R.; Kopriva, Ivica

2004-04-01

We proposed the physics approach to solve a physical inverse problem, namely to choose the unique equilibrium solution (at the minimum free energy: H= E - ToS, including the Wiener, l.m.s E, and ICA, Max S, as special cases). The "unsupervised classification" presumes that required information must be learned and derived directly and solely from the data alone, in consistence with the classical Duda-Hart ATR definition of the "unlabelled data". Such truly unsupervised methodology is presented for space-variant imaging processing for a single pixel in the real world case of remote sensing, early tumor detections and SARS. The indeterminacy of the multiple solutions of the inverse problem is regulated or selected by means of the absolute minimum of isothermal free energy as the ground truth of local equilibrium condition at the single-pixel foot print.

Gastroenteritis outbreaks on cruise ships: contributing factors and thresholds for early outbreak detection.

PubMed

Mouchtouri, Varvara A; Verykouki, Eleni; Zamfir, Dumitru; Hadjipetris, Christos; Lewis, Hannah C; Hadjichristodoulou, Christos

2017-11-01

When an increased number of acute gastroenteritis (AG) cases is detected among tourists staying at the same accommodation, outbreak management plans must be activated in a timely manner to prevent large outbreaks. Syndromic surveillance data collected between 1 January 2010 and 31 December 2013 by five seagoing cruise ships were analysed to identify attack rate thresholds for early outbreak detection. The overall incidence rate of AG was 2.81 cases per 10,000 traveller-days (95% confidence interval (CI): 0.00-17.60), while the attack rate was 19.37 cases per 10,000 travellers (95% CI: 0.00-127.69). The probability of an outbreak occurring was 11% if 4 per 1,000 passengers reported symptoms within the first 2 days of the voyage, and this increased to 23 % if 5 per 1,000 passengers reported such within the first 3 days. The risk ratio (RR) for outbreak occurrence was 2.35, 5.66 and 8.63 for 1, 2 and 3 days' delay of symptoms reporting respectively, suggesting a dose-response relationship. Shipping companies' policies and health authorities' efforts may consider these thresholds for initiating outbreak response measures based on the number of cases according to day of cruise. Efforts should focus on ensuring travellers report symptoms immediately and comply with isolation measures.

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.

A Web-Based Multidrug-Resistant Organisms Surveillance and Outbreak Detection System with Rule-Based Classification and Clustering

PubMed Central

Tseng, Yi-Ju; Wu, Jung-Hsuan; Ping, Xiao-Ou; Lin, Hui-Chi; Chen, Ying-Yu; Shang, Rung-Ji; Chen, Ming-Yuan; Lai, Feipei

2012-01-01

Background The emergence and spread of multidrug-resistant organisms (MDROs) are causing a global crisis. Combating antimicrobial resistance requires prevention of transmission of resistant organisms and improved use of antimicrobials. Objectives To develop a Web-based information system for automatic integration, analysis, and interpretation of the antimicrobial susceptibility of all clinical isolates that incorporates rule-based classification and cluster analysis of MDROs and implements control chart analysis to facilitate outbreak detection. Methods Electronic microbiological data from a 2200-bed teaching hospital in Taiwan were classified according to predefined criteria of MDROs. The numbers of organisms, patients, and incident patients in each MDRO pattern were presented graphically to describe spatial and time information in a Web-based user interface. Hierarchical clustering with 7 upper control limits (UCL) was used to detect suspicious outbreaks. The system’s performance in outbreak detection was evaluated based on vancomycin-resistant enterococcal outbreaks determined by a hospital-wide prospective active surveillance database compiled by infection control personnel. Results The optimal UCL for MDRO outbreak detection was the upper 90% confidence interval (CI) using germ criterion with clustering (area under ROC curve (AUC) 0.93, 95% CI 0.91 to 0.95), upper 85% CI using patient criterion (AUC 0.87, 95% CI 0.80 to 0.93), and one standard deviation using incident patient criterion (AUC 0.84, 95% CI 0.75 to 0.92). The performance indicators of each UCL were statistically significantly higher with clustering than those without clustering in germ criterion (P < .001), patient criterion (P = .04), and incident patient criterion (P < .001). Conclusion This system automatically identifies MDROs and accurately detects suspicious outbreaks of MDROs based on the antimicrobial susceptibility of all clinical isolates. PMID:23195868

Evaluating the utility of syndromic surveillance algorithms for screening to detect potentially clonal hospital infection outbreaks

PubMed Central

Talbot, Thomas R; Schaffner, William; Bloch, Karen C; Daniels, Titus L; Miller, Randolph A

2011-01-01

Objective The authors evaluated algorithms commonly used in syndromic surveillance for use as screening tools to detect potentially clonal outbreaks for review by infection control practitioners. Design Study phase 1 applied four aberrancy detection algorithms (CUSUM, EWMA, space-time scan statistic, and WSARE) to retrospective microbiologic culture data, producing a list of past candidate outbreak clusters. In phase 2, four infectious disease physicians categorized the phase 1 algorithm-identified clusters to ascertain algorithm performance. In phase 3, project members combined the algorithms to create a unified screening system and conducted a retrospective pilot evaluation. Measurements The study calculated recall and precision for each algorithm, and created precision-recall curves for various methods of combining the algorithms into a unified screening tool. Results Individual algorithm recall and precision ranged from 0.21 to 0.31 and from 0.053 to 0.29, respectively. Few candidate outbreak clusters were identified by more than one algorithm. The best method of combining the algorithms yielded an area under the precision-recall curve of 0.553. The phase 3 combined system detected all infection control-confirmed outbreaks during the retrospective evaluation period. Limitations Lack of phase 2 reviewers' agreement indicates that subjective expert review was an imperfect gold standard. Less conservative filtering of culture results and alternate parameter selection for each algorithm might have improved algorithm performance. Conclusion Hospital outbreak detection presents different challenges than traditional syndromic surveillance. Nevertheless, algorithms developed for syndromic surveillance have potential to form the basis of a combined system that might perform clinically useful hospital outbreak screening. PMID:21606134

A parallelized binary search tree

USDA-ARS?s Scientific Manuscript database

PTTRNFNDR is an unsupervised statistical learning algorithm that detects patterns in DNA sequences, protein sequences, or any natural language texts that can be decomposed into letters of a finite alphabet. PTTRNFNDR performs complex mathematical computations and its processing time increases when i...

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.

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.

Integrative analysis of gene expression and DNA methylation using unsupervised feature extraction for detecting candidate cancer biomarkers.

PubMed

Moon, Myungjin; Nakai, Kenta

2018-04-01

Currently, cancer biomarker discovery is one of the important research topics worldwide. In particular, detecting significant genes related to cancer is an important task for early diagnosis and treatment of cancer. Conventional studies mostly focus on genes that are differentially expressed in different states of cancer; however, noise in gene expression datasets and insufficient information in limited datasets impede precise analysis of novel candidate biomarkers. In this study, we propose an integrative analysis of gene expression and DNA methylation using normalization and unsupervised feature extractions to identify candidate biomarkers of cancer using renal cell carcinoma RNA-seq datasets. Gene expression and DNA methylation datasets are normalized by Box-Cox transformation and integrated into a one-dimensional dataset that retains the major characteristics of the original datasets by unsupervised feature extraction methods, and differentially expressed genes are selected from the integrated dataset. Use of the integrated dataset demonstrated improved performance as compared with conventional approaches that utilize gene expression or DNA methylation datasets alone. Validation based on the literature showed that a considerable number of top-ranked genes from the integrated dataset have known relationships with cancer, implying that novel candidate biomarkers can also be acquired from the proposed analysis method. Furthermore, we expect that the proposed method can be expanded for applications involving various types of multi-omics datasets.

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

Syndromic Surveillance Using Veterinary Laboratory Data: Algorithm Combination and Customization of Alerts

PubMed Central

Dórea, Fernanda C.; McEwen, Beverly J.; McNab, W. Bruce; Sanchez, Javier; Revie, Crawford W.

2013-01-01

Background Syndromic surveillance research has focused on two main themes: the search for data sources that can provide early disease detection; and the development of efficient algorithms that can detect potential outbreak signals. Methods This work combines three algorithms that have demonstrated solid performance in detecting simulated outbreak signals of varying shapes in time series of laboratory submissions counts. These are: the Shewhart control charts designed to detect sudden spikes in counts; the EWMA control charts developed to detect slow increasing outbreaks; and the Holt-Winters exponential smoothing, which can explicitly account for temporal effects in the data stream monitored. A scoring system to detect and report alarms using these algorithms in a complementary way is proposed. Results The use of multiple algorithms in parallel resulted in increased system sensitivity. Specificity was decreased in simulated data, but the number of false alarms per year when the approach was applied to real data was considered manageable (between 1 and 3 per year for each of ten syndromic groups monitored). The automated implementation of this approach, including a method for on-line filtering of potential outbreak signals is described. Conclusion The developed system provides high sensitivity for detection of potential outbreak signals while also providing robustness and flexibility in establishing what signals constitute an alarm. This flexibility allows an analyst to customize the system for different syndromes. PMID:24349216

Syndromic surveillance using veterinary laboratory data: algorithm combination and customization of alerts.

PubMed

Dórea, Fernanda C; McEwen, Beverly J; McNab, W Bruce; Sanchez, Javier; Revie, Crawford W

2013-01-01

Syndromic surveillance research has focused on two main themes: the search for data sources that can provide early disease detection; and the development of efficient algorithms that can detect potential outbreak signals. This work combines three algorithms that have demonstrated solid performance in detecting simulated outbreak signals of varying shapes in time series of laboratory submissions counts. These are: the Shewhart control charts designed to detect sudden spikes in counts; the EWMA control charts developed to detect slow increasing outbreaks; and the Holt-Winters exponential smoothing, which can explicitly account for temporal effects in the data stream monitored. A scoring system to detect and report alarms using these algorithms in a complementary way is proposed. The use of multiple algorithms in parallel resulted in increased system sensitivity. Specificity was decreased in simulated data, but the number of false alarms per year when the approach was applied to real data was considered manageable (between 1 and 3 per year for each of ten syndromic groups monitored). The automated implementation of this approach, including a method for on-line filtering of potential outbreak signals is described. The developed system provides high sensitivity for detection of potential outbreak signals while also providing robustness and flexibility in establishing what signals constitute an alarm. This flexibility allows an analyst to customize the system for different syndromes.

Laboratory preparedness for detection and monitoring of Shiga toxin 2-producing Escherichia coli O104:H4 in Europe and response to the 2011 outbreak.

PubMed

Rosin, P; Niskanen, T; Palm, D; Struelens, M; Takkinen, J

2013-06-20

A hybrid strain of enteroaggregative and Shiga toxin 2-producing Escherichia coli (EAEC-STEC) serotype O104:H4 strain caused a large outbreak of haemolytic uraemic syndrome and bloody diarrhoea in 2011 in Europe. Two surveys were performed in the European Union (EU) and European Economic Area (EEA) countries to assess their laboratory capabilities to detect and characterise this previously uncommon STEC strain. Prior to the outbreak, 11 of the 32 countries in this survey had capacity at national reference laboratory (NRL) level for epidemic case confirmation according to the EU definition. During the outbreak, at primary diagnostic level, nine countries reported that clinical microbiology laboratories routinely used Shiga toxin detection assays suitable for diagnosis of infections with EAEC-STEC O104:H4, while 14 countries had NRL capacity to confirm epidemic cases. Six months after the outbreak, 22 countries reported NRL capacity to confirm such cases following initiatives taken by NRLs and the European Centre for Disease Prevention and Control (ECDC) Food- and Waterborne Disease and Zoonoses laboratory network. These data highlight the challenge of detection and confirmation of epidemic infections caused by atypical STEC strains and the benefits of coordinated EU laboratory networks to strengthen capabilities in response to a major outbreak.

Rapid virus detection procedure for molecular tracing of shellfish associated with disease outbreaks.

PubMed

de Roda Husman, Ana Maria; Lodder-Verschoor, Froukje; van den Berg, Harold H J L; Le Guyader, Françoise S; van Pelt, Hilde; van der Poel, Wim H M; Rutjes, Saskia A

2007-04-01

Detection of pathogenic viruses in oysters implicated in gastroenteritis outbreaks is often hampered by time-consuming, specialist virus extraction methods. Five virus RNA extraction methods were evaluated with respect to performance characteristics and sensitivity on artificially contaminated oyster digestive glands. The two most promising procedures were further evaluated on bioaccumulated and naturally contaminated oysters. The most efficient method was used to trace the source in an outbreak situation. Out of five RNA extraction protocols, PEG precipitation and the RNeasy Kit performed best with norovirus genogroup III-spiked digestive glands. Analyzing 24-h bioaccumulated oysters revealed a slightly better sensitivity with PEG precipitation, but the RNeasy Kit was less prone to concentrate inhibitors. The latter procedure demonstrated the presence of human noroviruses in naturally contaminated oysters and oysters implicated in an outbreak. In this outbreak, in four out of nine individually analyzed digestive glands, norovirus was detected. In one of the oysters and in one of the fecal samples of the clinical cases, identical norovirus strains were detected. A standard and rapid virus extraction method using the RNeasy Kit appeared to be most useful in tracing shellfish as the source in gastroenteritis outbreaks, and to be able to make effective and timely risk management decisions.

A Participatory System for Preventing Pandemics of Animal Origins: Pilot Study of the Participatory One Health Disease Detection (PODD) System

PubMed Central

Yano, Terdsak; Phornwisetsirikun, Somphorn; Susumpow, Patipat; Visrutaratna, Surasing; Chanachai, Karoon; Phetra, Polawat; Chaisowwong, Warangkhana; Trakarnsirinont, Pairat; Hemwan, Phonpat; Kaewpinta, Boontuan; Singhapreecha, Charuk; Kreausukon, Khwanchai; Charoenpanyanet, Arisara ; Robert, Chongchit Sripun; Robert, Lamar; Rodtian, Pranee; Mahasing, Suteerat; Laiya, Ekkachai; Pattamakaew, Sakulrat; Tankitiyanon, Taweesart; Sansamur, Chalutwan

2018-01-01

Background Aiming for early disease detection and prompt outbreak control, digital technology with a participatory One Health approach was used to create a novel disease surveillance system called Participatory One Health Disease Detection (PODD). PODD is a community-owned surveillance system that collects data from volunteer reporters; identifies disease outbreak automatically; and notifies the local governments (LGs), surrounding villages, and relevant authorities. This system provides a direct and immediate benefit to the communities by empowering them to protect themselves. Objective The objective of this study was to determine the effectiveness of the PODD system for the rapid detection and control of disease outbreaks. Methods The system was piloted in 74 LGs in Chiang Mai, Thailand, with the participation of 296 volunteer reporters. The volunteers and LGs were key participants in the piloting of the PODD system. Volunteers monitored animal and human diseases, as well as environmental problems, in their communities and reported these events via the PODD mobile phone app. LGs were responsible for outbreak control and provided support to the volunteers. Outcome mapping was used to evaluate the performance of the LGs and volunteers. Results LGs were categorized into one of the 3 groups based on performance: A (good), B (fair), and C (poor), with the majority (46%,34/74) categorized into group B. Volunteers were similarly categorized into 4 performance groups (A-D), again with group A showing the best performance, with the majority categorized into groups B and C. After 16 months of implementation, 1029 abnormal events had been reported and confirmed to be true reports. The majority of abnormal reports were sick or dead animals (404/1029, 39.26%), followed by zoonoses and other human diseases (129/1029, 12.54%). Many potentially devastating animal disease outbreaks were detected and successfully controlled, including 26 chicken high mortality outbreaks, 4 cattle disease outbreaks, 3 pig disease outbreaks, and 3 fish disease outbreaks. In all cases, the communities and animal authorities cooperated to apply community contingency plans to control these outbreaks, and community volunteers continued to monitor the abnormal events for 3 weeks after each outbreak was controlled. Conclusions By design, PODD initially targeted only animal diseases that potentially could emerge into human pandemics (eg, avian influenza) and then, in response to community needs, expanded to cover human health and environmental health issues. PMID:29563079

Neural Evidence of Statistical Learning: Efficient Detection of Visual Regularities without Awareness

ERIC Educational Resources Information Center

Turk-Browne, Nicholas B.; Scholl, Brian J.; Chun, Marvin M.; Johnson, Marcia K.

2009-01-01

Our environment contains regularities distributed in space and time that can be detected by way of statistical learning. This unsupervised learning occurs without intent or awareness, but little is known about how it relates to other types of learning, how it affects perceptual processing, and how quickly it can occur. Here we use fMRI during…

Gastroenteritis outbreaks on cruise ships: contributing factors and thresholds for early outbreak detection

PubMed Central

Mouchtouri, Varvara A; Verykouki, Eleni; Zamfir, Dumitru; Hadjipetris, Christos; Lewis, Hannah C; Hadjichristodoulou, Christos

2017-01-01

When an increased number of acute gastroenteritis (AG) cases is detected among tourists staying at the same accommodation, outbreak management plans must be activated in a timely manner to prevent large outbreaks. Syndromic surveillance data collected between 1 January 2010 and 31 December 2013 by five seagoing cruise ships were analysed to identify attack rate thresholds for early outbreak detection. The overall incidence rate of AG was 2.81 cases per 10,000 traveller-days (95% confidence interval (CI): 0.00–17.60), while the attack rate was 19.37 cases per 10,000 travellers (95% CI: 0.00–127.69). The probability of an outbreak occurring was 11% if 4 per 1,000 passengers reported symptoms within the first 2 days of the voyage, and this increased to 23 % if 5 per 1,000 passengers reported such within the first 3 days. The risk ratio (RR) for outbreak occurrence was 2.35, 5.66 and 8.63 for 1, 2 and 3 days’ delay of symptoms reporting respectively, suggesting a dose–response relationship. Shipping companies’ policies and health authorities’ efforts may consider these thresholds for initiating outbreak response measures based on the number of cases according to day of cruise. Efforts should focus on ensuring travellers report symptoms immediately and comply with isolation measures. PMID:29162205

Automated attribution of remotely-sensed ecological disturbances using spatial and temporal characteristics of common disturbance classes.

NASA Astrophysics Data System (ADS)

Cooper, L. A.; Ballantyne, A.

2017-12-01

Forest disturbances are critical components of ecosystems. Knowledge of their prevalence and impacts is necessary to accurately describe forest health and ecosystem services through time. While there are currently several methods available to identify and describe forest disturbances, especially those which occur in North America, the process remains inefficient and inaccessible in many parts of the world. Here, we introduce a preliminary approach to streamline and automate both the detection and attribution of forest disturbances. We use a combination of the Breaks for Additive Season and Trend (BFAST) detection algorithm to detect disturbances in combination with supervised and unsupervised classification algorithms to attribute the detections to disturbance classes. Both spatial and temporal disturbance characteristics are derived and utilized for the goal of automating the disturbance attribution process. The resulting preliminary algorithm is applied to up-scaled (100m) Landsat data for several different ecosystems in North America, with varying success. Our results indicate that supervised classification is more reliable than unsupervised classification, but that limited training data are required for a region. Future work will improve the algorithm through refining and validating at sites within North America before applying this approach globally.

Evidential analysis of difference images for change detection of multitemporal remote sensing images

NASA Astrophysics Data System (ADS)

Chen, Yin; Peng, Lijuan; Cremers, Armin B.

2018-03-01

In this article, we develop two methods for unsupervised change detection in multitemporal remote sensing images based on Dempster-Shafer's theory of evidence (DST). In most unsupervised change detection methods, the probability of difference image is assumed to be characterized by mixture models, whose parameters are estimated by the expectation maximization (EM) method. However, the main drawback of the EM method is that it does not consider spatial contextual information, which may entail rather noisy detection results with numerous spurious alarms. To remedy this, we firstly develop an evidence theory based EM method (EEM) which incorporates spatial contextual information in EM by iteratively fusing the belief assignments of neighboring pixels to the central pixel. Secondly, an evidential labeling method in the sense of maximizing a posteriori probability (MAP) is proposed in order to further enhance the detection result. It first uses the parameters estimated by EEM to initialize the class labels of a difference image. Then it iteratively fuses class conditional information and spatial contextual information, and updates labels and class parameters. Finally it converges to a fixed state which gives the detection result. A simulated image set and two real remote sensing data sets are used to evaluate the two evidential change detection methods. Experimental results show that the new evidential methods are comparable to other prevalent methods in terms of total error rate.

Unsupervised Ensemble Anomaly Detection Using Time-Periodic Packet Sampling

NASA Astrophysics Data System (ADS)

Uchida, Masato; Nawata, Shuichi; Gu, Yu; Tsuru, Masato; Oie, Yuji

We propose an anomaly detection method for finding patterns in network traffic that do not conform to legitimate (i.e., normal) behavior. The proposed method trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline model is used as the basis for comparison with the audit network traffic. This anomaly detection works in an unsupervised manner through the use of time-periodic packet sampling, which is used in a manner that differs from its intended purpose — the lossy nature of packet sampling is used to extract normal packets from the unlabeled original traffic data. Evaluation using actual traffic traces showed that the proposed method has false positive and false negative rates in the detection of anomalies regarding TCP SYN packets comparable to those of a conventional method that uses manually labeled traffic data to train the baseline model. Performance variation due to the probabilistic nature of sampled traffic data is mitigated by using ensemble anomaly detection that collectively exploits multiple baseline models in parallel. Alarm sensitivity is adjusted for the intended use by using maximum- and minimum-based anomaly detection that effectively take advantage of the performance variations among the multiple baseline models. Testing using actual traffic traces showed that the proposed anomaly detection method performs as well as one using manually labeled traffic data and better than one using randomly sampled (unlabeled) traffic data.

SAR image segmentation using skeleton-based fuzzy clustering

NASA Astrophysics Data System (ADS)

Cao, Yun Yi; Chen, Yan Qiu

2003-06-01

SAR image segmentation can be converted to a clustering problem in which pixels or small patches are grouped together based on local feature information. In this paper, we present a novel framework for segmentation. The segmentation goal is achieved by unsupervised clustering upon characteristic descriptors extracted from local patches. The mixture model of characteristic descriptor, which combines intensity and texture feature, is investigated. The unsupervised algorithm is derived from the recently proposed Skeleton-Based Data Labeling method. Skeletons are constructed as prototypes of clusters to represent arbitrary latent structures in image data. Segmentation using Skeleton-Based Fuzzy Clustering is able to detect the types of surfaces appeared in SAR images automatically without any user input.

Daily Reportable Disease Spatiotemporal Cluster Detection, New York City, New York, USA, 2014-2015.

PubMed

Greene, Sharon K; Peterson, Eric R; Kapell, Deborah; Fine, Annie D; Kulldorff, Martin

2016-10-01

Each day, the New York City Department of Health and Mental Hygiene uses the free SaTScan software to apply prospective space-time permutation scan statistics to strengthen early outbreak detection for 35 reportable diseases. This method prompted early detection of outbreaks of community-acquired legionellosis and shigellosis.

Molecular analysis of an oyster-related norovirus outbreak.

PubMed

Nenonen, Nancy P; Hannoun, Charles; Olsson, Margareta B; Bergström, Tomas

2009-06-01

Contaminated raw oysters were implicated in a severe outbreak of norovirus (NoV) gastroenteritis affecting 30 restaurant guests. To define the outbreak source by using molecular methods to characterize NoV strains detected in patient and oyster samples. Molecular epidemiological studies based on nucleotide sequencing and phylogenetic analyses of patient and oyster NoV strains, and comparison to background dataset. NoV genotype (G) I.1 was detected in the one patient stool analyzed by in-house TaqMan real time RT-PCR and classical nested RT-PCR targeting NoV RNA-dependent polymerase (RdRp, 285 nt), and by nested RT-PCR targeting RdRp-capsid-poly(A)-3' (3085 nt). Patient strain showed >or=99% similarity (285 nt) with three NoV strains detected in two of five oysters examined by classical nested RT-PCR (RdRp). A third oyster tested positive for NoV GII.3. Phylogenetic analysis showed clustering of patient and oyster strains related to this outbreak with GI.1 strains from previous local outbreaks, and mussel studies. Sequence data revealed >or=99% similarity (285 nt) between NoV GI.1 strains detected in patient stool and suspect oysters, linking the contaminated oysters to the outbreak. Identification of human NoV GI and GII strains in oysters indicated contamination of human fecal origin, presumably from inappropriate storage in the harbor. Comparative long-fragment analysis of the patient strain revealed 99% similarity (3085 nt) with NoV GI.1 strains detected in previous outbreaks and environmental mussel studies from West Sweden, 87% with M87661 (Norwalk68) and 96% with L23828 (SRSV-KY-89/89/J). These results indicated considerable genomic stability of NoV GI.1 strains over time.

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

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

Identification of Norovirus as the Top Enteric Viruses Detected in Adult Cases with Acute Gastroenteritis

PubMed Central

Liu, Li-Juan; Liu, Wei; Liu, Yun-Xi; Xiao, Hong-Jv; Jia, Ning; Liu, Gang; Tong, Yi-Gang; Cao, Wu-Chun

2010-01-01

To elucidate the importance of the norovirus and other enteric viruses, and the difference of the genetic relatedness on norovirus between the outbreak and sporadic cases, a total of 557 stool samples, consisting of 503 sporadic cases and 54 samples of 4 outbreaks were collected and tested for norovirus and other enteric viruses in Beijing, China, July 2007–June 2008. The data showed norovirus, rotavirus, astrovirus, and sapovirus, were detected in 26.6%, 6.1%, 1.8%, and 0.5%, respectively. Norovirus was detected almost throughout the surveillance period, norovirus co-infecting with rotavirus, astrovirus, and sapovirus, respectively, were identified both in outbreak and the sporadic cases. GII.4/2006 was identified as the predominant strain circulating both in outbreak and sporadic cases. The results showed that norovirus was rather the important agent than other enteric viruses affected adults with acute gastroenteritis; no significant genetic relatedness of the dominant strains was found between the outbreak and sporadic cases. PMID:20348525

A concept for routine emergency-care data-based syndromic surveillance in Europe.

PubMed

Ziemann, A; Rosenkötter, N; Garcia-Castrillo Riesgo, L; Schrell, S; Kauhl, B; Vergeiner, G; Fischer, M; Lippert, F K; Krämer, A; Brand, H; Krafft, T

2014-11-01

We developed a syndromic surveillance (SyS) concept using emergency dispatch, ambulance and emergency-department data from different European countries. Based on an inventory of sub-national emergency data availability in 12 countries, we propose framework definitions for specific syndromes and a SyS system design. We tested the concept by retrospectively applying cumulative sum and spatio-temporal cluster analyses for the detection of local gastrointestinal outbreaks in four countries and comparing the results with notifiable disease reporting. Routine emergency data was available daily and electronically in 11 regions, following a common structure. We identified two gastrointestinal outbreaks in two countries; one was confirmed as a norovirus outbreak. We detected 1/147 notified outbreaks. Emergency-care data-based SyS can supplement local surveillance with near real-time information on gastrointestinal patients, especially in special circumstances, e.g. foreign tourists. It most likely cannot detect the majority of local gastrointestinal outbreaks with few, mild or dispersed cases.

METHODS FOR DETECTION OF CRYPTOSPORIDIUM SP. AND GIARDIA SP.

EPA Science Inventory

There have been several waterborne outbreaks of giardiasis caused by infection with Giardia lamblia, and cryptosporidiosis, caused by infection with Cryptosporidium parvum. These outbreaks have created a need to detect these organisms in source and finished drinking water. The pr...

A flexibly shaped space-time scan statistic for disease outbreak detection and monitoring.

PubMed

Takahashi, Kunihiko; Kulldorff, Martin; Tango, Toshiro; Yih, Katherine

2008-04-11

Early detection of disease outbreaks enables public health officials to implement disease control and prevention measures at the earliest possible time. A time periodic geographical disease surveillance system based on a cylindrical space-time scan statistic has been used extensively for disease surveillance along with the SaTScan software. In the purely spatial setting, many different methods have been proposed to detect spatial disease clusters. In particular, some spatial scan statistics are aimed at detecting irregularly shaped clusters which may not be detected by the circular spatial scan statistic. Based on the flexible purely spatial scan statistic, we propose a flexibly shaped space-time scan statistic for early detection of disease outbreaks. The performance of the proposed space-time scan statistic is compared with that of the cylindrical scan statistic using benchmark data. In order to compare their performances, we have developed a space-time power distribution by extending the purely spatial bivariate power distribution. Daily syndromic surveillance data in Massachusetts, USA, are used to illustrate the proposed test statistic. The flexible space-time scan statistic is well suited for detecting and monitoring disease outbreaks in irregularly shaped areas.

Prevalence of small round structured virus infections in acute gastroenteritis outbreaks in Tokyo.

PubMed

Sekine, S; Okada, S; Hayashi, Y; Ando, T; Terayama, T; Yabuuchi, K; Miki, T; Ohashi, M

1989-01-01

During the three-year period from 1984 to 1987, 506 acute gastroenteritis outbreaks involving 14,383 patients were reported to the Bureau of Public Health, Tokyo Metropolitan Government. Eighty (4,324 patients) of 150 outbreaks (4,860 patients) from which etiologic agents were not identified were subjected to virological investigation. Spherical particles of 28-32 nm in diameter with capsomere-like structures on the surface were detected in patients' stool specimens. Buoyant density of the particles appeared to be 1.36 to 1.40 g/ml in CsCl. Seroconversion to the particles was observed in patients by immune electron microscopy. From these observations, we concluded that the detected particles were members of small round structured virus (SRSV), and that they were implicated in the etiologically ill-defined outbreaks encountered. Prevalence of SRSV infections in these outbreaks was examined by electron microscopy. SRSV was positive in 83.8% of the outbreaks, and 96.4% of the cases. SRSV-positive outbreaks usually occurred during winter in contrast to bacterial outbreaks which often occurred in the summer season. Of 80 outbreaks examined, 53 were associated with the ingestion of oysters, and the remaining 27 mostly with food other than oysters. Oyster-associated outbreaks usually occurred on a small scale, while unassociated ones on diverse scales ranged from family clusters to large outbreaks.

Distribution of outbreak reporting in health care institutions by day of the week.

PubMed

Amirov, Chingiz; Walton, Ryan N; Ahmed, Sarah; Binns, Malcolm A; Van Toen, Jane E; Candon, Heather L

2012-12-01

The notion that outbreaks are more likely to occur on Friday is prevalent among staff in health care institutions. However, there is little evidence to support or discredit this notion. We postulated that outbreaks were no more likely to be reported on any particular day of the week. A total of 901 institutional outbreaks in Toronto health care facilities were tabulated according to type, outbreak setting, and day of the week reported. A χ(2) goodness-of-fit test compared daily values for 7-day per week and 5-day per week periods. Post hoc partitioning was used to pinpoint specific day(s) of the week that differed significantly. Fewer outbreaks were reported on Saturdays and Sundays. Further analysis examined the distribution of outbreak reporting specifically focusing on the Monday to Friday weekday period. Among the weekdays, higher proportions of outbreaks were reported on Mondays and Fridays. Our null hypothesis was rejected. Overall, Mondays and Fridays had the highest occurrence of outbreak reporting. We suggest that this might be due to "deadline" and "catch-up" reporting related to the "weekend effect," whereby structural differences in weekend staffing affect detection of outbreaks. Such delays warrant reexamination of surveillance processes for timely outbreak detection independent of calendar cycle. Copyright © 2012 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.

Deep sequencing of H7N8 avian influenza viruses from surveillance zone supports H7N8 high pathogenicity avian influenza was limited to a single outbreak farm in Indiana during 2016

USDA-ARS?s Scientific Manuscript database

In mid-January 2016, an outbreak of H7N8 high pathogenicity avian influenza (HPAI) virus in commercial turkeys occurred in Indiana. The outbreak was first detected by an increase in mortality followed by laboratory confirmation of H7N8 HPAI virus. Surveillance within the 10 km Control Zone detected...

Forest defoliator outbreaks under climate change: effects on the frequency and severity of outbreaks of five pine insect pests.

PubMed

Haynes, Kyle J; Allstadt, Andrew J; Klimetzek, Dietrich

2014-06-01

To identify general patterns in the effects of climate change on the outbreak dynamics of forest-defoliating insect species, we examined a 212-year record (1800-2011) of outbreaks of five pine-defoliating species (Bupalus piniarius, Panolis flammea, Lymantria monacha, Dendrolimus pini, and Diprion pini) in Bavaria, Germany for the evidence of climate-driven changes in the severity, cyclicity, and frequency of outbreaks. We also accounted for historical changes in forestry practices and examined effects of past insecticide use to suppress outbreaks. Analysis of relationships between severity or occurrence of outbreaks and detrended measures of temperature and precipitation revealed a mixture of positive and negative relationships between temperature and outbreak activity. Two moth species (P. flammea and Dendrolimus pini) exhibited lower outbreak activity following years or decades of unusually warm temperatures, whereas a sawfly (Diprion pini), for which voltinism is influenced by temperature, displayed increased outbreak occurrence in years of high summer temperatures. We detected only one apparent effect of precipitation, which showed Dendrolimus pini outbreaks tending to follow drought. Wavelet analysis of outbreak time series suggested climate change may be associated with collapse of L. monacha and Dendrolimus pini outbreak cycles (loss of cyclicity and discontinuation of outbreaks, respectively), but high-frequency cycles for B. piniarius and P. flammea in the late 1900s. Regional outbreak severity was generally not related to past suppression efforts (area treated with insecticides). Recent shifts in forestry practices affecting tree species composition roughly coincided with high-frequency outbreak cycles in B. piniarius and P. flammea but are unlikely to explain the detected relationships between climate and outbreak severity or collapses of outbreak cycles. Our results highlight both individualistic responses of different pine-defoliating species to climate changes and some patterns that are consistent across defoliator species in this and other forest systems, including collapsing of population cycles. © 2014 John Wiley & Sons Ltd.

Detecting Abnormal Vehicular Dynamics at Intersections Based on an Unsupervised Learning Approach and a Stochastic Model

PubMed Central

Jiménez-Hernández, Hugo; González-Barbosa, Jose-Joel; Garcia-Ramírez, Teresa

2010-01-01

This investigation demonstrates an unsupervised approach for modeling traffic flow and detecting abnormal vehicle behaviors at intersections. In the first stage, the approach reveals and records the different states of the system. These states are the result of coding and grouping the historical motion of vehicles as long binary strings. In the second stage, using sequences of the recorded states, a stochastic graph model based on a Markovian approach is built. A behavior is labeled abnormal when current motion pattern cannot be recognized as any state of the system or a particular sequence of states cannot be parsed with the stochastic model. The approach is tested with several sequences of images acquired from a vehicular intersection where the traffic flow and duration used in connection with the traffic lights are continuously changed throughout the day. Finally, the low complexity and the flexibility of the approach make it reliable for use in real time systems. PMID:22163616

Detecting abnormal vehicular dynamics at intersections based on an unsupervised learning approach and a stochastic model.

PubMed

Jiménez-Hernández, Hugo; González-Barbosa, Jose-Joel; Garcia-Ramírez, Teresa

2010-01-01

This investigation demonstrates an unsupervised approach for modeling traffic flow and detecting abnormal vehicle behaviors at intersections. In the first stage, the approach reveals and records the different states of the system. These states are the result of coding and grouping the historical motion of vehicles as long binary strings. In the second stage, using sequences of the recorded states, a stochastic graph model based on a Markovian approach is built. A behavior is labeled abnormal when current motion pattern cannot be recognized as any state of the system or a particular sequence of states cannot be parsed with the stochastic model. The approach is tested with several sequences of images acquired from a vehicular intersection where the traffic flow and duration used in connection with the traffic lights are continuously changed throughout the day. Finally, the low complexity and the flexibility of the approach make it reliable for use in real time systems.

Examining change detection approaches for tropical mangrove monitoring

USGS Publications Warehouse

Myint, Soe W.; Franklin, Janet; Buenemann, Michaela; Kim, Won; Giri, Chandra

2014-01-01

This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.

A large Legionnaires' disease outbreak in Pamplona, Spain: early detection, rapid control and no case fatality

PubMed Central

CASTILLA, J.; BARRICARTE, A.; ALDAZ, J.; GARCÍA CENOZ, M.; FERRER, T.; PELAZ, C.; PINEDA, S.; BALADRÓN, B.; MARTÍN, I.; GOÑI, B.; ARATAJO, P.; CHAMORRO, J.; LAMEIRO, F.; TORROBA, L.; DORRONSORO, I.; MARTÍNEZ-ARTOLA, V.; ESPARZA, M. J.; GASTAMINZA, M. A.; FRAILE, P.; ALDAZ, P.

2008-01-01

SUMMARY An outbreak of Legionnaire's disease was detected in Pamplona, Spain, on 1 June 2006. Patients with pneumonia were tested to detect Legionella pneumophila antigen in urine (Binax Now; Binax Inc., Scarborough, ME, USA), and all 146 confirmed cases were interviewed. The outbreak was related to district 2 (22 012 inhabitants), where 45% of the cases lived and 50% had visited; 5% lived in neighbouring districts. The highest incidence was found in the resident population of district 2 (3/1000 inhabitants), section 2 (14/1000). All 31 cooling towers of district 2 were analysed. L. pneumophila antigen (Binax Now) was detected in four towers, which were closed on 2 June. Only the strain isolated in a tower situated in section 2 of district 2 matched all five clinical isolates, as assessed by mAb and two genotyping methods, AFLP and PFGE. Eight days after closing the towers, new cases ceased appearing. Early detection and rapid coordinated medical and environmental actions permitted immediate control of the outbreak and probably contributed to the null case fatality. PMID:17662166

A large Legionnaires' disease outbreak in Pamplona, Spain: early detection, rapid control and no case fatality.

PubMed

Castilla, J; Barricarte, A; Aldaz, J; García Cenoz, M; Ferrer, T; Pelaz, C; Pineda, S; Baladrón, B; Martín, I; Goñi, B; Aratajo, P; Chamorro, J; Lameiro, F; Torroba, L; Dorronsoro, I; Martínez-Artola, V; Esparza, M J; Gastaminza, M A; Fraile, P; Aldaz, P

2008-06-01

An outbreak of Legionnaire's disease was detected in Pamplona, Spain, on 1 June 2006. Patients with pneumonia were tested to detect Legionella pneumophila antigen in urine (Binax Now; Binax Inc., Scarborough, ME, USA), and all 146 confirmed cases were interviewed. The outbreak was related to district 2 (22 012 inhabitants), where 45% of the cases lived and 50% had visited; 5% lived in neighbouring districts. The highest incidence was found in the resident population of district 2 (3/1000 inhabitants), section 2 (14/1000). All 31 cooling towers of district 2 were analysed. L. pneumophila antigen (Binax Now) was detected in four towers, which were closed on 2 June. Only the strain isolated in a tower situated in section 2 of district 2 matched all five clinical isolates, as assessed by mAb and two genotyping methods, AFLP and PFGE. Eight days after closing the towers, new cases ceased appearing. Early detection and rapid coordinated medical and environmental actions permitted immediate control of the outbreak and probably contributed to the null case fatality.

TESTING METHODS FOR DETECTION OF CRYPTOSPORIDIUM SPP. IN WATER SAMPLES

EPA Science Inventory

A large waterborne outbreak of cryptosporidiosis in Milwaukee, Wisconsin, U.S.A. in 1993 prompted a search for ways to prevent large-scale waterborne outbreaks of protozoan parasitoses. Methods for detecting Cryptosporidium parvum play an integral role in strategies that lead to...

Detection of multiple enteric virus strains within a foodborne outbreak of gastroenteritis: an indication of the source of contamination.

PubMed Central

Gallimore, C. I.; Pipkin, C.; Shrimpton, H.; Green, A. D.; Pickford, Y.; McCartney, C.; Sutherland, G.; Brown, D. W. G.; Gray, J. J.

2005-01-01

An outbreak of acute gastroenteritis of suspected viral aetiology occurred in April 2003 in the British Royal Fleet Auxiliary ship (RFA) Argus deployed in the Northern Arabian Gulf. There were 37 cases amongst a crew of 400 personnel. Of 13 samples examined from cases amongst the crew, six enteric viruses were detected by reverse transcriptase polymerase chain reaction (RT-PCR). Five different viruses were identified including, three norovirus genotypes, a sapovirus and a rotavirus. No multiple infections were detected. A common food source was implicated in the outbreak and epidemiological analysis showed a statistically significant association with salad as the source of the outbreak, with a relative risk of 3.41 (95% confidence interval of 1.7-6.81) of eating salad on a particular date prior to the onset of symptoms. Faecal contamination of the salad at source was the most probable explanation for the diversity of viruses detected and characterized. PMID:15724709

Massively multiplexed microbial identification using resequencing DNA microarrays for outbreak investigation

NASA Astrophysics Data System (ADS)

Leski, T. A.; Ansumana, R.; Jimmy, D. H.; Bangura, U.; Malanoski, A. P.; Lin, B.; Stenger, D. A.

2011-06-01

Multiplexed microbial diagnostic assays are a promising method for detection and identification of pathogens causing syndromes characterized by nonspecific symptoms in which traditional differential diagnosis is difficult. Also such assays can play an important role in outbreak investigations and environmental screening for intentional or accidental release of biothreat agents, which requires simultaneous testing for hundreds of potential pathogens. The resequencing pathogen microarray (RPM) is an emerging technological platform, relying on a combination of massively multiplex PCR and high-density DNA microarrays for rapid detection and high-resolution identification of hundreds of infectious agents simultaneously. The RPM diagnostic system was deployed in Sierra Leone, West Africa in collaboration with Njala University and Mercy Hospital Research Laboratory located in Bo. We used the RPM-Flu microarray designed for broad-range detection of human respiratory pathogens, to investigate a suspected outbreak of avian influenza in a number of poultry farms in which significant mortality of chickens was observed. The microarray results were additionally confirmed by influenza specific real-time PCR. The results of the study excluded the possibility that the outbreak was caused by influenza, but implicated Klebsiella pneumoniae as a possible pathogen. The outcome of this feasibility study confirms that application of broad-spectrum detection platforms for outbreak investigation in low-resource locations is possible and allows for rapid discovery of the responsible agents, even in cases when different agents are suspected. This strategy enables quick and cost effective detection of low probability events such as outbreak of a rare disease or intentional release of a biothreat agent.

Classifying seismic noise and sources from OBS data using unsupervised machine learning

NASA Astrophysics Data System (ADS)

Mosher, S. G.; Audet, P.

2017-12-01

The paradigm of plate tectonics was established mainly by recognizing the central role of oceanic plates in the production and destruction of tectonic plates at their boundaries. Since that realization, however, seismic studies of tectonic plates and their associated deformation have slowly shifted their attention toward continental plates due to the ease of installation and maintenance of high-quality seismic networks on land. The result has been a much more detailed understanding of the seismicity patterns associated with continental plate deformation in comparison with the low-magnitude deformation patterns within oceanic plates and at their boundaries. While the number of high-quality ocean-bottom seismometer (OBS) deployments within the past decade has demonstrated the potential to significantly increase our understanding of tectonic systems in oceanic settings, OBS data poses significant challenges to many of the traditional data processing techniques in seismology. In particular, problems involving the detection, location, and classification of seismic sources occurring within oceanic settings are much more difficult due to the extremely noisy seafloor environment in which data are recorded. However, classifying data without a priori constraints is a problem that is routinely pursued via unsupervised machine learning algorithms, which remain robust even in cases involving complicated datasets. In this research, we apply simple unsupervised machine learning algorithms (e.g., clustering) to OBS data from the Cascadia Initiative in an attempt to classify and detect a broad range of seismic sources, including various noise sources and tremor signals occurring within ocean settings.

Molecular detection of bovine coronavirus in a diarrhea outbreak in pasture-feeding Nellore steers in southern Brazil.

PubMed

Ribeiro, Juliane; Lorenzetti, Elis; Alfieri, Alice Fernandes; Alfieri, Amauri Alcindo

2016-03-01

Worldwide diarrhea outbreaks in cattle herds are more frequently detected in calves being that diarrhea outbreaks in adult cattle are not common. Winter dysentery (WD) is a bovine coronavirus (BCoV) enteric infection that is more reported in Northern hemisphere. Seasonal outbreaks of WD in adult cattle occur mainly in dairy cows. WD has not been described in beef cattle herds of tropical countries. This study describes the molecular detection of BCoV in a diarrhea outbreak in beef cattle steers (Nellore) raised on pasture in Parana, southern Brazil. During the outbreak, the farm had about 600 fattening steers. Watery and bloody diarrhea unresponsive to systemic broad-spectrum antibiotic therapy reveals a morbidity rate of approximately 15 %. The BCoV N gene was identified in 42.9 % (6/14) of the diarrheic fecal samples evaluated by semi-nested polymerase chain reaction (SN-PCR) technique. Other enteric microorganisms occasionally identified in adult cattle and evaluated in this study such as bovine groups A, B, and C rotavirus, bovine viral diarrhea virus, bovine torovirus, aichivirus B, and Eimeria sp. were not identified in the fecal samples. To the best knowledge of the authors, this is the first description of the BCoV diagnosis in fecal samples collected in a diarrhea outbreak in adult beef cattle grazing in the grass in a tropical country.

Burkholderia stabilis outbreak associated with contaminated commercially-available washing gloves, Switzerland, May 2015 to August 2016

PubMed Central

Sommerstein, Rami; Führer, Urs; Lo Priore, Elia; Casanova, Carlo; Meinel, Dominik M; Seth-Smith, Helena MB; Kronenberg, Andreas; Koch, Daniel; Senn, Laurence; Widmer, Andreas F; Egli, Adrian; Marschall, Jonas

2017-01-01

We describe an outbreak of Burkholderia stabilis associated with contaminated washing gloves, a commercially available Class I medical device. Triggered by an increase in Burkholderia cepacia complex (BCC) bacteremias and the detection of BCC in unopened packages of washing gloves, an ad hoc national outbreak committee comprising representatives of a public health organisation, a regulatory agency, and an expert association convened and commissioned an outbreak investigation. The investigation included retrospective case finding across Switzerland and whole genome sequencing (WGS) of isolates from cases and gloves. The investigation revealed that BCC were detected in clinical samples of 46 cases aged 17 to 91 years (33% females) from nine institutions between May 2015 and August 2016. Twenty-two isolates from case patients and 16 from washing gloves underwent WGS. All available outbreak isolates clustered within a span of < 19 differing alleles, while 13 unrelated clinical isolates differed by > 1,500 alleles. This BCC outbreak was rapidly identified, communicated, investigated and halted by an ad hoc collaboration of multiple stakeholders. WGS served as useful tool for confirming the source of the outbreak. This outbreak also highlights current regulatory limitations regarding Class I medical devices and the usefulness of a nationally coordinated outbreak response. PMID:29233255

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.

There's No Place Like Home: Crown-of-Thorns Outbreaks in the Central Pacific Are Regionally Derived and Independent Events

PubMed Central

Timmers, Molly A.; Bird, Christopher E.; Skillings, Derek J.; Smouse, Peter E.; Toonen, Robert J.

2012-01-01

One of the most significant biological disturbances on a tropical coral reef is a population outbreak of the fecund, corallivorous crown-of-thorns sea star, Acanthaster planci. Although the factors that trigger an initial outbreak may vary, successive outbreaks within and across regions are assumed to spread via the planktonic larvae released from a primary outbreak. This secondary outbreak hypothesis is predominantly based on the high dispersal potential of A. planci and the assertion that outbreak populations (a rogue subset of the larger population) are genetically more similar to each other than they are to low-density non-outbreak populations. Here we use molecular techniques to evaluate the spatial scale at which A. planci outbreaks can propagate via larval dispersal in the central Pacific Ocean by inferring the location and severity of gene flow restrictions from the analysis of mtDNA control region sequence (656 specimens, 17 non-outbreak and six outbreak locations, six archipelagos, and three regions). Substantial regional, archipelagic, and subarchipelagic-scale genetic structuring of A. planci populations indicate that larvae rarely realize their dispersal potential and outbreaks in the central Pacific do not spread across the expanses of open ocean. On a finer scale, genetic partitioning was detected within two of three islands with multiple sampling sites. The finest spatial structure was detected at Pearl & Hermes Atoll, between the lagoon and forereef habitats (<10 km). Despite using a genetic marker capable of revealing subtle partitioning, we found no evidence that outbreaks were a rogue genetic subset of a greater population. Overall, outbreaks that occur at similar times across population partitions are genetically independent and likely due to nutrient inputs and similar climatic and ecological conditions that conspire to fuel plankton blooms. PMID:22363570

GHOST: global hepatitis outbreak and surveillance technology.

PubMed

Longmire, Atkinson G; Sims, Seth; Rytsareva, Inna; Campo, David S; Skums, Pavel; Dimitrova, Zoya; Ramachandran, Sumathi; Medrzycki, Magdalena; Thai, Hong; Ganova-Raeva, Lilia; Lin, Yulin; Punkova, Lili T; Sue, Amanda; Mirabito, Massimo; Wang, Silver; Tracy, Robin; Bolet, Victor; Sukalac, Thom; Lynberg, Chris; Khudyakov, Yury

2017-12-06

Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections associated with unsafe injection practices, drug diversion, and other exposures to blood are difficult to detect and investigate. Effective HCV outbreak investigation requires comprehensive surveillance and robust case investigation. We previously developed and validated a methodology for the rapid and cost-effective identification of HCV transmission clusters. Global Hepatitis Outbreak and Surveillance Technology (GHOST) is a cloud-based system enabling users, regardless of computational expertise, to analyze and visualize transmission clusters in an independent, accurate and reproducible way. We present and explore performance of several GHOST implemented algorithms using next-generation sequencing data experimentally obtained from hypervariable region 1 of genetically related and unrelated HCV strains. GHOST processes data from an entire MiSeq run in approximately 3 h. A panel of seven specimens was used for preparation of six repeats of MiSeq libraries. Testing sequence data from these libraries by GHOST showed a consistent transmission linkage detection, testifying to high reproducibility of the system. Lack of linkage among genetically unrelated HCV strains and constant detection of genetic linkage between HCV strains from known transmission pairs and from follow-up specimens at different levels of MiSeq-read sampling indicate high specificity and sensitivity of GHOST in accurate detection of HCV transmission. GHOST enables automatic extraction of timely and relevant public health information suitable for guiding effective intervention measures. It is designed as a virtual diagnostic system intended for use in molecular surveillance and outbreak investigations rather than in research. The system produces accurate and reproducible information on HCV transmission clusters for all users, irrespective of their level of bioinformatics expertise. Improvement in molecular detection capacity will contribute to increasing the rate of transmission detection, thus providing opportunity for rapid, accurate and effective response to outbreaks of hepatitis C. Although GHOST was originally developed for hepatitis C surveillance, its modular structure is readily applicable to other infectious diseases. Worldwide availability of GHOST for the detection of HCV transmissions will foster deeper involvement of public health researchers and practitioners in hepatitis C outbreak investigation.

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

Estimating challenge load due to disease outbreaks and other challenges using reproduction records of sows.

PubMed

Mathur, P K; Herrero-Medrano, J M; Alexandri, P; Knol, E F; ten Napel, J; Rashidi, H; Mulder, H A

2014-12-01

A method was developed and tested to estimate challenge load due to disease outbreaks and other challenges in sows using reproduction records. The method was based on reproduction records from a farm with known disease outbreaks. It was assumed that the reduction in weekly reproductive output within a farm is proportional to the magnitude of the challenge. As the challenge increases beyond certain threshold, it is manifested as an outbreak. The reproduction records were divided into 3 datasets. The first dataset called the Training dataset consisted of 57,135 reproduction records from 10,901 sows from 1 farm in Canada with several outbreaks of porcine reproductive and respiratory syndrome (PRRS). The known disease status of sows was regressed on the traits number born alive, number of losses as a combination of still birth and mummified piglets, and number of weaned piglets. The regression coefficients from this analysis were then used as weighting factors for derivation of an index measure called challenge load indicator. These weighting factors were derived with i) a two-step approach using residuals or year-week solutions estimated from a previous step, and ii) a single-step approach using the trait values directly. Two types of models were used for each approach: a logistic regression model and a general additive model. The estimates of challenge load indicator were then compared based on their ability to detect PRRS outbreaks in a Test dataset consisting of records from 65,826 sows from 15 farms in the Netherlands. These farms differed from the Canadian farm with respect to PRRS virus strains, severity and frequency of outbreaks. The single-step approach using a general additive model was best and detected 14 out of the 15 outbreaks. This approach was then further validated using the third dataset consisting of reproduction records of 831,855 sows in 431 farms located in different countries in Europe and America. A total of 41 out of 48 outbreaks detected using data analysis were confirmed based on diagnostic information received from the farms. Among these, 30 outbreaks were due to PRRS while 11 were due to other diseases and challenging conditions. The results suggest that proposed method could be useful for estimation of challenge load and detection of challenge phases such as disease outbreaks.

[Meningitis outbreak caused by Echovirus serotype 30 in the Valencian Community].

PubMed

Juliá, M Lirios; Colomina, Javier; Domínguez, Victoria; Orta, Nieves; Guerrero, Antonio

2009-05-01

Aseptic meningitis can be caused by several agents, and in many cases the etiology remains unknown. The aim of this study to analyze the clinical and epidemiological characteristics of a meningitis outbreak detected in Health Department 11 of the Valencian Community (Spain). The study was performed in children hospitalized between November and December 2006 with meningitis symptoms, CSF pleocytosis, and negative CSF bacteriological culture. An epidemiological survey was conducted among cases and family members. Virus detection and phylogenetic analysis were performed with molecular biology techniques. The outbreak affected at least 44 children, with a mean age (standard deviation) of 5.5 years (2.9). The average hospital stay was 3.1 days and outcome was favorable in all cases. In 24 patients the CSF specimen sufficed for viral detection by PCR; enteroviruses ultimately serotyped as echovirus 30 were detected in 12 of them (50%). This serotype has been recently found in other parts of our country. Detection of echovirus 30 in CSF and the epidemiological presentation of cases enabled determination of the etiology of the outbreak. This finding coincided in time with other outbreaks of echovirus 30 in Spain, a fact that may explain the epidemic situation in the Valencian Community during 2006. Establishment of a national surveillance network for monitoring systemic enterovirus infection would provide data on the circulation patterns and identify new emerging serotypes.

Transmission patterns of smallpox: systematic review of natural outbreaks in Europe and North America since World War II.

PubMed

Bhatnagar, Vibha; Stoto, Michael A; Morton, Sally C; Boer, Rob; Bozzette, Samuel A

2006-05-05

Because smallpox (variola major) may be used as a biological weapon, we reviewed outbreaks in post-World War II Europe and North America in order to understand smallpox transmission patterns. A systematic review was used to identify papers from the National Library of Medicine, Embase, Biosis, Cochrane Library, Defense Technical Information Center, WorldCat, and reference lists of included publications. Two authors reviewed selected papers for smallpox outbreaks. 51 relevant outbreaks were identified from 1,389 publications. The median for the effective first generation reproduction rate (initial R) was 2 (range 0-38). The majority outbreaks were small (less than 5 cases) and contained within one generation. Outbreaks with few hospitalized patients had low initial R values (median of 1) and were prolonged if not initially recognized (median of 3 generations); outbreaks with mostly hospitalized patients had higher initial R values (median 12) and were shorter (median of 3 generations). Index cases with an atypical presentation of smallpox were less likely to have been diagnosed with smallpox; outbreaks in which the index case was not correctly diagnosed were larger (median of 27.5 cases) and longer (median of 3 generations) compared to outbreaks in which the index case was correctly diagnosed (median of 3 cases and 1 generation). Patterns of spread during Smallpox outbreaks varied with circumstances, but early detection and implementation of control measures is a most important influence on the magnitude of outbreaks. The majority of outbreaks studied in Europe and North America were controlled within a few generations if detected early.

Surveillance data for waterborne illness detection: an assessment following a massive waterborne outbreak of Cryptosporidium infection.

PubMed Central

Proctor, M. E.; Blair, K. A.; Davis, J. P.

1998-01-01

Following the 1993 Milwaukee cryptosporidiosis outbreak, we examined data from eight sources available during the time of the outbreak. Although there was a remarkable temporal correspondence of surveillance peaks, the most timely data involved use of systems in which personnel with existing close ties to public health programmes perceived the importance of providing information despite workload constraints associated with an outbreak. During the investigation, surveillance systems which could be easily linked with laboratory data, were flexible in adding new variables, and which demonstrated low baseline variability were most useful. Geographically fixed nursing home residents served as an ideal population with nonconfounded exposures. Use of surrogate measurements of morbidity can trigger worthwhile public health responses in advance of laboratory-confirmed diagnosis and help reduce total morbidity associated with an outbreak. This report describes the relative strengths and weaknesses of these surveillance methods for community-wide waterborne illness detection and their application in outbreak decision making. PMID:9528817

Author Detection on a Mobile Phone

DTIC Science & Technology

2011-03-01

handwriting , and to mine sales data for profitable trends. Two broad categories of machine learning are supervised learn- ing and unsupervised learning...evaluation,” AI 2006: Advances in Artificial Intelligence, p. 1015–1021, 2006. [23] “Gartner says worldwide mobile phone sales grew 17 per cent in first

Unsupervised malaria parasite detection based on phase spectrum.

PubMed

Fang, Yuming; Xiong, Wei; Lin, Weisi; Chen, Zhenzhong

2011-01-01

In this paper, we propose a novel method for malaria parasite detection based on phase spectrum. The method first obtains the amplitude spectrum and phase spectrum for blood smear images through Quaternion Fourier Transform (QFT). Then it gets the reconstructed image based on Inverse Quaternion Fourier transform (IQFT) on a constant amplitude spectrum and the original phase spectrum. The malaria parasite areas can be detected easily from the reconstructed blood smear images. Extensive experiments have demonstrated the effectiveness of this novel method.

Evaluation of outbreak detection performance using multi-stream syndromic surveillance for influenza-like illness in rural Hubei Province, China: a temporal simulation model based on healthcare-seeking behaviors.

PubMed

Fan, Yunzhou; Wang, Ying; Jiang, Hongbo; Yang, Wenwen; Yu, Miao; Yan, Weirong; Diwan, Vinod K; Xu, Biao; Dong, Hengjin; Palm, Lars; Nie, Shaofa

2014-01-01

Syndromic surveillance promotes the early detection of diseases outbreaks. Although syndromic surveillance has increased in developing countries, performance on outbreak detection, particularly in cases of multi-stream surveillance, has scarcely been evaluated in rural areas. This study introduces a temporal simulation model based on healthcare-seeking behaviors to evaluate the performance of multi-stream syndromic surveillance for influenza-like illness. Data were obtained in six towns of rural Hubei Province, China, from April 2012 to June 2013. A Susceptible-Exposed-Infectious-Recovered model generated 27 scenarios of simulated influenza A (H1N1) outbreaks, which were converted into corresponding simulated syndromic datasets through the healthcare-behaviors model. We then superimposed converted syndromic datasets onto the baselines obtained to create the testing datasets. Outbreak performance of single-stream surveillance of clinic visit, frequency of over the counter drug purchases, school absenteeism, and multi-stream surveillance of their combinations were evaluated using receiver operating characteristic curves and activity monitoring operation curves. In the six towns examined, clinic visit surveillance and school absenteeism surveillance exhibited superior performances of outbreak detection than over the counter drug purchase frequency surveillance; the performance of multi-stream surveillance was preferable to signal-stream surveillance, particularly at low specificity (Sp <90%). The temporal simulation model based on healthcare-seeking behaviors offers an accessible method for evaluating the performance of multi-stream surveillance.

Label-Free Detection and Serotyping of Salmonellae by Surface Enhanced Raman Spectroscopy with Immunomagnetic Separation

USDA-ARS?s Scientific Manuscript database

Salmonella spp. are one of the leading causes of foodborne outbreaks in the United States and globally. Current detection and characterization techniques for Salmonella are time consuming and rapid methods could greatly benefit outbreak investigation, new case prevention and disease treatment. In th...

Plasmodium falciparum Malaria, Southern Algeria, 2007

PubMed Central

Gassen, Ibrahim; Khechache, Yacine; Lamali, Karima; Tchicha, Boualem; Brengues, Cécile; Menegon, Michela; Severini, Carlo; Fontenille, Didier; Harrat, Zoubir

2010-01-01

An outbreak of Plasmodium falciparum malaria occurred in Tinzaouatine in southern Algeria in 2007. The likely vector, Anopheles gambiae mosquitoes, had not been detected in Algeria. Genes for resistance to chloroquine were detected in the parasite. The outbreak shows the potential for an increase in malaria vectors in Algeria. PMID:20113565

Simultaneous detection and serotyping of Salmonellae by immunomagnetic separation and label-free surface enhanced Raman spectroscopy

USDA-ARS?s Scientific Manuscript database

Salmonella spp. are one of the leading causes of foodborne outbreaks in the United States and globally. Current detection and characterization techniques for Salmonellae are time consuming and costly, and rapid methods could greatly benefit outbreak investigation, new case prevention and disease tre...

Cheese-related listeriosis outbreak, Portugal, March 2009 to February 2012.

PubMed

Magalhaes, R; Almeida, G; Ferreira, V; Santos, I; Silva, J; Mendes, M M; Pita, J; Mariano, G; Mancio, I; Sousa, M M; Farber, J; Pagotto, F; Teixeira, P

2015-04-30

In Portugal, listeriosis has been notifiable since April 2014, but there is no active surveillance programme for the disease. A retrospective study involving 25 national hospitals led to the detection of an outbreak that occurred between March 2009 and February 2012. The amount of time between the start of the outbreak and its detection was 16 months. Of the 30 cases of listeriosis reported, 27 were in the Lisbon and Vale do Tejo region. Two cases were maternal/neonatal infections and one resulted in fetal loss. The mean age of the non-maternal/neonatal cases was 59 years (standard deviation: 17); 13 cases were more than 65 years old. The case fatality rate was 36.7%. All cases were caused by molecular serogroup IVb isolates indistinguishable by pulsed-field gel electrophoresis and ribotype profiles. Collaborative investigations with the national health and food safety authorities identified cheese as the probable source of infection, traced to a processing plant. The magnitude of this outbreak, the first reported food-borne listeriosis outbreak in Portugal, highlights the importance of having an effective listeriosis surveillance system in place for early detection and resolution of outbreaks, as well as the need for a process for the prompt submission of Listeria monocytogenes isolates for routine laboratory typing.

Long-Term Dynamics of Bluetongue Virus in Wild Ruminants: Relationship with Outbreaks in Livestock in Spain, 2006-2011

PubMed Central

Lorca-Oró, Cristina; López-Olvera, Jorge Ramón; Ruiz-Fons, Francisco; Acevedo, Pelayo; García-Bocanegra, Ignacio; Oleaga, Álvaro; Gortázar, Christian; Pujols, Joan

2014-01-01

Wild and domestic ruminants are susceptible to Bluetongue virus (BTV) infection. Three BTV serotypes (BTV-4, BTV-1 and BTV-8) have been detected in Spain in the last decade. Even though control strategies have been applied to livestock, BTV circulation has been frequently detected in wild ruminant populations in Spain. The aim of the present study is to assess the role for wild ruminants in maintaining BTV after the vaccination programs in livestock in mainland Spain. A total of 931 out 1,914 (48.6%) serum samples, collected from eight different wild ruminant species between 2006 and 2011, were BTV positive by ELISA. In order to detect specific antibodies against BTV-1, BTV-4 and BTV-8, positive sera were also tested by serumneutralisation test (SNT). From the ELISA positive samples that could be tested by SNT (687 out of 931), 292 (42.5%) showed neutralising antibodies against one or two BTV serotypes. For each BTV seroptype, the number of outbreaks in livestock (11,857 outbreaks in total) was modelled with pure autoregressive models and the resulting smoothed values, representing the predicted number of BTV outbreaks in livestock at municipality level, were positively correlated with BTV persistence in wild species. The strength of this relationship significantly decreased as red deer (Cervus elaphus) population abundance increased. In addition, BTV RNA was detected by real time RT-PCR in 32 out of 311 (10.3%) spleen samples from seropositive animals. Although BT outbreaks in livestock have decreased substantially after vaccination campaigns, our results indicated that wild ruminants have been exposed to BTV in territories where outbreaks in domestic animals occurred. The detection of BTV RNA and spatial association between BT outbreaks in livestock and BTV rates in red deer are consistent with the hypothesis of virus circulation and BTV maintenance within Iberian wild ruminant populations. PMID:24940879

Data Mining for Anomaly Detection

NASA Technical Reports Server (NTRS)

Biswas, Gautam; Mack, Daniel; Mylaraswamy, Dinkar; Bharadwaj, Raj

2013-01-01

The Vehicle Integrated Prognostics Reasoner (VIPR) program describes methods for enhanced diagnostics as well as a prognostic extension to current state of art Aircraft Diagnostic and Maintenance System (ADMS). VIPR introduced a new anomaly detection function for discovering previously undetected and undocumented situations, where there are clear deviations from nominal behavior. Once a baseline (nominal model of operations) is established, the detection and analysis is split between on-aircraft outlier generation and off-aircraft expert analysis to characterize and classify events that may not have been anticipated by individual system providers. Offline expert analysis is supported by data curation and data mining algorithms that can be applied in the contexts of supervised learning methods and unsupervised learning. In this report, we discuss efficient methods to implement the Kolmogorov complexity measure using compression algorithms, and run a systematic empirical analysis to determine the best compression measure. Our experiments established that the combination of the DZIP compression algorithm and CiDM distance measure provides the best results for capturing relevant properties of time series data encountered in aircraft operations. This combination was used as the basis for developing an unsupervised learning algorithm to define "nominal" flight segments using historical flight segments.

Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models.

PubMed

Beltrame, Thomas; Amelard, Robert; Wong, Alexander; Hughson, Richard L

2018-02-01

Physical activity levels are related through algorithms to the energetic demand, with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V̇o 2 ) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., V̇o 2 kinetics). This study evaluated aerobic system dynamics based on predicted V̇o 2 data obtained from wearable sensors during unsupervised activities of daily living (μADL). Thirteen healthy men performed a laboratory-controlled moderate exercise protocol and were monitored for ≈6 h/day for 4 days (μADL data). Variables derived from hip accelerometer (ACC HIP ), heart rate monitor, and respiratory bands during μADL were extracted and processed by a validated random forest regression model to predict V̇o 2 . The aerobic system analysis was based on the frequency-domain analysis of ACC HIP and predicted V̇o 2 data obtained during μADL. Optimal samples for frequency domain analysis (constrained to ≤0.01 Hz) were selected when ACC HIP was higher than 0.05 g at a given frequency (i.e., participants were active). The temporal characteristics of predicted V̇o 2 data during μADL correlated with the temporal characteristics of measured V̇o 2 data during laboratory-controlled protocol ([Formula: see text] = 0.82, P < 0.001, n = 13). In conclusion, aerobic system dynamics can be investigated during unsupervised activities of daily living by wearable sensors. Although speculative, these algorithms have the potential to be incorporated into wearable systems for early detection of changes in health status in realistic environments by detecting changes in aerobic response dynamics. NEW & NOTEWORTHY The early detection of subclinical aerobic system impairments might be indicative of impaired physiological reserves that impact the capacity for physical activity. This study is the first to use wearable sensors in unsupervised activities of daily living in combination with novel machine learning algorithms to investigate the aerobic system dynamics with the potential to contribute to models of functional health status and guide future individualized health care in the normal population.

A Space–Time Permutation Scan Statistic for Disease Outbreak Detection

PubMed Central

Kulldorff, Martin; Heffernan, Richard; Hartman, Jessica; Assunção, Renato; Mostashari, Farzad

2005-01-01

Background The ability to detect disease outbreaks early is important in order to minimize morbidity and mortality through timely implementation of disease prevention and control measures. Many national, state, and local health departments are launching disease surveillance systems with daily analyses of hospital emergency department visits, ambulance dispatch calls, or pharmacy sales for which population-at-risk information is unavailable or irrelevant. Methods and Findings We propose a prospective space–time permutation scan statistic for the early detection of disease outbreaks that uses only case numbers, with no need for population-at-risk data. It makes minimal assumptions about the time, geographical location, or size of the outbreak, and it adjusts for natural purely spatial and purely temporal variation. The new method was evaluated using daily analyses of hospital emergency department visits in New York City. Four of the five strongest signals were likely local precursors to citywide outbreaks due to rotavirus, norovirus, and influenza. The number of false signals was at most modest. Conclusion If such results hold up over longer study times and in other locations, the space–time permutation scan statistic will be an important tool for local and national health departments that are setting up early disease detection surveillance systems. PMID:15719066

Detection and molecular characterization of norovirus from oysters implicated in outbreaks in the US.

PubMed

Woods, Jacquelina W; Calci, Kevin R; Marchant-Tambone, Joey G; Burkhardt, William

2016-10-01

Human noroviruses are the leading cause of non-bacterial shellfish associated gastroenteritis. Here we report on the detection and characterization of norovirus (NoV) in shellfish associated outbreaks. Requests were received from state and federal officials for technical assistance in the analysis of shellfish for NoV and male specific coliphage (MSC; an enteric virus surrogate) during the years 2009 thru 2014. In outbreaks where NoV was detected, genogroup II (GII) levels ranged from 2.4 to 82.0 RT-qPCR U/g of digestive diverticula (DD) while NoV genogroup I (GI) levels ranged from 1.5 to 29.8 RT-qPCR U/g of DD. Murine norovirus extraction efficiencies ranged between 50 and 85%. MSC levels ranged from <6 to 80 PFU/100 g. Phylogenetic analysis of the outbreak sequences revealed strains clustering with GI.8, GI.4, GII.3, GII.4, GII.7, and GII.21. There was 100% homology between the shellfish and clinical strains occurring in 2 of 8 outbreaks. Known shellfish consumption data demonstrated probable infectious particles ingested as low as 12. These investigations demonstrate effective detection, quantification, and characterization of NoV in shellfish associated with illness. Published by Elsevier Ltd.

Diagnostic Evasion of Highly-Resistant Microorganisms: A Critical Factor in Nosocomial Outbreaks.

PubMed

Zhou, Xuewei; Friedrich, Alexander W; Bathoorn, Erik

2017-01-01

Highly resistant microorganisms (HRMOs) may evade screening strategies used in routine diagnostics. Bacteria that have evolved to evade diagnostic tests may have a selective advantage in the nosocomial environment. Evasion of resistance detection can result from the following mechanisms: low-level expression of resistance genes not resulting in detectable resistance, slow growing variants, mimicry of wild-type-resistance, and resistance mechanisms that are only detected if induced by antibiotic pressure. We reviewed reports on hospital outbreaks in the Netherlands over the past 5 years. Remarkably, many outbreaks including major nation-wide outbreaks were caused by microorganisms able to evade resistance detection by diagnostic screening tests. We describe various examples of diagnostic evasion by several HRMOs and discuss this in a broad and international perspective. The epidemiology of hospital-associated bacteria may strongly be affected by diagnostic screening strategies. This may result in an increasing reservoir of resistance genes in hospital populations that is unnoticed. The resistance elements may horizontally transfer to hosts with systems for high-level expression, resulting in a clinically significant resistance problem. We advise to communicate the identification of HRMOs that evade diagnostics within national and regional networks. Such signaling networks may prevent inter-hospital outbreaks, and allow collaborative development of adapted diagnostic tests.

Time delays in the response to the Neisseria meningitidis serogroup C outbreak in Nigeria - 2017.

PubMed

Hassan, Assad; Mustapha, G U; Lawal, Bola B; Na'uzo, Aliyu M; Ismail, Raji; Womi-Eteng Oboma, Eteng; Oyebanji, Oyeronke; Agenyi, Jeremiah; Thomas, Chima; Balogun, Muhammad Shakir; Dalhat, Mahmood M; Nguku, Patrick; Ihekweazu, Chikwe

2018-01-01

Nigeria reports high rates of mortality linked with recurring meningococcal meningitis outbreaks within the African meningitis belt. Few studies have thoroughly described the response to these outbreaks to provide strong and actionable public health messages. We describe how time delays affected the response to the 2016/2017 meningococcal meningitis outbreak in Nigeria. Using data from Nigeria Centre for Disease Control (NCDC), National Primary Health Care Development Agency (NPHCDA), World Health Organisation (WHO), and situation reports of rapid response teams, we calculated attack and death rates of reported suspected meningococcal meningitis cases per week in Zamfara, Sokoto and Yobe states respectively, between epidemiological week 49 in 2016 and epidemiological week 25 in 2017. We identified when alert and epidemic thresholds were crossed and determined when the outbreak was detected and notified in each state. We examined response activities to the outbreak. There were 12,535 suspected meningococcal meningitis cases and 877 deaths (CFR: 7.0%) in the three states. It took an average time of three weeks before the outbreaks were detected and notified to NCDC. Four weeks after receiving notification, an integrated response coordinating centre was set up by NCDC and requests for vaccines were sent to International Coordinating Group (ICG) on vaccine provision. While it took ICG one week to approve the requests, it took an average of two weeks for approximately 41% of requested vaccines to arrive. On the average, it took nine weeks from the date the epidemic threshold was crossed to commencement of reactive vaccination in the three states. There were delays in detection and notification of the outbreak, in coordinating response activities, in requesting for vaccines and their arrival from ICG, and in initiating reactive vaccination. Reducing these delays in future outbreaks could help decrease the morbidity and mortality linked with meningococcal meningitis outbreaks.

Derivation of Tissue-specific Functional Gene Sets to Aid Transcriptomic Analysis of Chemical Impacts on the Teleost Reproductive Axis.

EPA Science Inventory

Oligonucleotide microarrays are a powerful tool for unsupervised analysis of chemical impacts on biological systems. However, the lack of well annotated biological pathways for many aquatic organisms, including fish, and the poor power of microarray-based analyses to detect diffe...

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.

Nosocomial transmission of respiratory syncytial virus in an outpatient cancer center.

PubMed

Chu, Helen Y; Englund, Janet A; Podczervinski, Sara; Kuypers, Jane; Campbell, Angela P; Boeckh, Michael; Pergam, Steven A; Casper, Corey

2014-06-01

Respiratory syncytial virus (RSV) outbreaks in inpatient settings are associated with poor outcomes in cancer patients. The use of molecular epidemiology to document RSV transmission in the outpatient setting has not been well described. We performed a retrospective cohort study of 2 nosocomial outbreaks of RSV at the Seattle Cancer Care Alliance. Subjects included patients seen at the Seattle Cancer Care Alliance with RSV detected in 2 outbreaks in 2007-2008 and 2012 and all employees with respiratory viruses detected in the 2007-2008 outbreak. A subset of samples was sequenced using semi-nested PCR targeting the RSV attachment glycoprotein coding region. Fifty-one cases of RSV were identified in 2007-2008. Clustering of identical viral strains was detected in 10 of 15 patients (67%) with RSV sequenced from 2007 to 2008. As part of a multimodal infection control strategy implemented as a response to the outbreak, symptomatic employees had nasal washes collected. Of 254 employee samples, 91 (34%) tested positive for a respiratory virus, including 14 with RSV. In another RSV outbreak in 2012, 24 cases of RSV were identified; 9 of 10 patients (90%) had the same viral strain, and 1 (10%) had another viral strain. We document spread of clonal strains within an outpatient cancer care setting. Infection control interventions should be implemented in outpatient, as well as inpatient, settings to reduce person-to-person transmission and limit progression of RSV outbreaks. Copyright © 2014 American Society for Blood and Marrow Transplantation. All rights reserved.

Nosocomial Transmission of Respiratory Syncytial Virus in an Outpatient Cancer Center

PubMed Central

Chu, Helen Y.; Englund, Janet A.; Podczervinski, Sara; Kuypers, Jane; Campbell, Angela P.; Boeckh, Michael; Pergam, Steven A.; Casper, Crey

2014-01-01

Background Respiratory syncytial virus (RSV) outbreaks in inpatient settings are associated with poor outcomes in cancer patients. The use of molecular epidemiology to document RSV transmission in the outpatient setting has not been well described. Methods We performed a retrospective cohort study of two nosocomial outbreaks of RSV at the Seattle Cancer Care Alliance (SCCA). Subjects included patients seen at the SCCA with RSV detected in two outbreaks in 2007-2008 and 2012, and all employees with respiratory viruses detected in the 2007-2008 outbreak. A subset of samples was sequenced using semi-nested polymerase chain reaction targeting the RSV attachment glycoprotein coding region. Results Fifty-one cases of RSV were identified in 2007-2008. Clustering of identical viral strains was detected in 10 (67%) of 15 patients with RSV sequenced from 2007-2008. As part of a multimodal infection control strategy implemented as a response to the outbreak, symptomatic employees had nasal washes collected. Of 254 employee samples, 91 (34%) tested positive for a respiratory virus, including 14 with RSV. In another RSV outbreak in 2012, 24 cases of RSV were identified; nine (90%) of 10 patients had the same viral strain, and 1 (10%) had another viral strain. Conclusions We document spread of clonal strains within an outpatient cancer care setting. Infection control interventions should be implemented in outpatient, as well as inpatient, settings to reduce person-to-person transmission and limit progression of RSV outbreaks. PMID:24607551

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 first mile: community experience of outbreak control during an Ebola outbreak in Luwero District, Uganda.

PubMed

de Vries, Daniel H; Rwemisisi, Jude T; Musinguzi, Laban K; Benoni, Turinawe E; Muhangi, Denis; de Groot, Marije; Kaawa-Mafigiri, David; Pool, Robert

2016-02-16

A major challenge to outbreak control lies in early detection of viral haemorrhagic fevers (VHFs) in local community contexts during the critical initial stages of an epidemic, when risk of spreading is its highest ("the first mile"). In this paper we document how a major Ebola outbreak control effort in central Uganda in 2012 was experienced from the perspective of the community. We ask to what extent the community became a resource for early detection, and identify problems encountered with community health worker and social mobilization strategies. Analysis is based on first-hand ethnographic data from the center of a small Ebola outbreak in Luwero Country, Uganda, in 2012. Three of this paper's authors were engaged in an 18 month period of fieldwork on community health resources when the outbreak occurred. In total, 13 respondents from the outbreak site were interviewed, along with 21 key informants and 61 focus group respondents from nearby Kaguugo Parish. All informants were chosen through non-probability sampling sampling. Our data illustrate the lack of credibility, from an emic perspective, of biomedical explanations which ignore local understandings. These explanations were undermined by an insensitivity to local culture, a mismatch between information circulated and the local interpretative framework, and the inability of the emergency response team to take the time needed to listen and empathize with community needs. Stigmatization of the local community--in particular its belief in amayembe spirits--fuelled historical distrust of the external health system and engendered community-level resistance to early detection. Given the available anthropological knowledge of a previous outbreak in Northern Uganda, it is surprising that so little serious effort was made this time round to take local sensibilities and culture into account. The "first mile" problem is not only a question of using local resources for early detection, but also of making use of the contextual cultural knowledge that has already been collected and is readily available. Despite remarkable technological innovations, outbreak control remains contingent upon human interaction and openness to cultural difference.

A Participatory System for Preventing Pandemics of Animal Origins: Pilot Study of the Participatory One Health Disease Detection (PODD) System.

PubMed

Yano, Terdsak; Phornwisetsirikun, Somphorn; Susumpow, Patipat; Visrutaratna, Surasing; Chanachai, Karoon; Phetra, Polawat; Chaisowwong, Warangkhana; Trakarnsirinont, Pairat; Hemwan, Phonpat; Kaewpinta, Boontuan; Singhapreecha, Charuk; Kreausukon, Khwanchai; Charoenpanyanet, Arisara; Robert, Chongchit Sripun; Robert, Lamar; Rodtian, Pranee; Mahasing, Suteerat; Laiya, Ekkachai; Pattamakaew, Sakulrat; Tankitiyanon, Taweesart; Sansamur, Chalutwan; Srikitjakarn, Lertrak

2018-03-21

Aiming for early disease detection and prompt outbreak control, digital technology with a participatory One Health approach was used to create a novel disease surveillance system called Participatory One Health Disease Detection (PODD). PODD is a community-owned surveillance system that collects data from volunteer reporters; identifies disease outbreak automatically; and notifies the local governments (LGs), surrounding villages, and relevant authorities. This system provides a direct and immediate benefit to the communities by empowering them to protect themselves. The objective of this study was to determine the effectiveness of the PODD system for the rapid detection and control of disease outbreaks. The system was piloted in 74 LGs in Chiang Mai, Thailand, with the participation of 296 volunteer reporters. The volunteers and LGs were key participants in the piloting of the PODD system. Volunteers monitored animal and human diseases, as well as environmental problems, in their communities and reported these events via the PODD mobile phone app. LGs were responsible for outbreak control and provided support to the volunteers. Outcome mapping was used to evaluate the performance of the LGs and volunteers. LGs were categorized into one of the 3 groups based on performance: A (good), B (fair), and C (poor), with the majority (46%,34/74) categorized into group B. Volunteers were similarly categorized into 4 performance groups (A-D), again with group A showing the best performance, with the majority categorized into groups B and C. After 16 months of implementation, 1029 abnormal events had been reported and confirmed to be true reports. The majority of abnormal reports were sick or dead animals (404/1029, 39.26%), followed by zoonoses and other human diseases (129/1029, 12.54%). Many potentially devastating animal disease outbreaks were detected and successfully controlled, including 26 chicken high mortality outbreaks, 4 cattle disease outbreaks, 3 pig disease outbreaks, and 3 fish disease outbreaks. In all cases, the communities and animal authorities cooperated to apply community contingency plans to control these outbreaks, and community volunteers continued to monitor the abnormal events for 3 weeks after each outbreak was controlled. By design, PODD initially targeted only animal diseases that potentially could emerge into human pandemics (eg, avian influenza) and then, in response to community needs, expanded to cover human health and environmental health issues. ©Terdsak Yano, Somphorn Phornwisetsirikun, Patipat Susumpow, Surasing Visrutaratna, Karoon Chanachai, Polawat Phetra, Warangkhana Chaisowwong, Pairat Trakarnsirinont, Phonpat Hemwan, Boontuan Kaewpinta, Charuk Singhapreecha, Khwanchai Kreausukon, Arisara  Charoenpanyanet, Chongchit Sripun Robert, Lamar Robert, Pranee Rodtian, Suteerat Mahasing, Ekkachai Laiya, Sakulrat Pattamakaew, Taweesart Tankitiyanon, Chalutwan Sansamur, Lertrak Srikitjakarn. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 21.03.2018.

A single Legionella pneumophila genotype in the freshwater system in a ship experiencing three separate outbreaks of legionellosis in 6 years.

PubMed

Ahlen, Catrine; Aas, Marianne; Krusnell, Jadwiga; Iversen, Ole-Jan

2016-01-01

Recurrent legionella outbreaks at one and the same location are common. We have identified a single Legionella pneumophila genotype associated with recurrent Legionella outbreaks over 6 years. Field emergency surveys following Legionella outbreaks were performed on a vessel in 2008, 2009 and 2013. Water samples from both the distribution and technical parts of the potable water system were analyzed with respect to L. pneumophila [Real-Time PCR, cultivation, serotyping and genotyping (PFGE)] and free-living amoebae, (FLA). Legionella pneumophila serogroup 1 was present in the ship's potable water system during every outbreak. Genotyping of the 2008 survey material showed two separate PFGE genotypes while those in 2009 and 2013 demonstrated the presence of only one of the two genotypes. FLA with intracellular L. pneumophila of the same genotype were also detected. Analyses of the freshwater system on a ship following three separate Legionella outbreaks, for L. pneumophila and FLAs, revealed a single L. pneumophila genotype and FLA (Hartmanella). It is reasonable to assume that the L. pneumophila genotype detected in the freshwater system was the causal agent in the outbreaks onboard. Persistence of an apparently low-pathogenic L. pneumophila genotype and FLA in a potable water system represent a potential risk for recurrent outbreaks.

Two consecutive nationwide outbreaks of Listeriosis in France, October 1999-February 2000.

PubMed

de Valk, H; Vaillant, V; Jacquet, C; Rocourt, J; Le Querrec, F; Stainer, F; Quelquejeu, N; Pierre, O; Pierre, V; Desenclos, J C; Goulet, V

2001-11-15

In France, listeriosis surveillance is based on mandatory notification of all culture-confirmed cases, with systematic typing of isolates and routine collection of the patient's food history. From October 1999 to March 2000, two outbreaks of listeriosis were detected through this enhanced surveillance system. In outbreak 1, analysis of the food histories of cases suggested brand X "rillettes," a pâté-like meat product, as the vehicle of infection, and the outbreak strain of Listeria monocytogenes was subsequently isolated from the incriminated rillettes. In outbreak 2, a case-control study showed that consumption of jellied pork tongue was strongly associated with infection with the outbreak strain (odds ratio = 75.5, 95% confidence interval: 4.7, 1,216.0). However, trace-back results did not permit incrimination of any particular manufacturer of jellied pork tongue, and the outbreak strain was not isolated from the incriminated food or from any production sites. Consumption of jellied pork tongue was discouraged on epidemiologic evidence alone. The consecutive occurrence of these two outbreaks confirms the epidemic potential of listeriosis, even in a context of decreasing incidence, and underlines the importance of timely case-reporting and systematic typing of human L. monocytogenes strains to allow early detection and separate investigation of different clusters.

Estimating the effectiveness of early control measures through school absenteeism surveillance in observed outbreaks at rural schools in Hubei, China.

PubMed

Fan, Yunzhou; Yang, Mei; Jiang, Hongbo; Wang, Ying; Yang, Wenwen; Zhang, Zhixia; Yan, Weirong; Diwan, Vinod K; Xu, Biao; Dong, Hengjin; Palm, Lars; Liu, Li; Nie, Shaofa

2014-01-01

School absenteeism is a common data source in syndromic surveillance, which allows for the detection of outbreaks at an early stage. Previous studies focused on its correlation with other data sources. In this study, we evaluated the effectiveness of control measures based on early warning signals from school absenteeism surveillance in rural Chinese schools. A school absenteeism surveillance system was established in all 17 primary schools in 3 adjacent towns in the Chinese region of Hubei. Three outbreaks (varicella, mumps, and influenza-like illness) were detected and controlled successfully from April 1, 2012, to January 15, 2014. An impulse susceptible-exposed-infectious-recovered model was used to fit the epidemics of these three outbreaks. Moreover, it simulated the potential epidemics under interventions resulting from traditional surveillance signals. The effectiveness of the absenteeism-based control measures was evaluated by comparing the simulated datasets. The school absenteeism system generated 52 signals. Three outbreaks were verified through epidemiological investigation. Compared to traditional surveillance, the school absenteeism system generated simultaneous signals for the varicella outbreak, but 3 days in advance for the mumps outbreak and 2-4 days in advance for the influenza-like illness outbreak. The estimated excess protection rates of control measures based on early signals were 0.0%, 19.0-44.1%, and 29.0-37.0% for the three outbreaks, respectively. Although not all outbreak control measures can benefit from early signals through school absenteeism surveillance, the effectiveness of early signal-based interventions is obvious. School absenteeism surveillance plays an important role in reducing outbreak spread.

[The application of the prospective space-time statistic in early warning of infectious disease].

PubMed

Yin, Fei; Li, Xiao-Song; Feng, Zi-Jian; Ma, Jia-Qi

2007-06-01

To investigate the application of prospective space-time scan statistic in the early stage of detecting infectious disease outbreaks. The prospective space-time scan statistic was tested by mimicking daily prospective analyses of bacillary dysentery data of Chengdu city in 2005 (3212 cases in 102 towns and villages). And the results were compared with that of purely temporal scan statistic. The prospective space-time scan statistic could give specific messages both in spatial and temporal. The results of June indicated that the prospective space-time scan statistic could timely detect the outbreaks that started from the local site, and the early warning message was powerful (P = 0.007). When the merely temporal scan statistic for detecting the outbreak was sent two days later, and the signal was less powerful (P = 0.039). The prospective space-time scan statistic could make full use of the spatial and temporal information in infectious disease data and could timely and effectively detect the outbreaks that start from the local sites. The prospective space-time scan statistic could be an important tool for local and national CDC to set up early detection surveillance systems.

Unsupervised Sequential Outlier Detection With Deep Architectures.

PubMed

Lu, Weining; Cheng, Yu; Xiao, Cao; Chang, Shiyu; Huang, Shuai; Liang, Bin; Huang, Thomas

2017-09-01

Unsupervised outlier detection is a vital task and has high impact on a wide variety of applications domains, such as image analysis and video surveillance. It also gains long-standing attentions and has been extensively studied in multiple research areas. Detecting and taking action on outliers as quickly as possible are imperative in order to protect network and related stakeholders or to maintain the reliability of critical systems. However, outlier detection is difficult due to the one class nature and challenges in feature construction. Sequential anomaly detection is even harder with more challenges from temporal correlation in data, as well as the presence of noise and high dimensionality. In this paper, we introduce a novel deep structured framework to solve the challenging sequential outlier detection problem. We use autoencoder models to capture the intrinsic difference between outliers and normal instances and integrate the models to recurrent neural networks that allow the learning to make use of previous context as well as make the learners more robust to warp along the time axis. Furthermore, we propose to use a layerwise training procedure, which significantly simplifies the training procedure and hence helps achieve efficient and scalable training. In addition, we investigate a fine-tuning step to update all parameters set by incorporating the temporal correlation in the sequence. We further apply our proposed models to conduct systematic experiments on five real-world benchmark data sets. Experimental results demonstrate the effectiveness of our model, compared with other state-of-the-art approaches.

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

A novel unsupervised spike sorting algorithm for intracranial EEG.

PubMed

Yadav, R; Shah, A K; Loeb, J A; Swamy, M N S; Agarwal, R

2011-01-01

This paper presents a novel, unsupervised spike classification algorithm for intracranial EEG. The method combines template matching and principal component analysis (PCA) for building a dynamic patient-specific codebook without a priori knowledge of the spike waveforms. The problem of misclassification due to overlapping classes is resolved by identifying similar classes in the codebook using hierarchical clustering. Cluster quality is visually assessed by projecting inter- and intra-clusters onto a 3D plot. Intracranial EEG from 5 patients was utilized to optimize the algorithm. The resulting codebook retains 82.1% of the detected spikes in non-overlapping and disjoint clusters. Initial results suggest a definite role of this method for both rapid review and quantitation of interictal spikes that could enhance both clinical treatment and research studies on epileptic patients.

Outbreak of acute respiratory disease caused by human adenovirus type 7 in a military training camp in Shaanxi, China.

PubMed

Yu, Pengbo; Ma, Chaofeng; Nawaz, Muhammad; Han, Lei; Zhang, Jianfang; Du, Quanli; Zhang, Lixia; Feng, Qunling; Wang, Jingjun; Xu, Jiru

2013-08-01

Outbreaks of ARD associated with HAdV have been reported in military populations in many countries. Here, we report an ARD outbreak caused by HAdV-7 in a military training camp in Shaanxi Province, China, from February to March of 2012. Epidemic data and samples from the patients were collected, and viral nucleotides from samples and viral isolations were detected and sequenced. IgG and IgA antibodies against HAdV, and the neutralization antibodies against the viral strain isolated in this outbreak, were detected. Epidemiological study showed that all personnel affected were males with an average age of 19.1 years. Two peaks appeared on the epicurve and there was an 8-day interval between peaks. Laboratory results of viral nucleotide detection carried out with clinical specimens were positive for HAdV (83.33%, 15/18). Further study through serum antibody assay, virus isolation and phylogenetic analysis showed that HAdV-7 was the etiological agent responsible for the outbreak. IgA antibody began to appear on the 4th day after the onset and showed 100% positivity on the 8th day. The virus strain in the present outbreak was highly similar to the virus isolated in Hanzhong Shaanxi in 2009. We conclude that HAdV-7 was the pathogen corresponding to the outbreak, and this is the first report of an ARD outbreak caused by HAdV-7 in military persons in China. Vaccine development, as well as enhanced epidemiological and virological surveillance of HAdV infections in China should be emphasized. © 2013 The Societies and Wiley Publishing Asia Pty Ltd.

Near-real-time processing of a ceilometer network assisted with sun-photometer data: monitoring a dust outbreak over the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Cazorla, Alberto; Andrés Casquero-Vera, Juan; Román, Roberto; Guerrero-Rascado, Juan Luis; Toledano, Carlos; Cachorro, Victoria E.; Orza, José Antonio G.; Cancillo, María Luisa; Serrano, Antonio; Titos, Gloria; Pandolfi, Marco; Alastuey, Andres; Hanrieder, Natalie; Alados-Arboledas, Lucas

2017-10-01

The interest in the use of ceilometers for optical aerosol characterization has increased in the last few years. They operate continuously almost unattended and are also much less expensive than lidars; hence, they can be distributed in dense networks over large areas. However, due to the low signal-to-noise ratio it is not always possible to obtain particle backscatter coefficient profiles, and the vast number of data generated require an automated and unsupervised method that ensures the quality of the profiles inversions. In this work we describe a method that uses aerosol optical depth (AOD) measurements from the AERONET network that it is applied for the calibration and automated quality assurance of inversion of ceilometer profiles. The method is compared with independent inversions obtained by co-located multiwavelength lidar measurements. A difference smaller than 15 % in backscatter is found between both instruments. This method is continuously and automatically applied to the Iberian Ceilometer Network (ICENET) and a case example during an unusually intense dust outbreak affecting the Iberian Peninsula between 20 and 24 February 2016 is shown. Results reveal that it is possible to obtain quantitative optical aerosol properties (particle backscatter coefficient) and discriminate the quality of these retrievals with ceilometers over large areas. This information has a great potential for alert systems and model assimilation and evaluation.

Implementation of Nationwide Real-time Whole-genome Sequencing to Enhance Listeriosis Outbreak Detection and Investigation.

PubMed

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

2016-08-01

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. Published by Oxford University Press for the Infectious Diseases Society of America 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Effects of gypsy moth outbreaks on North American woodpeckers

Treesearch

Walter D. Koenig; Eric L. Walters; Andrew M. Liebhold

2011-01-01

We examined the effects of the introduced gypsy moth (Lymantria dispar) on seven species of North American woodpeckers by matching spatially explicit data on gypsy moth outbreaks with data on breeding and wintering populations. In general, we detected modest effects during outbreaks: during the breeding season one species, the Red-headed Woodpecker...

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...

Characterization of an outbreak of Clostridium perfringens food poisoning by quantitative fecal culture and fecal enterotoxin measurement.

PubMed Central

Birkhead, G; Vogt, R L; Heun, E M; Snyder, J T; McClane, B A

1988-01-01

Published criteria for implicating Clostridium perfringens as the cause of food-poisoning outbreaks include finding a median fecal C. perfringens spore count of greater than 10(6)/g among specimens from ill persons. We investigated a food-poisoning outbreak with the epidemiologic characteristics of C. perfringens-related disease in a nursing home in which the median fecal spore count for ill patients (2.5 X 10(7)/g) was similar to that for well patients (4.0 X 10(6)/g), making the etiology of the outbreak uncertain. All ill and well patients tested had eaten turkey, the implicated food item. C. perfringens enterotoxin was detected by reverse passive latex agglutination in fecal specimens from six of six ill and none of four well patients who had eaten turkey (P = 0.005), suggesting that this organism had caused the outbreak. This investigation suggests that detection of fecal C. perfringens enterotoxin is a specific way to identify this organism as the causative agent in food-poisoning outbreaks. PMID:2895776

Accuracy of un-supervised versus provider-supervised self-administered HIV testing in Uganda: A randomized implementation trial.

PubMed

Asiimwe, Stephen; Oloya, James; Song, Xiao; Whalen, Christopher C

2014-12-01

Unsupervised HIV self-testing (HST) has potential to increase knowledge of HIV status; however, its accuracy is unknown. To estimate the accuracy of unsupervised HST in field settings in Uganda, we performed a non-blinded, randomized controlled, non-inferiority trial of unsupervised compared with supervised HST among selected high HIV risk fisherfolk (22.1 % HIV Prevalence) in three fishing villages in Uganda between July and September 2013. The study enrolled 246 participants and randomized them in a 1:1 ratio to unsupervised HST or provider-supervised HST. In an intent-to-treat analysis, the HST sensitivity was 90 % in the unsupervised arm and 100 % among the provider-supervised, yielding a difference 0f -10 % (90 % CI -21, 1 %); non-inferiority was not shown. In a per protocol analysis, the difference in sensitivity was -5.6 % (90 % CI -14.4, 3.3 %) and did show non-inferiority. We conclude that unsupervised HST is feasible in rural Africa and may be non-inferior to provider-supervised HST.

A Study of Failure Events in Drinking Water Systems As a Basis for Comparison and Evaluation of the Efficacy of Potable Reuse Schemes

PubMed Central

Onyango, Laura A.; Quinn, Chloe; Tng, Keng H.; Wood, James G.; Leslie, Greg

2015-01-01

Potable reuse is implemented in several countries around the world to augment strained water supplies. This article presents a public health perspective on potable reuse by comparing the critical infrastructure and institutional capacity characteristics of two well-established potable reuse schemes with conventional drinking water schemes in developed nations that have experienced waterborne outbreaks. Analysis of failure events in conventional water systems between 2003 and 2013 showed that despite advances in water treatment technologies, drinking water outbreaks caused by microbial contamination were still frequent in developed countries and can be attributed to failures in infrastructure or institutional practices. Numerous institutional failures linked to ineffective treatment protocols, poor operational practices, and negligence were detected. In contrast, potable reuse schemes that use multiple barriers, online instrumentation, and operational measures were found to address the events that have resulted in waterborne outbreaks in conventional systems in the past decade. Syndromic surveillance has emerged as a tool in outbreak detection and was useful in detecting some outbreaks; increases in emergency department visits and GP consultations being the most common data source, suggesting potential for an increasing role in public health surveillance of waterborne outbreaks. These results highlight desirable characteristics of potable reuse schemes from a public health perspective with potential for guiding policy on surveillance activities. PMID:27053920

A Study of Failure Events in Drinking Water Systems As a Basis for Comparison and Evaluation of the Efficacy of Potable Reuse Schemes.

PubMed

Onyango, Laura A; Quinn, Chloe; Tng, Keng H; Wood, James G; Leslie, Greg

2015-01-01

Potable reuse is implemented in several countries around the world to augment strained water supplies. This article presents a public health perspective on potable reuse by comparing the critical infrastructure and institutional capacity characteristics of two well-established potable reuse schemes with conventional drinking water schemes in developed nations that have experienced waterborne outbreaks. Analysis of failure events in conventional water systems between 2003 and 2013 showed that despite advances in water treatment technologies, drinking water outbreaks caused by microbial contamination were still frequent in developed countries and can be attributed to failures in infrastructure or institutional practices. Numerous institutional failures linked to ineffective treatment protocols, poor operational practices, and negligence were detected. In contrast, potable reuse schemes that use multiple barriers, online instrumentation, and operational measures were found to address the events that have resulted in waterborne outbreaks in conventional systems in the past decade. Syndromic surveillance has emerged as a tool in outbreak detection and was useful in detecting some outbreaks; increases in emergency department visits and GP consultations being the most common data source, suggesting potential for an increasing role in public health surveillance of waterborne outbreaks. These results highlight desirable characteristics of potable reuse schemes from a public health perspective with potential for guiding policy on surveillance activities.

Noroviruses associated with acute gastroenteritis in a children's day care facility in Rio de Janeiro, Brazil.

PubMed

Gallimore, C I; Barreiros, M A B; Brown, D W G; Nascimento, J P; Leite, J P G

2004-03-01

Noroviruses (Norwalk-like viruses) are an important cause of gastroenteritis worldwide. They are the most common cause of outbreaks of gastroenteritis in the adult population and occur in nursing homes for the elderly, geriatric wards, medical wards, and in hotel and restaurant settings. Food-borne outbreaks have also occurred following consumption of contaminated oysters. This study describes the application of a reverse transcription-polymerase chain reaction (RT-PCR) assay using random primers (PdN6) and specific Ni and E3 primers, directed at a small region of the RNA-dependent RNA polymerase-coding region of the norovirus genome, and DNA sequencing for the detection and preliminary characterisation of noroviruses in outbreaks of gastroenteritis in children in Brazil. The outbreak samples were collected from children <5 years of age at the Bertha Lutz children's day care facility at Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, that occurred between 1996 and 1998, where no pathogen had been identified. At the Bertha Lutz day care center facility, only Fiocruz's employee children are provided for, and they come from different social, economic and cultural backgrounds. Three distinct genogroup II strains were detected in three outbreaks in 1997/98 and were most closely related to genotypes GII-3 (Mexico virus) and GII-4 (Grimsby virus), both of which have been detected in paediatric and adult outbreaks of gastroenteritis worldwide.

First detection of foot-and-mouth disease virus O/Ind-2001d in Vietnam.

PubMed

Vu, Le T; Long, Ngo T; Brito, Barbara; Stenfeldt, Carolina; Phuong, Nguyen T; Hoang, Bui H; Pauszek, Steven J; Hartwig, Ethan J; Smoliga, George R; Vu, Pham P; Quang, Le T V; Hung, Vo V; Tho, Nguyen D; Dong, Pham V; Minh, Phan Q; Bertram, Miranda; Fish, Ian H; Rodriguez, Luis L; Dung, Do H; Arzt, Jonathan

2017-01-01

In recent years, foot-and-mouth disease virus (FMDV) serotype O, topotype Middle East-South Asia (ME-SA), lineage Ind-2001d has spread from the Indian subcontinent to the Middle East, North Africa, and Southeast Asia. In the current report, we describe the first detection of this lineage in Vietnam in May, 2015 in Đắk Nông province. Three subsequent outbreaks caused by genetically related viruses occurred between May-October, 2015 after which the virus was not detected in clinical outbreaks for at least 15 subsequent months. The observed outbreaks affected (in chronological order): cattle in Đắk Nông province, pigs in Đắk Lắk province and Đắk Nông province, and cattle in Ninh Thuận province. The clinical syndromes associated with these outbreaks were consistent with typical FMD in the affected species. Overall attack rate on affected premises was 0.85 in pigs and 0.93 in cattle over the course of the outbreak. Amongst 378 pigs at risk on affected premises, 85 pigs died during the outbreaks; there were no deaths among cattle. The manner in which FMDV/O/ME-SA/Ind-2001d was introduced into Vietnam remains undetermined; however, movement of live cattle is the suspected route. This incursion has substantial implications for epidemiology and control of FMD in Southeast Asia.

Listeriosis Outbreaks and Associated Food Vehicles, United States, 1998–2008

PubMed Central

Cartwright, Emily J.; Jackson, Kelly A.; Johnson, Shacara D.; Graves, Lewis M.; Mahon, Barbara E.

2013-01-01

Listeria monocytogenes, a bacterial foodborne pathogen, can cause meningitis, bacteremia, and complications during pregnancy. This report summarizes listeriosis outbreaks reported to the Foodborne Disease Outbreak Surveillance System of the Centers for Disease Control and Prevention during 1998–2008. The study period includes the advent of PulseNet (a national molecular subtyping network for outbreak detection) in 1998 and the Listeria Initiative (enhanced surveillance for outbreak investigation) in 2004. Twenty-four confirmed listeriosis outbreaks were reported during 1998–2008, resulting in 359 illnesses, 215 hospitalizations, and 38 deaths. Outbreaks earlier in the study period were generally larger and longer. Serotype 4b caused the largest number of outbreaks and outbreak-associated cases. Ready-to-eat meats caused more early outbreaks, and novel vehicles (i.e., sprouts, taco/nacho salad) were associated with outbreaks later in the study period. These changes may reflect the effect of PulseNet and the Listeria Initiative and regulatory initiatives designed to prevent contamination in ready-to-eat meat and poultry products. PMID:23260661

Representativeness of Tuberculosis Genotyping Surveillance in the United States, 2009-2010.

PubMed

Shak, Emma B; France, Anne Marie; Cowan, Lauren; Starks, Angela M; Grant, Juliana

2015-01-01

Genotyping of Mycobacterium tuberculosis isolates contributes to tuberculosis (TB) control through detection of possible outbreaks. However, 20% of U.S. cases do not have an isolate for testing, and 10% of cases with isolates do not have a genotype reported. TB outbreaks in populations with incomplete genotyping data might be missed by genotyping-based outbreak detection. Therefore, we assessed the representativeness of TB genotyping data by comparing characteristics of cases reported during January 1, 2009-December 31, 2010, that had a genotype result with those cases that did not. Of 22,476 cases, 14,922 (66%) had a genotype result. Cases without genotype results were more likely to be patients <19 years of age, with unknown HIV status, of female sex, U.S.-born, and with no recent history of homelessness or substance abuse. Although cases with a genotype result are largely representative of all reported U.S. TB cases, outbreak detection methods that rely solely on genotyping data may underestimate TB transmission among certain groups.

Representativeness of Tuberculosis Genotyping Surveillance in the United States, 2009–2010

PubMed Central

Shak, Emma B.; Cowan, Lauren; Starks, Angela M.; Grant, Juliana

2015-01-01

Genotyping of Mycobacterium tuberculosis isolates contributes to tuberculosis (TB) control through detection of possible outbreaks. However, 20% of U.S. cases do not have an isolate for testing, and 10% of cases with isolates do not have a genotype reported. TB outbreaks in populations with incomplete genotyping data might be missed by genotyping-based outbreak detection. Therefore, we assessed the representativeness of TB genotyping data by comparing characteristics of cases reported during January 1, 2009–December 31, 2010, that had a genotype result with those cases that did not. Of 22,476 cases, 14,922 (66%) had a genotype result. Cases without genotype results were more likely to be patients <19 years of age, with unknown HIV status, of female sex, U.S.-born, and with no recent history of homelessness or substance abuse. Although cases with a genotype result are largely representative of all reported U.S. TB cases, outbreak detection methods that rely solely on genotyping data may underestimate TB transmission among certain groups. PMID:26556930

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.

Estimating the Effectiveness of Early Control Measures through School Absenteeism Surveillance in Observed Outbreaks at Rural Schools in Hubei, China

PubMed Central

Fan, Yunzhou; Yang, Mei; Jiang, Hongbo; Wang, Ying; Yang, Wenwen; Zhang, Zhixia; Yan, Weirong; Diwan, Vinod K.; Xu, Biao; Dong, Hengjin; Palm, Lars; Liu, Li; Nie, Shaofa

2014-01-01

Background School absenteeism is a common data source in syndromic surveillance, which allows for the detection of outbreaks at an early stage. Previous studies focused on its correlation with other data sources. In this study, we evaluated the effectiveness of control measures based on early warning signals from school absenteeism surveillance in rural Chinese schools. Methods A school absenteeism surveillance system was established in all 17 primary schools in 3 adjacent towns in the Chinese region of Hubei. Three outbreaks (varicella, mumps, and influenza-like illness) were detected and controlled successfully from April 1, 2012, to January 15, 2014. An impulse susceptible-exposed-infectious-recovered model was used to fit the epidemics of these three outbreaks. Moreover, it simulated the potential epidemics under interventions resulting from traditional surveillance signals. The effectiveness of the absenteeism-based control measures was evaluated by comparing the simulated datasets. Results The school absenteeism system generated 52 signals. Three outbreaks were verified through epidemiological investigation. Compared to traditional surveillance, the school absenteeism system generated simultaneous signals for the varicella outbreak, but 3 days in advance for the mumps outbreak and 2–4 days in advance for the influenza-like illness outbreak. The estimated excess protection rates of control measures based on early signals were 0.0%, 19.0–44.1%, and 29.0–37.0% for the three outbreaks, respectively. Conclusions Although not all outbreak control measures can benefit from early signals through school absenteeism surveillance, the effectiveness of early signal-based interventions is obvious. School absenteeism surveillance plays an important role in reducing outbreak spread. PMID:25250786

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.

Coccidioidomycosis Outbreaks, United States and Worldwide, 1940-2015.

PubMed

Freedman, Michael; Jackson, Brendan R; McCotter, Orion; Benedict, Kaitlin

2018-03-01

Coccidioidomycosis causes substantial illness and death in the United States each year. Although most cases are sporadic, outbreaks provide insight into the clinical and environmental features of coccidioidomycosis, high-risk activities, and the geographic range of Coccidioides fungi. We identified reports published in English of 47 coccidioidomycosis outbreaks worldwide that resulted in 1,464 cases during 1940-2015. Most (85%) outbreaks were associated with environmental exposures; the 2 largest outbreaks resulted from an earthquake and a large dust storm. More than one third of outbreaks occurred in areas where the fungus was not previously known to be endemic, and more than half of outbreaks involved occupational exposures. Coccidioidomycosis outbreaks can be difficult to detect and challenging to prevent given the unknown effectiveness of environmental control methods and personal protective equipment; therefore, increased awareness of coccidioidomycosis outbreaks is needed among public health professionals, healthcare providers, and the public.

Coccidioidomycosis Outbreaks, United States and Worldwide, 1940–2015

PubMed Central

Freedman, Michael; Jackson, Brendan R.; McCotter, Orion

2018-01-01

Coccidioidomycosis causes substantial illness and death in the United States each year. Although most cases are sporadic, outbreaks provide insight into the clinical and environmental features of coccidioidomycosis, high-risk activities, and the geographic range of Coccidioides fungi. We identified reports published in English of 47 coccidioidomycosis outbreaks worldwide that resulted in 1,464 cases during 1940–2015. Most (85%) outbreaks were associated with environmental exposures; the 2 largest outbreaks resulted from an earthquake and a large dust storm. More than one third of outbreaks occurred in areas where the fungus was not previously known to be endemic, and more than half of outbreaks involved occupational exposures. Coccidioidomycosis outbreaks can be difficult to detect and challenging to prevent given the unknown effectiveness of environmental control methods and personal protective equipment; therefore, increased awareness of coccidioidomycosis outbreaks is needed among public health professionals, healthcare providers, and the public. PMID:29460741

[Outbreaks of acute gastroenteritis caused by small round structured viruses in Tokyo].

PubMed

Sekine, S; Hayashi, Y; Ando, T; Ohta, K; Miki, T; Okada, S

1992-07-01

Of 34 non-bacterial gastroenteritis outbreaks which occurred at day-care centers, kindergartens, elementary and secondary schools in Tokyo during the period from February 1985 to June 1991, 28 outbreaks from which small round structured viruses (SRSV) were detected in the patients' stool specimens by electron microscopy were subjected to an epidemiological investigation. The outbreaks tended to occur frequently in the cold season; twenty-two (79%) of these outbreaks from November through April. Though detailed epidemiological informations was not obtained from all outbreaks, the common source of infection were presumed to be present in many of the outbreaks, judged from the incidence as to time course of patients. Food doubted to be incriminated as transmission vehicles in these outbreaks was served at schools, kindergartens, and lodgings. In some outbreaks, SRSV was detected from stool specimens of food handlers, or they were seroconverted to SRSV, suggesting that food was incriminated as a transmission vehicle. The symptoms of patients differ slightly from age to age: in the age range of 0 to 6 years, vomiting 90%, fever 41% and diarrhea 32%; in the 6 to 12 year-olds, nausea 61%, vomiting 48%, abdominal pain 65%, diarrhea 20% and fever 29%; and in the 12 to 15 year-olds, nausea 69%, vomiting 42%, abdominal pain 60%, diarrhea 30% and fever 34%. The lower the age of patient vomiting was more frequently observed. In these lower age groups, the frequency of nausea and vomiting tended to exceed that of diarrhea.

Quantification and molecular characterization of Salmonella isolated from food samples involved in salmonellosis outbreaks in Rio Grande do Sul, Brazil

PubMed Central

Mürmann, Lisandra; dos Santos, Maria Cecília; Longaray, Solange Mendes; Both, Jane Mari Corrêa; Cardoso, Marisa

2008-01-01

Data concerning the prevalence and populations of Salmonella in foods implicated in outbreaks may be important to the development of quantitative microbial risk assessments of individual food products. In this sense, the objective of the present study was to assess the amount of Salmonella sp. in different foods implicated in foodborne outbreaks in Rio Grande do Sul occurred in 2005 and to characterize the isolated strains using phenotypic and genotypic methods. Nineteen food samples involved in ten foodborne outbreaks occurred in 2005, and positive on Salmonella isolation at the Central Laboratory of the Health Department of the State of Rio Grande do Sul, were included in this study. Food samples were submitted to estimation of Salmonella using the Most Probable Number (MPN) technique. Moreover, one confirmed Salmonella colony of each food sample was serotyped, characterized by its XbaI-macrorestriction profile, and submitted to antimicrobial resistance testing. Foods containing eggs, mayonnaise or chicken were contaminated with Salmonella in eight outbreaks. Higher counts (>107 MPN.g-1) of Salmonella were detected mostly in foods containing mayonnaise. The isolation of Salmonella from multiple food items in five outbreaks probably resulted from the cross-contamination, and the high Salmonella counts detected in almost all analyzed samples probably resulted from storing in inadequate temperature. All strains were identified as S. Enteritidis, and presented a unique macrorestriction profile, demonstrating the predominance of one clonal group in foods involved in the salmonellosis outbreaks. A low frequency of antimicrobial resistant S. Enteritidis strains was observed and nalidixic acid was the only resistance marker detected. PMID:24031261

Using demographic characteristics of populations to detect spatial fragmentation following suspected ebola outbreaks in great apes.

PubMed

Genton, Céline; Cristescu, Romane; Gatti, Sylvain; Levréro, Florence; Bigot, Elodie; Motsch, Peggy; Le Gouar, Pascaline; Pierre, Jean-Sébastien; Ménard, Nelly

2017-09-01

Demographic crashes due to emerging diseases can contribute to population fragmentation and increase extinction risk of small populations. Ebola outbreaks in 2002-2004 are suspected to have caused a decline of more than 80% in some Western lowland gorilla (Gorilla gorilla gorilla) populations. We investigated whether demographic indicators of this event allowed for the detection of spatial fragmentation in gorilla populations. We collected demographic data from two neighbouring populations: the Lokoué population, suspected to have been affected by an Ebola outbreak (followed from 2001 to 2014), and the Romani population, of unknown demographic status before Ebola outbreaks (followed from 2005 to 2014). Ten years after the outbreak, the Lokoué population is slowly recovering and the short-term demographic indicators of a population crash were no longer detectable. The Lokoué population has not experienced any additional demographic perturbation over the past decade. The Romani population did not show any of the demographic indicators of a population crash over the past decade. Its demographic structure remained similar to that of unaffected populations. Our results highlighted that the Ebola disease could contribute to fragmentation of gorilla populations due to the spatially heterogeneous impact of its outbreaks. The demographic structure of populations (i.e., age-sex and group structure) can be useful indicators of a possible occurrence of recent Ebola outbreaks in populations without known history, and may be more broadly used in other emerging disease/species systems. Longitudinal data are critical to our understanding of the impact of emerging diseases on wild populations and their conservation. © 2017 Wiley Periodicals, Inc.

Emergence of Pseudomonas aeruginosa with KPC-type carbapenemase in a teaching hospital: an 8-year study.

PubMed

García Ramírez, Dolores; Nicola, Federico; Zarate, Soledad; Relloso, Silvia; Smayevsky, Jorgelina; Arduino, Sonia

2013-10-01

An outbreak of Klebsiella pneumoniae carbapenamase (KPC)-producing K. pneumoniae occurred at our institution. Multiresistant Pseudomonas aeruginosa could have acquired this transmissible resistance mechanism, going unnoticed because its phenotypic detection in this species is difficult. We compared P. aeruginosa isolates obtained before and after the KPC-producing K. pneumoniae outbreak. No bla(KPC) genes were detected in the isolates obtained before the outbreak, whereas 33/76 (43%) of the isolates obtained after the outbreak harboured the bla(KPC) gene. P. aeruginosa may thus become a reservoir of this transmissible resistance mechanism. It is very important to understand the epidemiology of these multiresistant isolates, in order to achieve early implementation of adequate control measures to contain and reduce their dissemination in the hospital environment.

Application of supervised and unsupervised tools to direct effects-based monitoring efforts in the Great Lakes areas of concern: Maumee River, Ohio

EPA Science Inventory

Effects-based approaches that employ molecular and tissue level tools to detect and characterize biological responses to contaminants can be a useful complement to chemical monitoring approaches. When the source/type of contamination is known, a predetermined, or supervised, set...

A new prior for bayesian anomaly detection: application to biosurveillance.

PubMed

Shen, Y; Cooper, G F

2010-01-01

Bayesian anomaly detection computes posterior probabilities of anomalous events by combining prior beliefs and evidence from data. However, the specification of prior probabilities can be challenging. This paper describes a Bayesian prior in the context of disease outbreak detection. The goal is to provide a meaningful, easy-to-use prior that yields a posterior probability of an outbreak that performs at least as well as a standard frequentist approach. If this goal is achieved, the resulting posterior could be usefully incorporated into a decision analysis about how to act in light of a possible disease outbreak. This paper describes a Bayesian method for anomaly detection that combines learning from data with a semi-informative prior probability over patterns of anomalous events. A univariate version of the algorithm is presented here for ease of illustration of the essential ideas. The paper describes the algorithm in the context of disease-outbreak detection, but it is general and can be used in other anomaly detection applications. For this application, the semi-informative prior specifies that an increased count over baseline is expected for the variable being monitored, such as the number of respiratory chief complaints per day at a given emergency department. The semi-informative prior is derived based on the baseline prior, which is estimated from using historical data. The evaluation reported here used semi-synthetic data to evaluate the detection performance of the proposed Bayesian method and a control chart method, which is a standard frequentist algorithm that is closest to the Bayesian method in terms of the type of data it uses. The disease-outbreak detection performance of the Bayesian method was statistically significantly better than that of the control chart method when proper baseline periods were used to estimate the baseline behavior to avoid seasonal effects. When using longer baseline periods, the Bayesian method performed as well as the control chart method. The time complexity of the Bayesian algorithm is linear in the number of the observed events being monitored, due to a novel, closed-form derivation that is introduced in the paper. This paper introduces a novel prior probability for Bayesian outbreak detection that is expressive, easy-to-apply, computationally efficient, and performs as well or better than a standard frequentist method.

Outbreaks and Investigations

MedlinePlus

... on Facebook Tweet Share Compartir Disease detectives collecting soil samples to test for fungus When fungal disease ... a hydroelectric dam Source: related to disruption of soil contaminated with bat droppings Outbreak investigation partners: Dominican ...

Big Data and the Global Public Health Intelligence Network (GPHIN)

PubMed Central

Dion, M; AbdelMalik, P; Mawudeku, A

2015-01-01

Background Globalization and the potential for rapid spread of emerging infectious diseases have heightened the need for ongoing surveillance and early detection. The Global Public Health Intelligence Network (GPHIN) was established to increase situational awareness and capacity for the early detection of emerging public health events. Objective To describe how the GPHIN has used Big Data as an effective early detection technique for infectious disease outbreaks worldwide and to identify potential future directions for the GPHIN. Findings Every day the GPHIN analyzes over more than 20,000 online news reports (over 30,000 sources) in nine languages worldwide. A web-based program aggregates data based on an algorithm that provides potential signals of emerging public health events which are then reviewed by a multilingual, multidisciplinary team. An alert is sent out if a potential risk is identified. This process proved useful during the Severe Acute Respiratory Syndrome (SARS) outbreak and was adopted shortly after by a number of countries to meet new International Health Regulations that require each country to have the capacity for early detection and reporting. The GPHIN identified the early SARS outbreak in China, was credited with the first alert on MERS-CoV and has played a significant role in the monitoring of the Ebola outbreak in West Africa. Future developments are being considered to advance the GPHIN’s capacity in light of other Big Data sources such as social media and its analytical capacity in terms of algorithm development. Conclusion The GPHIN’s early adoption of Big Data has increased global capacity to detect international infectious disease outbreaks and other public health events. Integration of additional Big Data sources and advances in analytical capacity could further strengthen the GPHIN’s capability for timely detection and early warning. PMID:29769954

Detection of West Nile Virus - Lineage 2 in Culex pipiens mosquitoes, associated with disease outbreak in Greece, 2017.

PubMed

Mavridis, Konstantinos; Fotakis, Emmanouil A; Kioulos, Ilias; Mpellou, Spiridoula; Konstantas, Spiros; Varela, Evangelia; Gewehr, Sandra; Diamantopoulos, Vasilis; Vontas, John

2018-06-01

During July-October 2017 a WNV outbreak took place in the Peloponnese, Southern Greece with five confirmed deaths. During routine monitoring survey in the Peloponnese, supported by the local Prefecture, we have confirmed the presence of all three Culex pipiens biotypes in the region, with a high percentage of Culex pipiens/molestus hybrids (37.0%) which are considered a highly competent vector of WNV. Kdr mutations related to pyrethroid resistance were found at relatively low levels (14.3% homozygosity) while no mosquitoes harboring the recently identified chitin synthase diflubenzuron-resistance mutations were detected in the region. As an immediate action, following the disease outbreak (within days), we collected a large number of mosquitoes using CO 2 CDC traps from the villages in the Argolis area of the Peloponnese, where high incidence of WNV human infections were reported. WNV lineage 2 was detected in 3 out of 47 Cx. pipiens mosquito pools (detection rate = 6.38%). The virus was not detected in any other mosquito species, such as Aedes albopictus, sampled from the region at the time of the disease outbreak. Our results show that detection of WNV lineage 2 in Cx. pipiens pools is spatially and chronologically associated with human clinical cases, thus implicating Cx. pipiens mosquitoes as the most likely WNV vector. The absence of diflubenzuron resistance mutations and the low frequency of pyrethroid (kdr) resistance mutations indicates the suitability of these insecticides for Cx. pipiens control, in the format of larvicides and/or residual spraying applications respectively, which was indeed the main (evidence based) response, following the disease outbreak. Copyright © 2018 Elsevier B.V. All rights reserved.

Searching Remote Homology with Spectral Clustering with Symmetry in Neighborhood Cluster Kernels

PubMed Central

Maulik, Ujjwal; Sarkar, Anasua

2013-01-01

Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of “recent” paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request. Contact: sarkar@labri.fr. PMID:23457439

Searching remote homology with spectral clustering with symmetry in neighborhood cluster kernels.

PubMed

Maulik, Ujjwal; Sarkar, Anasua

2013-01-01

Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of "recent" paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request. sarkar@labri.fr.

Early warning system for Douglas-fir tussock moth outbreaks in the Western United States.

Treesearch

Gary E. Daterman; John M. Wenz; Katharine A. Sheehan

2004-01-01

The Early Warning System is a pheromone-based trapping system used to detect outbreaks of Douglas-fir tussock moth (DFTM, Orgyia pseudotsugata) in the western United States. Millions of acres are susceptible to DFTM defoliation, but Early Warning System monitoring focuses attention only on the relatively limited areas where outbreaks may be...

Measles Cases during Ebola Outbreak, West Africa, 2013-2106.

PubMed

Colavita, Francesca; Biava, Mirella; Castilletti, Concetta; Quartu, Serena; Vairo, Francesco; Caglioti, Claudia; Agrati, Chiara; Lalle, Eleonora; Bordi, Licia; Lanini, Simone; Guanti, Michela Delli; Miccio, Rossella; Ippolito, Giuseppe; Capobianchi, Maria R; Di Caro, Antonino

2017-06-01

The recent Ebola outbreak in West Africa caused breakdowns in public health systems, which might have caused outbreaks of vaccine-preventable diseases. We tested 80 patients admitted to an Ebola treatment center in Freetown, Sierra Leone, for measles. These patients were negative for Ebola virus. Measles virus IgM was detected in 13 (16%) of the patients.

Evaluating a New Online Course in the Epidemiology of Infectious Diseases by Studying Student Learning Styles

ERIC Educational Resources Information Center

Rogers, James W.; Cox, James R.

2008-01-01

At RMIT University, students may now elect to study infectious diseases through a course called Outbreak--The Detection and Control of Infectious Disease. Outbreak was designed to simulate in an online class the effective teamwork required to bring resolution to outbreak crises and enable frameworks for future prevention. The appropriateness of…

[Legionella pneumophila pneumonia community epidemic outbreak in Barcelona: "The Barceloneta outbreak". Effect on the early diagnosis and treatment].

PubMed

Jericó Alba, C; Nogués Solán, X; Santos Martínez, M J; Félez Flor, M; Garcés Jarque, J M; Mariñosa Marré, M; Sanz Salvador, X

2004-02-01

Clinical and microbiological descriptive analysis of the outbreak of community legionnaire's disease recorded in the Barcelona's Barcelonesa neighborhood in November 2000. Retrospective review of the epidemiological and clinical manifestations, as well as the evolution of the cases of Legionella pneumophila pneumonia associated with the outbreak and cared of in the Hospital del Mar. The 48 patients evaluated, all of them with confirmed diagnoses, represent 89% of the cases communicated. Seventy-five percent of patients showed some underlying disease, 54% had some criterion for severity, and mortality was 4%. In 81% of cases the detection of the antigen of Legionella pneumophila in urine was the diagnostic method. The detection in urine of the Legionella pneumophila antigen makes possible the early diagnosis of legionnaire's disease, particularly in epidemic outbreaks, which that facilitates the fast establishment of the adequate treatment and contributes to the reduction in mortality even in patients of high risk.

An Unsupervised Anomalous Event Detection and Interactive Analysis Framework for Large-scale Satellite Data

NASA Astrophysics Data System (ADS)

LIU, Q.; Lv, Q.; Klucik, R.; Chen, C.; Gallaher, D. W.; Grant, G.; Shang, L.

2016-12-01

Due to the high volume and complexity of satellite data, computer-aided tools for fast quality assessments and scientific discovery are indispensable for scientists in the era of Big Data. In this work, we have developed a framework for automated anomalous event detection in massive satellite data. The framework consists of a clustering-based anomaly detection algorithm and a cloud-based tool for interactive analysis of detected anomalies. The algorithm is unsupervised and requires no prior knowledge of the data (e.g., expected normal pattern or known anomalies). As such, it works for diverse data sets, and performs well even in the presence of missing and noisy data. The cloud-based tool provides an intuitive mapping interface that allows users to interactively analyze anomalies using multiple features. As a whole, our framework can (1) identify outliers in a spatio-temporal context, (2) recognize and distinguish meaningful anomalous events from individual outliers, (3) rank those events based on "interestingness" (e.g., rareness or total number of outliers) defined by users, and (4) enable interactively query, exploration, and analysis of those anomalous events. In this presentation, we will demonstrate the effectiveness and efficiency of our framework in the application of detecting data quality issues and unusual natural events using two satellite datasets. The techniques and tools developed in this project are applicable for a diverse set of satellite data and will be made publicly available for scientists in early 2017.

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.

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.

Novel Use of Flu Surveillance Data: Evaluating Potential of Sentinel Populations for Early Detection of Influenza Outbreaks.

PubMed

Daughton, Ashlynn R; Velappan, Nileena; Abeyta, Esteban; Priedhorsky, Reid; Deshpande, Alina

2016-01-01

Influenza causes significant morbidity and mortality each year, with 2-8% of weekly outpatient visits around the United States for influenza-like-illness (ILI) during the peak of the season. Effective use of existing flu surveillance data allows officials to understand and predict current flu outbreaks and can contribute to reductions in influenza morbidity and mortality. Previous work used the 2009-2010 influenza season to investigate the possibility of using existing military and civilian surveillance systems to improve early detection of flu outbreaks. Results suggested that civilian surveillance could help predict outbreak trajectory in local military installations. To further test that hypothesis, we compare pairs of civilian and military outbreaks in seven locations between 2000 and 2013. We find no predictive relationship between outbreak peaks or time series of paired outbreaks. This larger study does not find evidence to support the hypothesis that civilian data can be used as sentinel surveillance for military installations. We additionally investigate the effect of modifying the ILI case definition between the standard Department of Defense definition, a more specific definition proposed in literature, and confirmed Influenza A. We find that case definition heavily impacts results. This study thus highlights the importance of careful selection of case definition, and appropriate consideration of case definition in the interpretation of results.

Investigation of a type C/D botulism outbreak in free-range laying hens in France.

PubMed

Souillard, R; Le Maréchal, C; Ballan, V; Rouxel, S; Léon, D; Balaine, L; Poëzevara, T; Houard, E; Robineau, B; Robinault, C; Chemaly, M; Le Bouquin, S

2017-04-01

In 2014, a botulism outbreak in a flock of laying hens was investigated in France. In the flock of 5020 hens, clinical signs of botulism occurred at 46 weeks of age. A type C/D botulism outbreak was confirmed using the mouse lethality assay for detection of botulinum toxin in serum and a real-time PCR test to detect Clostridium botulinum in intestinal contents. The disease lasted one week with a mortality rate of 2.6% without recurrence. Botulism in laying hens has rarely been reported. Five monthly visits were made to the farm between December 2014 and May 2015 for a longitudinal study of the persistence of C. botulinum in the poultry house after the outbreak, and to assess egg contamination by C. botulinum. Several samples were collected on each visit: in the house (from the ventilation circuit, the egg circuit, water and feed, droppings) and the surrounding area. Thirty clean and 30 dirty eggs were also swabbed at each visit. In addition, 12 dirty and 12 clean eggs were collected to analyse eggshell and egg content. The samples were analysed using real-time PCR to detect type C/D C. botulinum. The bacterium was still detected in the house more than 5 months after the outbreak, mostly on the walls and in the egg circuit. Regarding egg contamination, the bacteria were detected only on the shell but not in the content of the eggs. Control measures should therefore be implemented throughout the egg production period to avoid dissemination of the bacteria, particularly during egg collection.

Toxin Detection in Patients' Sera by Mass Spectrometry during Two Outbreaks of Type A Botulism in France

PubMed Central

Mazuet, Christelle; Ezan, Eric; Volland, Hervé; Becher, François

2012-01-01

In two outbreaks of food-borne botulism in France, Clostridium botulinum type A was isolated and characterized from incriminated foods. Botulinum neurotoxin type A was detected in the patients' sera by mouse bioassay and in vitro endopeptidase assay with an immunocapture step and identification of the cleavage products by mass spectrometry. PMID:22993181

Metagenomic Analysis of Viruses in Feces from Unsolved Outbreaks of Gastroenteritis in Humans

PubMed Central

Moore, Nicole E.; Wang, Jing; Hewitt, Joanne; Croucher, Dawn; Williamson, Deborah A.; Paine, Shevaun; Yen, Seiha; Greening, Gail E.

2014-01-01

The etiology of an outbreak of gastroenteritis in humans cannot always be determined, and ∼25% of outbreaks remain unsolved in New Zealand. It is hypothesized that novel viruses may account for a proportion of unsolved cases, and new unbiased high-throughput sequencing methods hold promise for their detection. Analysis of the fecal metagenome can reveal the presence of viruses, bacteria, and parasites which may have evaded routine diagnostic testing. Thirty-one fecal samples from 26 gastroenteritis outbreaks of unknown etiology occurring in New Zealand between 2011 and 2012 were selected for de novo metagenomic analysis. A total data set of 193 million sequence reads of 150 bp in length was produced on an Illumina MiSeq. The metagenomic data set was searched for virus and parasite sequences, with no evidence of novel pathogens found. Eight viruses and one parasite were detected, each already known to be associated with gastroenteritis, including adenovirus, rotavirus, sapovirus, and Dientamoeba fragilis. In addition, we also describe the first detection of human parechovirus 3 (HPeV3) in Australasia. Metagenomics may thus provide a useful audit tool when applied retrospectively to determine where routine diagnostic processes may have failed to detect a pathogen. PMID:25339401

Waterborne disease outbreak detection: an integrated approach using health administrative databases.

PubMed

Coly, S; Vincent, N; Vaissiere, E; Charras-Garrido, M; Gallay, A; Ducrot, C; Mouly, D

2017-08-01

Hundreds of waterborne disease outbreaks (WBDO) of acute gastroenteritis (AGI) due to contaminated tap water are reported in developed countries each year. Such outbreaks are probably under-detected. The aim of our study was to develop an integrated approach to detect and study clusters of AGI in geographical areas with homogeneous exposure to drinking water. Data for the number of AGI cases are available at the municipality level while exposure to tap water depends on drinking water networks (DWN). These two geographical units do not systematically overlap. This study proposed to develop an algorithm which would match the most relevant grouping of municipalities with a specific DWN, in order that tap water exposure can be taken into account when investigating future disease outbreaks. A space-time detection method was applied to the grouping of municipalities. Seven hundred and fourteen new geographical areas (groupings of municipalities) were obtained compared with the 1,310 municipalities and the 1,706 DWN. Eleven potential WBDO were identified in these groupings of municipalities. For ten of them, additional environmental investigations identified at least one event that could have caused microbiological contamination of DWN in the days previous to the occurrence of a reported WBDO.

Molecular Analysis of an Outbreak of Lethal Postpartum Sepsis Caused by Streptococcus pyogenes

PubMed Central

Turner, Claire E.; Dryden, Matthew; Holden, Matthew T. G.; Davies, Frances J.; Lawrenson, Richard A.; Farzaneh, Leili; Bentley, Stephen D.; Efstratiou, Androulla

2013-01-01

Sepsis is now the leading direct cause of maternal death in the United Kingdom, and Streptococcus pyogenes is the leading pathogen. We combined conventional and genomic analyses to define the duration and scale of a lethal outbreak. Two postpartum deaths caused by S. pyogenes occurred within 24 h; one was characterized by bacteremia and shock and the other by hemorrhagic pneumonia. The women gave birth within minutes of each other in the same maternity unit 2 days earlier. Seven additional infections in health care and household contacts were subsequently detected and treated. All cluster-associated S. pyogenes isolates were genotype emm1 and were initially indistinguishable from other United Kingdom emm1 isolates. Sequencing of the virulence gene sic revealed that all outbreak isolates had the same unique sic type. Genome sequencing confirmed that the cluster was caused by a unique S. pyogenes clone. Transmission between patients occurred on a single day and was associated with casual contact only. A single isolate from one patient demonstrated a sequence change in sic consistent with longer infection duration. Transmission to health care workers was traced to single clinical contacts with index cases. The last case was detected 18 days after the first case. Following enhanced surveillance, the outbreak isolate was not detected again. Mutations in bacterial regulatory genes played no detectable role in this outbreak, illustrating the intrinsic ability of emm1 S. pyogenes to spread while retaining virulence. This fast-moving outbreak highlights the potential of S. pyogenes to cause a range of diseases in the puerperium with rapid transmission, underlining the importance of immediate recognition and response by clinical infection and occupational health teams. PMID:23616448

Dynamic linear models using the Kalman filter for early detection and early warning of malaria outbreaks

NASA Astrophysics Data System (ADS)

Merkord, C. L.; Liu, Y.; DeVos, M.; Wimberly, M. C.

2015-12-01

Malaria early detection and early warning systems are important tools for public health decision makers in regions where malaria transmission is seasonal and varies from year to year with fluctuations in rainfall and temperature. Here we present a new data-driven dynamic linear model based on the Kalman filter with time-varying coefficients that are used to identify malaria outbreaks as they occur (early detection) and predict the location and timing of future outbreaks (early warning). We fit linear models of malaria incidence with trend and Fourier form seasonal components using three years of weekly malaria case data from 30 districts in the Amhara Region of Ethiopia. We identified past outbreaks by comparing the modeled prediction envelopes with observed case data. Preliminary results demonstrated the potential for improved accuracy and timeliness over commonly-used methods in which thresholds are based on simpler summary statistics of historical data. Other benefits of the dynamic linear modeling approach include robustness to missing data and the ability to fit models with relatively few years of training data. To predict future outbreaks, we started with the early detection model for each district and added a regression component based on satellite-derived environmental predictor variables including precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and land surface temperature (LST) and spectral indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). We included lagged environmental predictors in the regression component of the model, with lags chosen based on cross-correlation of the one-step-ahead forecast errors from the first model. Our results suggest that predictions of future malaria outbreaks can be improved by incorporating lagged environmental predictors.

Canine Circovirus 1 (CaCV-1) and Canine Parvovirus 2 (CPV-2): Recurrent Dual Infections in a Papillon Breeding Colony.

PubMed

Thaiwong, T; Wise, A G; Maes, R K; Mullaney, T; Kiupel, M

2016-11-01

Recurrent outbreaks of sudden death and bloody diarrhea were reported in March 2013 and February 2014 in a breeding colony of Papillon dogs. During the first outbreak, 1 adult dog and 2 eight-month-old puppies died. During the second outbreak, 2 ten-week-old puppies died. One puppy from the first outbreak and 2 puppies from the second outbreak were examined at necropsy. Histologically, all 3 puppies had severe segmental crypt necrosis of the small intestine and marked lymphoid follicle depletion in the spleen and Peyer's patches. Real-time (RT) polymerase chain reaction (PCR) demonstrated abundant canine parvovirus (CPV-2) DNA (Ct<15) in the affected small intestine, and immunohistochemistry detected large amounts of CPV-2 antigen in intestinal crypt epithelium and Kupffer cells but few positive macrophages in lymphoid organs. All puppies had marked sinusoidal histiocytosis and multifocal granulomatous inflammation in mesenteric lymph nodes and spleen, prompting additional RT-PCR testing for canine circovirus 1 (CaCV-1). Very high levels of CaCV-1 DNA (Ct<13) were detected in small intestine, lymph nodes, and spleen. In situ hybridization for CaCV-1 detected rare positive nuclei of regenerating crypt epithelium but abundant amounts of CaCV-1 nucleic acid in the cytoplasm and nuclei of histiocytes in all lymphoid tissues, including granulomatous inflammatory foci and hepatic Kupffer cells. Significant levels of CaCV-1 DNA were detected in blood and serum (Ct as low as 13) but not feces from 3 surviving dogs at 2 months or 1 year after the outbreak, respectively. We hypothesize that CPV-2 infection predisposed dogs to CaCV-1 infection and ultimately resulted in more severe clinical disease. © The Author(s) 2016.

Noroviruses associated with outbreaks of acute gastroenteritis in the State of Rio Grande do Sul, Brazil, 2004-2011.

PubMed

de Andrade, Juliana da Silva Ribeiro; Rocha, Monica Simões; Carvalho-Costa, Felipe Aníbal; Fioretti, Julia Monassa; Xavier, Maria da Penha Trindade Pinheiro; Nunes, Zenaida Maria Alves; Cardoso, Jeanice; Fialho, Alexandre Madi; Leite, José Paulo Gagliardi; Miagostovich, Marize Pereira

2014-11-01

Acute gastroenteritis norovirus (NoV) in a country of continental dimensions like Brazil has resulted in under-reporting of the number of outbreaks, as well as the genotypes associated. To demonstrate the role of NoV in outbreaks occurring in the State of Rio Grande do Sul, Southern Brazil, we determined its prevalence, as well as the genotypes associated, and evaluated clinical and epidemiological aspects. NoV investigation was carried out in rotavirus group A negative stool samples from 2265 patients from 741 outbreaks that occurred in the State of Rio Grande do Sul, Brazil, during a period of eight years (2004-2011). NoV detection and nucleotide sequencing for genotype characterization was carried by using sets of primers targeting a conservative Rd-Rp polymerase genome region and the viral capsid gene, respectively. NoVs were detected in 817 stool samples (36.1%) and associated with 327 outbreaks (44.1%). NoV GII.2, GII.3, GII.4, GII.6, GII.12, GII.13, GII.14, GII.15, GII.17, GII.21; and GI.1 and GI.3 were characterized. GII.4 was the most frequently detected (72.3%), with five variants identified (Asia_2003, Hunter_2004, Yerseke_2006a, Den_Haag_2006b, New Orleans_2009). This study describes the first detection of GI.1 and GII.13 and GII.15 in Brazil and demonstrates NoV winter-spring seasonality in this region of the country. NoVs were responsible for almost 50% of outbreaks, with about 70% of them resulting from genotype GII.4 and its variants. The seasonality observed could help health authorities to establish a system of active surveillance in order to reduce NoV impact especially in congregate settings. Copyright © 2014 Elsevier B.V. All rights reserved.

How to select a proper early warning threshold to detect infectious disease outbreaks based on the China infectious disease automated alert and response system (CIDARS).

PubMed

Wang, Ruiping; Jiang, Yonggen; Michael, Engelgau; Zhao, Genming

2017-06-12

China Centre for Diseases Control and Prevention (CDC) developed the China Infectious Disease Automated Alert and Response System (CIDARS) in 2005. The CIDARS was used to strengthen infectious disease surveillance and aid in the early warning of outbreak. The CIDARS has been integrated into the routine outbreak monitoring efforts of the CDC at all levels in China. Early warning threshold is crucial for outbreak detection in the CIDARS, but CDCs at all level are currently using thresholds recommended by the China CDC, and these recommended thresholds have recognized limitations. Our study therefore seeks to explore an operational method to select the proper early warning threshold according to the epidemic features of local infectious diseases. The data used in this study were extracted from the web-based Nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS), and data for infectious disease cases were organized by calendar week (1-52) and year (2009-2015) in Excel format; Px was calculated using a percentile-based moving window (moving window [5 week*5 year], x), where x represents one of 12 centiles (0.40, 0.45, 0.50….0.95). Outbreak signals for the 12 Px were calculated using the moving percentile method (MPM) based on data from the CIDARS. When the outbreak signals generated by the 'mean + 2SD' gold standard were in line with a Px generated outbreak signal for each week during the year of 2014, this Px was then defined as the proper threshold for the infectious disease. Finally, the performance of new selected thresholds for each infectious disease was evaluated by simulated outbreak signals based on 2015 data. Six infectious diseases were selected in this study (chickenpox, mumps, hand foot and mouth diseases (HFMD), scarlet fever, influenza and rubella). Proper thresholds for chickenpox (P75), mumps (P80), influenza (P75), rubella (P45), HFMD (P75), and scarlet fever (P80) were identified. The selected proper thresholds for these 6 infectious diseases could detect almost all simulated outbreaks within a shorter time period compared to thresholds recommended by the China CDC. It is beneficial to select the proper early warning threshold to detect infectious disease aberrations based on characteristics and epidemic features of local diseases in the CIDARS.

Case study of the use of pulsed field gel electrophoresis in the detection of a food-borne outbreak.

PubMed

De Lappe, Niall; Cormican, Martin

2015-01-01

In early July 2008, a cluster of six Salmonella Agona was identified in the Republic of Ireland. A dispersed, common source outbreak was suspected. Later in July a further case was identified and the Health Protection Agency in the UK indicated that they had 32 cases of S. Agona since Feb 2008. This chapter discusses how pulsed field gel electrophoresis was used to help confirm an outbreak and to trace the source of the outbreak.

[Detection of local influenza outbreaks and role of virological diagnostics].

PubMed

Schweiger, B; Buda, S

2013-01-01

For many years, the Working Group on Influenza (AGI) has been the most important influenza surveillance system in Germany. An average sample of the population is covered by both syndromic and virological surveillance, which provides timely data regarding the onset and course of the influenza wave as well as its burden of disease. However, smaller influenza outbreaks cannot be detected by the AGI sentinel system. This is achieved by the information reported by the mandatory notification system (Protection Against Infection Act, IfSG), which serves as the second pillar of the national influenza surveillance. Approaches to recognize such outbreaks are based either on reported influenza virus detection and subsequent investigations by local health authorities or by notification of an accumulation of respiratory diseases or nosocomial infections and subsequent laboratory investigations. In this context, virological diagnostics plays an essential role. This has been true particularly for the early phase of the 2009 pandemic, but generally timely diagnostics is essential for the identification of outbreaks. Regarding potential future outbreaks, it is also important to keep an eye on animal influenza viruses that have repeatedly infected humans. This mainly concerns avian influenza viruses of the subtypes H5, H7, and H9 as well as porcine influenza viruses for which a specific PCR has been established at the National Influenza Reference Centre. An increased incidence of respiratory infections, both during and outside the season, should always encourage virological laboratory diagnostics to be performed as a prerequisite of further extensive investigations and an optimal outbreak management.

Nosocomial outbreak of neonatal gastroenteritis caused by a new serotype 4, subtype 4B human rotavirus.

PubMed

Gerna, G; Forster, J; Parea, M; Sarasini, A; Di Matteo, A; Baldanti, F; Langosch, B; Schmidt, S; Battaglia, M

1990-07-01

A nosocomial outbreak of rotavirus gastroenteritis involving 52 newborns occurred between June and September 1988 at the University Children's Hospital of Freiburg, Federal Republic of Germany. Stools from 27 representative patients were examined for rotavirus serotypes, using a monoclonal antibody-based enzyme-linked immunosorbent assay. The electropherotype was also examined by polyacrylamide gel electrophoresis of genomic RNA. As many as 18 patients were found to be infected by serotype 4, subtype 4B strain, and in all of them the same electropherotype was detected. Although rotavirus from the remaining nine patients could not be typed, the electropherotype in four was identical to that of the serotype 4, subtype 4B strain. Thus, most of the patients in the outbreak were infected by the same rotavirus strain. Retrospective epidemiological studies showed that the 4B strain began to circulate at the hospital in January 1988, whereas only rotavirus serotypes 1, 3, and 4A were detected in 1985-1987. The primary case of the outbreak was presumably a newborn with acute gastroenteritis, admitted to the hospital from a small maternity unit in the same urban area. During the outbreak, 12 of 44 healthy newborns in the nurseries of the Children's Hospital and other maternity hospitals were found to be asymptomatic rotavirus carriers, and in three of the newborns the same 4B strain was detected. This is the first reported outbreak caused by a serotype 4, subtype 4B strain.

Field study of pneumonia in vaccinated cattle associated with incorrect vaccination and Pasteurella multocida infection.

PubMed

Crawshaw, W M; Caldow, G L

2015-04-25

This field study used data on the vaccine courses against bovine respiratory disease sold by one pharmaceutical company in conjunction with pharmacovigilance data to explore reported suspected lack of expected efficacy and the reasons for this. The study ran from May 1, 2007, to April 30, 2010, and covered vaccines sold in Scotland and part of Northumberland. In total, 83 groups of cattle reported suspected lack of expected efficacy, representing 1.6 per cent of the 804,618 vaccine courses sold. It was possible to investigate 45 of these outbreaks in depth using a standard questionnaire and diagnostic protocol. Vaccine usage outwith the specific product characteristics (SPC) occurred in 47 per cent of cases (21/45). The proportion of vaccination courses used where a pathogen contained in the vaccine was detected in the diseased cattle and vaccine use was consistent with the SPC was estimated at 0.12 per cent of the courses sold. Pasteurella multocida was the most common pathogen detected and was found in 21 of the outbreaks. For outbreaks where a pathogen contained in the vaccine was detected, P. multocida was found at a significantly greater frequency (P=0.03) where vaccine use was compliant with the SPC (five of six outbreaks) compared with outbreaks where vaccine use had not been compliant with the SPC (one of seven outbreaks). The limitations of the study, including the diagnostic tests employed and definition of vaccination outwith the SPC, are discussed. British Veterinary Association.

Embedded security system for multi-modal surveillance in a railway carriage

NASA Astrophysics Data System (ADS)

Zouaoui, Rhalem; Audigier, Romaric; Ambellouis, Sébastien; Capman, François; Benhadda, Hamid; Joudrier, Stéphanie; Sodoyer, David; Lamarque, Thierry

2015-10-01

Public transport security is one of the main priorities of the public authorities when fighting against crime and terrorism. In this context, there is a great demand for autonomous systems able to detect abnormal events such as violent acts aboard passenger cars and intrusions when the train is parked at the depot. To this end, we present an innovative approach which aims at providing efficient automatic event detection by fusing video and audio analytics and reducing the false alarm rate compared to classical stand-alone video detection. The multi-modal system is composed of two microphones and one camera and integrates onboard video and audio analytics and fusion capabilities. On the one hand, for detecting intrusion, the system relies on the fusion of "unusual" audio events detection with intrusion detections from video processing. The audio analysis consists in modeling the normal ambience and detecting deviation from the trained models during testing. This unsupervised approach is based on clustering of automatically extracted segments of acoustic features and statistical Gaussian Mixture Model (GMM) modeling of each cluster. The intrusion detection is based on the three-dimensional (3D) detection and tracking of individuals in the videos. On the other hand, for violent events detection, the system fuses unsupervised and supervised audio algorithms with video event detection. The supervised audio technique detects specific events such as shouts. A GMM is used to catch the formant structure of a shout signal. Video analytics use an original approach for detecting aggressive motion by focusing on erratic motion patterns specific to violent events. As data with violent events is not easily available, a normality model with structured motions from non-violent videos is learned for one-class classification. A fusion algorithm based on Dempster-Shafer's theory analyses the asynchronous detection outputs and computes the degree of belief of each probable event.

[Outbreak of pandemic virus (H1N1) 2009 in a residence for mentally disabled persons in Balearic Island, Spain].

PubMed

Giménez Duran, Jaume; Galmés Truyols, Antònia; Nicolau Riutort, Antonio; Reina Prieto, Jorge; Gallegos Álvarez, Maria de Carmen; Pareja Bezares, Antonio; Vanrell Berga, Juana María

2010-01-01

The flu season 2009-2010 has been shorter and less severe than expected. Since January 2010, influenza surveillance systems indicated rates of very low incidence of influenza without detection of virus circulation. In this context, a hospital reported a suspected outbreak of severe respiratory disease, the aetiology proved influenza A(H1N1)v. We describe the outbreak and public health measures for their control. Descriptive study of an outbreak of pandemic influenza virus in a residency home for mentally disabled. Establishment of active surveillance. The case definition of influenza was very sensitive to detect new cases early, treated early and minimize transmission. Steps were taken to contain the influenza virus infection. Among 38 cases detected 7 had serious complications(all of them with risk factors). There were no deaths. The overall attack rate was 35.2%. The first cases were workers. The residents were ill at the peak of the outbreak, and among workers the presentation was more dispersed. None of the workers and only three of residents had been vaccinated. Workers possibly have initiated and contributed to the maintenance of transmission. We emphasize the need to comply with vaccination recommendations, not just those with risk factors, but particularly for workers in contact with those.

Legionella longbeachae detected in an industrial cooling tower linked to a legionellosis outbreak, New Zealand, 2015; possible waterborne transmission?

PubMed

Thornley, C N; Harte, D J; Weir, R P; Allen, L J; Knightbridge, K J; Wood, P R T

2017-08-01

A legionellosis outbreak at an industrial site was investigated to identify and control the source. Cases were identified from disease notifications, workplace illness records, and from clinicians. Cases were interviewed for symptoms and risk factors and tested for legionellosis. Implicated environmental sources were sampled and tested for legionella. We identified six cases with Legionnaires' disease and seven with Pontiac fever; all had been exposed to aerosols from the cooling towers on the site. Nine cases had evidence of infection with either Legionella pneumophila serogroup (sg) 1 or Legionella longbeachae sg1; these organisms were also isolated from the cooling towers. There was 100% DNA sequence homology between cooling tower and clinical isolates of L. pneumophila sg1 using sequence-based typing analysis; no clinical L. longbeachae isolates were available to compare with environmental isolates. Routine monitoring of the towers prior to the outbreak failed to detect any legionella. Data from this outbreak indicate that L. pneumophila sg1 transmission occurred from the cooling towers; in addition, L. longbeachae transmission was suggested but remains unproven. L. longbeachae detection in cooling towers has not been previously reported in association with legionellosis outbreaks. Waterborne transmission should not be discounted in investigations for the source of L. longbeachae infection.

Optimizing the response to surveillance alerts in automated surveillance systems.

PubMed

Izadi, Masoumeh; Buckeridge, David L

2011-02-28

Although much research effort has been directed toward refining algorithms for disease outbreak alerting, considerably less attention has been given to the response to alerts generated from statistical detection algorithms. Given the inherent inaccuracy in alerting, it is imperative to develop methods that help public health personnel identify optimal policies in response to alerts. This study evaluates the application of dynamic decision making models to the problem of responding to outbreak detection methods, using anthrax surveillance as an example. Adaptive optimization through approximate dynamic programming is used to generate a policy for decision making following outbreak detection. We investigate the degree to which the model can tolerate noise theoretically, in order to keep near optimal behavior. We also evaluate the policy from our model empirically and compare it with current approaches in routine public health practice for investigating alerts. Timeliness of outbreak confirmation and total costs associated with the decisions made are used as performance measures. Using our approach, on average, 80 per cent of outbreaks were confirmed prior to the fifth day of post-attack with considerably less cost compared to response strategies currently in use. Experimental results are also provided to illustrate the robustness of the adaptive optimization approach and to show the realization of the derived error bounds in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Multiple exposures during a norovirus outbreak on a river-cruise sailing through Europe, 2006.

PubMed

Verhoef, L; Boxman, I L; Duizer, E; Rutjes, S A; Vennema, H; Friesema, I H M; de Roda Husman, A M; Koopmans, M

2008-06-12

In the summer of 2006, several cruise-related viral gastroenteritis outbreaks were reported in Europe. One report came from a river-cruise, belonging to a ship-owner who had two other ships with outbreaks. This situation warranted onsite investigation in order to identify a potential common source of infection. A retrospective cohort study was performed among 137 people on board. Epidemiological questionnaire data were analysed using logistic regression. Stool, food, water and surface samples were collected for norovirus detection. Norovirus GGII.4-2006b was responsible for 48 gastroenteritis cases on this ship as confirmed in six patients. Identical norovirus sequences were detected in stool samples, on surfaces and in tap water. Epidemiological and microbiological data indicated multiple exposures contributing to the outbreak. Microbiological results demonstrated person-to-person transmission to be clearly present. Epidemiological results indicated that consuming tap water was a risk factor; however, this could not be concluded definitively on the basis of the available data. A common source for all cruise-related outbreaks was unlikely. The ongoing outbreaks on this ship demonstrated that evidence based guidelines on effective disinfection strategies are needed.

Evidence for the presence of African swine fever virus in an endemic region of Western Kenya in the absence of any reported outbreak.

PubMed

Thomas, Lian F; Bishop, Richard P; Onzere, Cynthia; Mcintosh, Michael T; Lemire, Karissa A; de Glanville, William A; Cook, E Anne J; Fèvre, Eric M

2016-09-08

African swine fever (ASF), caused by African swine fever virus (ASFV), is a severe haemorrhagic disease of pigs, outbreaks of which can have a devastating impact upon commercial and small-holder pig production. Pig production in western Kenya is characterised by low-input, free-range systems practised by poor farmers keeping between two and ten pigs. These farmers are particularly vulnerable to the catastrophic loss of livestock assets experienced in an ASF outbreak. This study wished to expand our understanding of ASFV epidemiology during a period when no outbreaks were reported. Two hundred and seventy six whole blood samples were analysed using two independent conventional and real time PCR assays to detect ASFV. Despite no recorded outbreak of clinical ASF during this time, virus was detected in 90/277 samples analysed by conventional PCR and 142/209 samples analysed by qPCR. Genotyping of a sub-set of these samples indicated that the viruses associated with the positive samples were classified within genotype IX and that these strains were therefore genetically similar to the virus associated with the 2006/2007 ASF outbreaks in Kenya. The detection of ASFV viral DNA in a relatively high number of pigs delivered for slaughter during a period with no reported outbreaks provides support for two hypotheses, which are not mutually exclusive: (1) that virus prevalence may be over-estimated by slaughter-slab sampling, relative to that prevailing in the wider pig population; (2) that sub-clinical, chronically infected or recovered pigs may be responsible for persistence of the virus in endemic areas.

Detection of pathogenic viruses in sewage provided early warnings of hepatitis A virus and norovirus outbreaks.

PubMed

Hellmér, Maria; Paxéus, Nicklas; Magnius, Lars; Enache, Lucica; Arnholm, Birgitta; Johansson, Annette; Bergström, Tomas; Norder, Heléne

2014-11-01

Most persons infected with enterically transmitted viruses shed large amounts of virus in feces for days or weeks, both before and after onset of symptoms. Therefore, viruses causing gastroenteritis may be detected in wastewater, even if only a few persons are infected. In this study, the presence of eight pathogenic viruses (norovirus, astrovirus, rotavirus, adenovirus, Aichi virus, parechovirus, hepatitis A virus [HAV], and hepatitis E virus) was investigated in sewage to explore whether their identification could be used as an early warning of outbreaks. Samples of the untreated sewage were collected in proportion to flow at Ryaverket, Gothenburg, Sweden. Daily samples collected during every second week between January and May 2013 were pooled and analyzed for detection of viruses by concentration through adsorption to milk proteins and PCR. The largest amount of noroviruses was detected in sewage 2 to 3 weeks before most patients were diagnosed with this infection in Gothenburg. The other viruses were detected at lower levels. HAV was detected between weeks 5 and 13, and partial sequencing of the structural VP1protein identified three different strains. Two strains were involved in an ongoing outbreak in Scandinavia and were also identified in samples from patients with acute hepatitis A in Gothenburg during spring of 2013. The third strain was unique and was not detected in any patient sample. The method used may thus be a tool to detect incipient outbreaks of these viruses and provide early warning before the causative pathogens have been recognized in health care. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

Detection of Pathogenic Viruses in Sewage Provided Early Warnings of Hepatitis A Virus and Norovirus Outbreaks

PubMed Central

Hellmér, Maria; Paxéus, Nicklas; Magnius, Lars; Enache, Lucica; Arnholm, Birgitta; Johansson, Annette; Bergström, Tomas

2014-01-01

Most persons infected with enterically transmitted viruses shed large amounts of virus in feces for days or weeks, both before and after onset of symptoms. Therefore, viruses causing gastroenteritis may be detected in wastewater, even if only a few persons are infected. In this study, the presence of eight pathogenic viruses (norovirus, astrovirus, rotavirus, adenovirus, Aichi virus, parechovirus, hepatitis A virus [HAV], and hepatitis E virus) was investigated in sewage to explore whether their identification could be used as an early warning of outbreaks. Samples of the untreated sewage were collected in proportion to flow at Ryaverket, Gothenburg, Sweden. Daily samples collected during every second week between January and May 2013 were pooled and analyzed for detection of viruses by concentration through adsorption to milk proteins and PCR. The largest amount of noroviruses was detected in sewage 2 to 3 weeks before most patients were diagnosed with this infection in Gothenburg. The other viruses were detected at lower levels. HAV was detected between weeks 5 and 13, and partial sequencing of the structural VP1protein identified three different strains. Two strains were involved in an ongoing outbreak in Scandinavia and were also identified in samples from patients with acute hepatitis A in Gothenburg during spring of 2013. The third strain was unique and was not detected in any patient sample. The method used may thus be a tool to detect incipient outbreaks of these viruses and provide early warning before the causative pathogens have been recognized in health care. PMID:25172863

Unsupervised segmentation of lungs from chest radiographs

NASA Astrophysics Data System (ADS)

Ghosh, Payel; Antani, Sameer K.; Long, L. Rodney; Thoma, George R.

2012-03-01

This paper describes our preliminary investigations for deriving and characterizing coarse-level textural regions present in the lung field on chest radiographs using unsupervised grow-cut (UGC), a cellular automaton based unsupervised segmentation technique. The segmentation has been performed on a publicly available data set of chest radiographs. The algorithm is useful for this application because it automatically converges to a natural segmentation of the image from random seed points using low-level image features such as pixel intensity values and texture features. Our goal is to develop a portable screening system for early detection of lung diseases for use in remote areas in developing countries. This involves developing automated algorithms for screening x-rays as normal/abnormal with a high degree of sensitivity, and identifying lung disease patterns on chest x-rays. Automatically deriving and quantitatively characterizing abnormal regions present in the lung field is the first step toward this goal. Therefore, region-based features such as geometrical and pixel-value measurements were derived from the segmented lung fields. In the future, feature selection and classification will be performed to identify pathological conditions such as pulmonary tuberculosis on chest radiographs. Shape-based features will also be incorporated to account for occlusions of the lung field and by other anatomical structures such as the heart and diaphragm.

Improved Anomaly Detection using Integrated Supervised and Unsupervised Processing

NASA Astrophysics Data System (ADS)

Hunt, B.; Sheppard, D. G.; Wetterer, C. J.

There are two broad technologies of signal processing applicable to space object feature identification using nonresolved imagery: supervised processing analyzes a large set of data for common characteristics that can be then used to identify, transform, and extract information from new data taken of the same given class (e.g. support vector machine); unsupervised processing utilizes detailed physics-based models that generate comparison data that can then be used to estimate parameters presumed to be governed by the same models (e.g. estimation filters). Both processes have been used in non-resolved space object identification and yield similar results yet arrived at using vastly different processes. The goal of integrating the results of the two is to seek to achieve an even greater performance by building on the process diversity. Specifically, both supervised processing and unsupervised processing will jointly operate on the analysis of brightness (radiometric flux intensity) measurements reflected by space objects and observed by a ground station to determine whether a particular day conforms to a nominal operating mode (as determined from a training set) or exhibits anomalous behavior where a particular parameter (e.g. attitude, solar panel articulation angle) has changed in some way. It is demonstrated in a variety of different scenarios that the integrated process achieves a greater performance than each of the separate processes alone.

Advanced Treatment Monitoring for Olympic-Level Athletes Using Unsupervised Modeling Techniques

PubMed Central

Siedlik, Jacob A.; Bergeron, Charles; Cooper, Michael; Emmons, Russell; Moreau, William; Nabhan, Dustin; Gallagher, Philip; Vardiman, John P.

2016-01-01

Context Analysis of injury and illness data collected at large international competitions provides the US Olympic Committee and the national governing bodies for each sport with information to best prepare for future competitions. Research in which authors have evaluated medical contacts to provide the expected level of medical care and sports medicine services at international competitions is limited. Objective To analyze the medical-contact data for athletes, staff, and coaches who participated in the 2011 Pan American Games in Guadalajara, Mexico, using unsupervised modeling techniques to identify underlying treatment patterns. Design Descriptive epidemiology study. Setting Pan American Games. Patients or Other Participants A total of 618 US athletes (337 males, 281 females) participated in the 2011 Pan American Games. Main Outcome Measure(s) Medical data were recorded from the injury-evaluation and injury-treatment forms used by clinicians assigned to the central US Olympic Committee Sport Medicine Clinic and satellite locations during the operational 17-day period of the 2011 Pan American Games. We used principal components analysis and agglomerative clustering algorithms to identify and define grouped modalities. Lift statistics were calculated for within-cluster subgroups. Results Principal component analyses identified 3 components, accounting for 72.3% of the variability in datasets. Plots of the principal components showed that individual contacts focused on 4 treatment clusters: massage, paired manipulation and mobilization, soft tissue therapy, and general medical. Conclusions Unsupervised modeling techniques were useful for visualizing complex treatment data and provided insights for improved treatment modeling in athletes. Given its ability to detect clinically relevant treatment pairings in large datasets, unsupervised modeling should be considered a feasible option for future analyses of medical-contact data from international competitions. PMID:26794628

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.

Early detection and control of an Acinetobacter baumannii multi-resistant outbreak in a hospital in Quito, Ecuador.

PubMed

Cartelle Gestal, Monica; Zurita, Jeannete; Gualpa, Gabriela; Gonzalez, Cecibel; Paz Y Mino, Ariane

2016-12-30

Acinetobacter baumannii (ABA) is an important opportunistic pathogen associated with high mortality rates in intensive care units (ICUs). An outbreak in the ICU of a secondary-level hospital in Quito, Ecuador, occurred during April and May 2015 and was successfully controlled. Enterobacterial repetitive intergenic consensus polymerase chain reaction (ERIC-PCR) and repetitive element palindromic (REP)-PCR was conducted on all isolates recovered from patients, as well as environmental samples, to confirm the presence of an outbreak. A case-control study was conducted by comparing the clinical histories of the affected patients and of control patients present in the ICU during the outbreak period who did not present a positive culture for ABA. Five patients were infected and two were colonized with the same clonal strain of ABA, which was also identified on the stethoscope and a monitor associated with an isolation room. Statistical analysis of case histories did not identify any additional risk factors, but the outbreak was initiated by one patient in the isolation room of the ICU who was infected with the outbreak strain. All patients who ocupied that room after the index case tested positive for at least one culture of ABA. The outbreak strain was found on the stethoscope, and a subclone was found on the monitor of that room. Having access to basic equipment will enable well-trained professionals to rapidly detect and initiate the control process of an outbreak, saving lives and money spent on nosocomial infection treatments.

Insights into quasar UV spectra using unsupervised clustering analysis

NASA Astrophysics Data System (ADS)

Tammour, A.; Gallagher, S. C.; Daley, M.; Richards, G. T.

2016-06-01

Machine learning techniques can provide powerful tools to detect patterns in multidimensional parameter space. We use K-means - a simple yet powerful unsupervised clustering algorithm which picks out structure in unlabelled data - to study a sample of quasar UV spectra from the Quasar Catalog of the 10th Data Release of the Sloan Digital Sky Survey (SDSS-DR10) of Paris et al. Detecting patterns in large data sets helps us gain insights into the physical conditions and processes giving rise to the observed properties of quasars. We use K-means to find clusters in the parameter space of the equivalent width (EW), the blue- and red-half-width at half-maximum (HWHM) of the Mg II 2800 Å line, the C IV 1549 Å line, and the C III] 1908 Å blend in samples of broad absorption line (BAL) and non-BAL quasars at redshift 1.6-2.1. Using this method, we successfully recover correlations well-known in the UV regime such as the anti-correlation between the EW and blueshift of the C IV emission line and the shape of the ionizing spectra energy distribution (SED) probed by the strength of He II and the Si III]/C III] ratio. We find this to be particularly evident when the properties of C III] are used to find the clusters, while those of Mg II proved to be less strongly correlated with the properties of the other lines in the spectra such as the width of C IV or the Si III]/C III] ratio. We conclude that unsupervised clustering methods (such as K-means) are powerful methods for finding `natural' binning boundaries in multidimensional data sets and discuss caveats and future work.

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.

[Epidemiological characteristics of influenza outbreaks in China, 2005-2013].

PubMed

Li, Ming; Feng, Luzhao; Cao, Yu; Peng, Zhibin; Yu, Hongjie

2015-07-01

To understand the epidemiological characteristics of influenza outbreaks in China from 2005 to 2013. The data of influenza-like illness outbreaks involving 10 or more cases were collected through Public Health Emergency Management Information System and National Influenza Surveillance Information System in China, and the influenza outbreaks were identified according to the laboratory detection results. Descriptive epidemiological analysis was conducted to understand the type/subtype of influenza virus and outbreak time, area, place and extent. From 2005 to 2013, a total of 3 252 influenza-like illness outbreaks were reported in the mainland of China, in which 2 915 influenza outbreaks were laboratory confirmed, and influenza A (H1N1) pdm09 virus and influenza B virus were predominant. More influenza outbreaks were reported in the influenza A (H1N1) pandemic during 2009-2010. Influenza outbreaks mainly occurred during winter-spring, and less influenza outbreaks occurred in winter and summer vacations of schools. More influenza outbreaks were reported in southern provinces, accounting for 79% of the total. Influenza outbreaks mainly occurred in primary and middle schools, where 2 763 outbreaks were reported, accounting for 85% of the total. Average 30-99 people were involved in an outbreak. A large number of influenza outbreaks occur during influenza season every year in China, the predominant virus type or subtype varies with season. Primary and middle schools are mainly affected by influenza outbreaks.

Calibration model maintenance in melamine resin production: Integrating drift detection, smart sample selection and model adaptation.

PubMed

Nikzad-Langerodi, Ramin; Lughofer, Edwin; Cernuda, Carlos; Reischer, Thomas; Kantner, Wolfgang; Pawliczek, Marcin; Brandstetter, Markus

2018-07-12

The physico-chemical properties of Melamine Formaldehyde (MF) based thermosets are largely influenced by the degree of polymerization (DP) in the underlying resin. On-line supervision of the turbidity point by means of vibrational spectroscopy has recently emerged as a promising technique to monitor the DP of MF resins. However, spectroscopic determination of the DP relies on chemometric models, which are usually sensitive to drifts caused by instrumental and/or sample-associated changes occurring over time. In order to detect the time point when drifts start causing prediction bias, we here explore a universal drift detector based on a faded version of the Page-Hinkley (PH) statistic, which we test in three data streams from an industrial MF resin production process. We employ committee disagreement (CD), computed as the variance of model predictions from an ensemble of partial least squares (PLS) models, as a measure for sample-wise prediction uncertainty and use the PH statistic to detect changes in this quantity. We further explore supervised and unsupervised strategies for (semi-)automatic model adaptation upon detection of a drift. For the former, manual reference measurements are requested whenever statistical thresholds on Hotelling's T 2 and/or Q-Residuals are violated. Models are subsequently re-calibrated using weighted partial least squares in order to increase the influence of newer samples, which increases the flexibility when adapting to new (drifted) states. Unsupervised model adaptation is carried out exploiting the dual antecedent-consequent structure of a recently developed fuzzy systems variant of PLS termed FLEXFIS-PLS. In particular, antecedent parts are updated while maintaining the internal structure of the local linear predictors (i.e. the consequents). We found improved drift detection capability of the CD compared to Hotelling's T 2 and Q-Residuals when used in combination with the proposed PH test. Furthermore, we found that active selection of samples by active learning (AL) used for subsequent model adaptation is advantageous compared to passive (random) selection in case that a drift leads to persistent prediction bias allowing more rapid adaptation at lower reference measurement rates. Fully unsupervised adaptation using FLEXFIS-PLS could improve predictive accuracy significantly for light drifts but was not able to fully compensate for prediction bias in case of significant lack of fit w.r.t. the latent variable space. Copyright © 2018 Elsevier B.V. All rights reserved.

Unsupervised laparoscopic appendicectomy by surgical trainees is safe and time-effective.

PubMed

Wong, Kenneth; Duncan, Tristram; Pearson, Andrew

2007-07-01

Open appendicectomy is the traditional standard treatment for appendicitis. Laparoscopic appendicectomy is perceived as a procedure with greater potential for complications and longer operative times. This paper examines the hypothesis that unsupervised laparoscopic appendicectomy by surgical trainees is a safe and time-effective valid alternative. Medical records, operating theatre records and histopathology reports of all patients undergoing laparoscopic and open appendicectomy over a 15-month period in two hospitals within an area health service were retrospectively reviewed. Data were analysed to compare patient features, pathology findings, operative times, complications, readmissions and mortality between laparoscopic and open groups and between unsupervised surgical trainee operators versus consultant surgeon operators. A total of 143 laparoscopic and 222 open appendicectomies were reviewed. Unsupervised trainees performed 64% of the laparoscopic appendicectomies and 55% of the open appendicectomies. There were no significant differences in complication rates, readmissions, mortality and length of stay between laparoscopic and open appendicectomy groups or between trainee and consultant surgeon operators. Conversion rates (laparoscopic to open approach) were similar for trainees and consultants. Unsupervised senior surgical trainees did not take significantly longer to perform laparoscopic appendicectomy when compared to unsupervised trainee-performed open appendicectomy. Unsupervised laparoscopic appendicectomy by surgical trainees is safe and time-effective.

Interagency Coordination in the Case of an Intentional Agroterrorist Incident

DTIC Science & Technology

2009-05-11

and working groups; development of a National Veterinary Stockpile of vaccines needed to respond to animal diseases; and funding of research...outbreak or an intentional incident. They include lack of personnel able to recognize a foreign animal disease outbreak, difficulty with vaccination and... vaccination stockpiling, and difficulty detecting a covert attack and differentiating it from a natural outbreak with the current surveillance and

Novel use of flu surveillance data: Evaluating potential of sentinel populations for early detection of influenza outbreaks

DOE PAGES

Daughton, Ashlynn R.; Velappan, Nileena; Abeyta, Esteban; ...

2016-07-08

Influenza causes significant morbidity and mortality each year, with 2–8% of weekly outpatient visits around the United States for influenza-like-illness (ILI) during the peak of the season. Effective use of existing flu surveillance data allows officials to understand and predict current flu outbreaks and can contribute to reductions in influenza morbidity and mortality. Previous work used the 2009–2010 influenza season to investigate the possibility of using existing military and civilian surveillance systems to improve early detection of flu outbreaks. Results suggested that civilian surveillance could help predict outbreak trajectory in local military installations. To further test that hypothesis, we comparemore » pairs of civilian and military outbreaks in seven locations between 2000 and 2013. We find no predictive relationship between outbreak peaks or time series of paired outbreaks. This larger study does not find evidence to support the hypothesis that civilian data can be used as sentinel surveillance for military installations. We additionally investigate the effect of modifying the ILI case definition between the standard Department of Defense definition, a more specific definition proposed in literature, and confirmed Influenza A. We find that case definition heavily impacts results. In conclusion, this study thus highlights the importance of careful selection of case definition, and appropriate consideration of case definition in the interpretation of results.« less

Novel use of flu surveillance data: Evaluating potential of sentinel populations for early detection of influenza outbreaks

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

Daughton, Ashlynn R.; Velappan, Nileena; Abeyta, Esteban

Influenza causes significant morbidity and mortality each year, with 2–8% of weekly outpatient visits around the United States for influenza-like-illness (ILI) during the peak of the season. Effective use of existing flu surveillance data allows officials to understand and predict current flu outbreaks and can contribute to reductions in influenza morbidity and mortality. Previous work used the 2009–2010 influenza season to investigate the possibility of using existing military and civilian surveillance systems to improve early detection of flu outbreaks. Results suggested that civilian surveillance could help predict outbreak trajectory in local military installations. To further test that hypothesis, we comparemore » pairs of civilian and military outbreaks in seven locations between 2000 and 2013. We find no predictive relationship between outbreak peaks or time series of paired outbreaks. This larger study does not find evidence to support the hypothesis that civilian data can be used as sentinel surveillance for military installations. We additionally investigate the effect of modifying the ILI case definition between the standard Department of Defense definition, a more specific definition proposed in literature, and confirmed Influenza A. We find that case definition heavily impacts results. In conclusion, this study thus highlights the importance of careful selection of case definition, and appropriate consideration of case definition in the interpretation of results.« less

Novel Use of Flu Surveillance Data: Evaluating Potential of Sentinel Populations for Early Detection of Influenza Outbreaks

PubMed Central

Velappan, Nileena; Abeyta, Esteban; Priedhorsky, Reid; Deshpande, Alina

2016-01-01

Influenza causes significant morbidity and mortality each year, with 2–8% of weekly outpatient visits around the United States for influenza-like-illness (ILI) during the peak of the season. Effective use of existing flu surveillance data allows officials to understand and predict current flu outbreaks and can contribute to reductions in influenza morbidity and mortality. Previous work used the 2009–2010 influenza season to investigate the possibility of using existing military and civilian surveillance systems to improve early detection of flu outbreaks. Results suggested that civilian surveillance could help predict outbreak trajectory in local military installations. To further test that hypothesis, we compare pairs of civilian and military outbreaks in seven locations between 2000 and 2013. We find no predictive relationship between outbreak peaks or time series of paired outbreaks. This larger study does not find evidence to support the hypothesis that civilian data can be used as sentinel surveillance for military installations. We additionally investigate the effect of modifying the ILI case definition between the standard Department of Defense definition, a more specific definition proposed in literature, and confirmed Influenza A. We find that case definition heavily impacts results. This study thus highlights the importance of careful selection of case definition, and appropriate consideration of case definition in the interpretation of results. PMID:27391232

Gastroenteritis outbreak caused by waterborne norovirus at a New Zealand ski resort.

PubMed

Hewitt, Joanne; Bell, Derek; Simmons, Greg C; Rivera-Aban, Malet; Wolf, Sandro; Greening, Gail E

2007-12-01

In July 2006, public health services investigated an outbreak of acute gastroenteritis among staff and visitors of a popular ski resort in southern New Zealand. The source of the outbreak was a drinking water supply contaminated by human sewage. The virological component of the investigation played a major role in confirming the source of the outbreak. Drinking water, source stream water, and 31 fecal specimens from gastroenteritis outbreak cases were analyzed for the presence of norovirus (NoV). Water samples were concentrated by ultrafiltration, and real-time reverse transcription-PCR (RT-PCR) was used for rapid detection of NoV from both water and fecal samples. The implicated NoV strain was further characterized by DNA sequencing. NoV genogroup GI/5 was identified in water samples and linked case fecal specimens, providing clear evidence of the predominant pathogen and route of exposure. A retrospective cohort study demonstrated that staff who consumed drinking water from the resort supply were twice as likely to have gastroenteritis than those who did not. This is the first time that an outbreak of gastroenteritis in New Zealand has been conclusively linked to NoV detected in a community water supply. To our knowledge, this is the first report of the use of ultrafiltration combined with quantitative real-time RT-PCR and DNA sequencing for investigation of a waterborne NoV outbreak.

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.

Molecular characterization of Hepatitis A virus causing an outbreak among Thai navy recruits.

PubMed

Theamboonlers, A; Rianthavorn, P; Jiamsiri, S; Kumthong, S; Silaporn, P; Thongmee, C; Poovorawan, Y

2009-12-01

Hepatitis A virus (HAV) infection is a communicable disease, typically transmitted by faecal-oral contamination. HAV outbreaks usually occur in endemic areas. We report an outbreak of HAV from June to July, 2008 among Thai navy recruits who had received training at the Sattahip Navy Base, Chonburi province, Thailand. Upon conclusion of the training, the recruits were deployed to serve at several navy bases across the country. Secondary cases of HAV infection were reported among military personnel from these navy bases. To elucidate origin and distribution of these outbreaks, we characterized the genome and genotype of HAV isolated from the different navy bases. Sera and stool from the subjects were tested for antiHAV IgM, antiHAV IgG and HAV RNA. Subsequently, molecular characterization of HAV was performed by nucleotide sequencing of the VP1-P2A region, BLAST/FASTA and phylogenetic analysis. HAV RNA was detected in specimens obtained from different areas. All isolated strains clustered in the same lineage and belonged to genotype 1A. They shared nearly 100% genome homology indicating a single point source of this outbreak. This study provides essential baseline data as a reference for genetic analysis of HAV strains causing future outbreaks. Early detection of HAV infection and identification of the source by using molecular characterization and prompt preventive measures will hopefully prevent further outbreaks.

Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data

DOE PAGES

McCann, Cooper; Repasky, Kevin S.; Morin, Mikindra; ...

2017-05-23

Hyperspectral image analysis has benefited from an array of methods that take advantage of the increased spectral depth compared to multispectral sensors; however, the focus of these developments has been on supervised classification methods. Lack of a priori knowledge regarding land cover characteristics can make unsupervised classification methods preferable under certain circumstances. An unsupervised classification technique is presented in this paper that utilizes physically relevant basis functions to model the reflectance spectra. These fit parameters used to generate the basis functions allow clustering based on spectral characteristics rather than spectral channels and provide both noise and data reduction. Histogram splittingmore » of the fit parameters is then used as a means of producing an unsupervised classification. Unlike current unsupervised classification techniques that rely primarily on Euclidian distance measures to determine similarity, the unsupervised classification technique uses the natural splitting of the fit parameters associated with the basis functions creating clusters that are similar in terms of physical parameters. The data set used in this work utilizes the publicly available data collected at Indian Pines, Indiana. This data set provides reference data allowing for comparisons of the efficacy of different unsupervised data analysis. The unsupervised histogram splitting technique presented in this paper is shown to be better than the standard unsupervised ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. Finally, this improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique compared to 15.8/28.5 for ISODATA.« less

Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data

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

McCann, Cooper; Repasky, Kevin S.; Morin, Mikindra

Hyperspectral image analysis has benefited from an array of methods that take advantage of the increased spectral depth compared to multispectral sensors; however, the focus of these developments has been on supervised classification methods. Lack of a priori knowledge regarding land cover characteristics can make unsupervised classification methods preferable under certain circumstances. An unsupervised classification technique is presented in this paper that utilizes physically relevant basis functions to model the reflectance spectra. These fit parameters used to generate the basis functions allow clustering based on spectral characteristics rather than spectral channels and provide both noise and data reduction. Histogram splittingmore » of the fit parameters is then used as a means of producing an unsupervised classification. Unlike current unsupervised classification techniques that rely primarily on Euclidian distance measures to determine similarity, the unsupervised classification technique uses the natural splitting of the fit parameters associated with the basis functions creating clusters that are similar in terms of physical parameters. The data set used in this work utilizes the publicly available data collected at Indian Pines, Indiana. This data set provides reference data allowing for comparisons of the efficacy of different unsupervised data analysis. The unsupervised histogram splitting technique presented in this paper is shown to be better than the standard unsupervised ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. Finally, this improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique compared to 15.8/28.5 for ISODATA.« less

An evaluation of asymptomatic Dengue infections among blood donors during the 2014 Dengue outbreak in Guangzhou, China.

PubMed

Liao, Qiao; Shan, Zhengang; Wang, Min; Huang, Jieting; Xu, Ru; Huang, Ke; Tang, Xi; Zhang, Weiyun; Nelson, Kenrad; Li, Chengyao; Fu, Yongshui; Rong, Xia

2017-11-01

In 2014, an outbreak of dengue virus (DENV) infection led to 45 171 clinical cases diagnosed in Guangdong province, Southern China. However, the potential risk of blood donors asymptomatically infected with DENV has not been evaluated . In the current study we detected anti-DENV IgG antibody and RNA in volunteer Chinese blood donors. We found that anti-DENV IgG antibody was positively detected in 3.4% (51/1500) and two donors were detected as being DENV RNA positive out of 3000 blood samples. We concluded that the presence of potential DENV in blood donors might be potential risk for blood safety. Therefore, screening for DENV infection should be considered in blood donations during a period of dengue outbreak in high epidemic area of China. © 2017 Wiley Periodicals, Inc.

A new method of real-time detection of changes in periodic data stream

NASA Astrophysics Data System (ADS)

Lyu, Chen; Lu, Guoliang; Cheng, Bin; Zheng, Xiangwei

2017-07-01

The change point detection in periodic time series is much desirable in many practical usages. We present a novel algorithm for this task, which includes two phases: 1) anomaly measure- on the basis of a typical regression model, we propose a new computation method to measure anomalies in time series which does not require any reference data from other measurement(s); 2) change detection- we introduce a new martingale test for detection which can be operated in an unsupervised and nonparametric way. We have conducted extensive experiments to systematically test our algorithm. The results make us believe that our algorithm can be directly applicable in many real-world change-point-detection applications.

Learned filters for object detection in multi-object visual tracking

NASA Astrophysics Data System (ADS)

Stamatescu, Victor; Wong, Sebastien; McDonnell, Mark D.; Kearney, David

2016-05-01

We investigate the application of learned convolutional filters in multi-object visual tracking. The filters were learned in both a supervised and unsupervised manner from image data using artificial neural networks. This work follows recent results in the field of machine learning that demonstrate the use learned filters for enhanced object detection and classification. Here we employ a track-before-detect approach to multi-object tracking, where tracking guides the detection process. The object detection provides a probabilistic input image calculated by selecting from features obtained using banks of generative or discriminative learned filters. We present a systematic evaluation of these convolutional filters using a real-world data set that examines their performance as generic object detectors.

Detection of human norovirus from frozen raspberries in a cluster of gastroenteritis outbreaks.

PubMed

Maunula, L; Roivainen, M; Keränen, M; Mäkela, S; Söderberg, K; Summa, M; von Bonsdorff, C H; Lappalainen, M; Korhonen, T; Kuusi, M; Niskanen, T

2009-12-10

We describe a cluster of norovirus outbreaks affecting about 200 people in Southern Finland in September and October 2009. All outbreaks occurred after consumption of imported raspberries from the same batch intended for the catering sector. Human norovirus genotype GI.4 was found in frozen raspberries. The berries were served in toppings of cakes in separate catering settings or mixed in curd cheese as a snack for children in a daycare center. The relative risk for consumption of the berry dish was 3.0 (p

Multinational outbreak of Salmonella Enteritidis infection during an international youth ice hockey competition in Riga, Latvia, preliminary report, March and April 2015.

PubMed

Pesola, A K; Parn, T; Huusko, S; Perevosčikovs, J; Ollgren, J; Salmenlinna, S; Lienemann, T; Gossner, C; Danielsson, N; Rimhanen-Finne, R

2015-05-21

A multinational outbreak of salmonellosis linked to the Riga Cup 2015 junior ice-hockey competition was detected by the Finnish health authorities in mid-April and immediately notified at the European Union level. This prompted an international outbreak investigation supported by the European Centre for Disease Prevention and Control. As of 8 May 2015, seven countries have reported 214 confirmed and suspected cases, among which 122 from Finland. The search for the source of the outbreak is ongoing.

Unsupervised Categorization in a Sample of Children with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Edwards, Darren J.; Perlman, Amotz; Reed, Phil

2012-01-01

Studies of supervised Categorization have demonstrated limited Categorization performance in participants with autism spectrum disorders (ASD), however little research has been conducted regarding unsupervised Categorization in this population. This study explored unsupervised Categorization using two stimulus sets that differed in their…

Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval.

PubMed

Zhang, Haofeng; Liu, Li; Long, Yang; Shao, Ling

2018-04-01

In order to achieve efficient similarity searching, hash functions are designed to encode images into low-dimensional binary codes with the constraint that similar features will have a short distance in the projected Hamming space. Recently, deep learning-based methods have become more popular, and outperform traditional non-deep methods. However, without label information, most state-of-the-art unsupervised deep hashing (DH) algorithms suffer from severe performance degradation for unsupervised scenarios. One of the main reasons is that the ad-hoc encoding process cannot properly capture the visual feature distribution. In this paper, we propose a novel unsupervised framework that has two main contributions: 1) we convert the unsupervised DH model into supervised by discovering pseudo labels; 2) the framework unifies likelihood maximization, mutual information maximization, and quantization error minimization so that the pseudo labels can maximumly preserve the distribution of visual features. Extensive experiments on three popular data sets demonstrate the advantages of the proposed method, which leads to significant performance improvement over the state-of-the-art unsupervised hashing algorithms.

Characterization of Viral Load, Viability and Persistence of Influenza A Virus in Air and on Surfaces of Swine Production Facilities.

PubMed

Neira, Victor; Rabinowitz, Peter; Rendahl, Aaron; Paccha, Blanca; Gibbs, Shawn G; Torremorell, Montserrat

2016-01-01

Indirect transmission of influenza A virus (IAV) in swine is poorly understood and information is lacking on levels of environmental exposure encountered by swine and people during outbreaks of IAV in swine barns. We characterized viral load, viability and persistence of IAV in air and on surfaces during outbreaks in swine barns. IAV was detected in pigs, air and surfaces from five confirmed outbreaks with 48% (47/98) of oral fluid, 38% (32/84) of pen railing and 43% (35/82) of indoor air samples testing positive by IAV RT-PCR. IAV was isolated from air and oral fluids yielding a mixture of subtypes (H1N1, H1N2 and H3N2). Detection of IAV RNA from air was sustained during the outbreaks with maximum levels estimated between 7 and 11 days from reported onset. Our results indicate that during outbreaks of IAV in swine, aerosols and surfaces in barns contain significant levels of IAV potentially representing an exposure hazard to both swine and people.

Imported dengue from 2013 Angola outbreak: Not just serotype 1 was detected.

PubMed

Abreu, Cândida; Silva-Pinto, André; Lazzara, Daniela; Sobrinho-Simões, Joana; Guimarães, João Tiago; Sarmento, António

2016-06-01

All the reports from Angola's 2013 dengue outbreak revealed serotype 1. However, previously dengue serotypes 1-4 have been reported in Africa and in 2014 serotype 4 was reported in Angola. To report dengue serotypes in patients returning from Angola during 2013 outbreak. Retrospective, cross-sectional study. We serotyped the dengue by an in house Polymerase Chain Reaction technique in randomly selected cases. From the 2013 Angola's dengue outbreak we treated 47 adult patients. None had history of past dengue. A combo kit test for dengue revealed positive NS1 antigen in 39 and IgM antibodies in 8. From 17 randomly patients tested by RNA Real Time-PCR, 11 were positive: 7 for DENV-1, 2 for DENV-2, 1 for DENV-3 (co-infected with DENV-1) and 1 for DENV-4. None had a complicated or fatal evolution. Unlike previous reports the 4 serotypes were detected, and this resulted in a different epidemiological situation, raising the risk of future outbreaks of severe dengue. Copyright © 2016 Elsevier B.V. All rights reserved.

Echovirus 30 meningitis epidemic followed by an outbreak-specific RT-qPCR.

PubMed

Österback, Riikka; Kalliokoski, Teemu; Lähdesmäki, Tuire; Peltola, Ville; Ruuskanen, Olli; Waris, Matti

2015-08-01

An outbreak of enteroviral aseptic meningitis emerged in Southwestern Finland in August 2009. The same enterovirus reappeared with increasing incidence of meningitis in other parts of Finland in 2010. To identify the incidence and molecular epidemiology of enteroviral meningitis outbreak. The causative agent was identified as echovirus 30 (E-30) by sequencing partial viral protein 1 capsid genome, and a virus type-specific RT-qPCR was set up for sensitive detection of the virus in cerebrospinal fluid specimens. Enterovirus positive CSF specimens were subjected to the E-30-specific assay to investigate this unusual occurrence of aseptic meningitis and facilitate case confirmation during the outbreaks between August 2009 and September 2010. E-30 was detected in 106 (72%) enterovirus positive cerebrospinal fluid specimens. All the meningitis cases in 2009 and most of them in 2010 were among adolescents and several were members of sport teams. Between August 2009 and September 2010, E-30 caused an extensive outbreak with two peaks in Finland. Type-specific RT-PCR allowed rapid diagnostic follow-up of the epidemic. Copyright © 2015 Elsevier B.V. All rights reserved.

Mild Illness during Outbreak of Shiga Toxin-Producing Escherichia coli O157 Infections Associated with Agricultural Show, Australia.

PubMed

Vasant, Bhakti R; Stafford, Russell J; Jennison, Amy V; Bennett, Sonya M; Bell, Robert J; Doyle, Christine J; Young, Jeannette R; Vlack, Susan A; Titmus, Paul; El Saadi, Debra; Jarvinen, Kari A J; Coward, Patricia; Barrett, Janine; Staples, Megan; Graham, Rikki M A; Smith, Helen V; Lambert, Stephen B

2017-10-01

During a large outbreak of Shiga toxin-producing Escherichia coli illness associated with an agricultural show in Australia, we used whole-genome sequencing to detect an IS1203v insertion in the Shiga toxin 2c subunit A gene of Shiga toxin-producing E. coli. Our study showed that clinical illness was mild, and hemolytic uremic syndrome was not detected.

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...

Spatiotemporal information during unsupervised learning enhances viewpoint invariant object recognition

PubMed Central

Tian, Moqian; Grill-Spector, Kalanit

2015-01-01

Recognizing objects is difficult because it requires both linking views of an object that can be different and distinguishing objects with similar appearance. Interestingly, people can learn to recognize objects across views in an unsupervised way, without feedback, just from the natural viewing statistics. However, there is intense debate regarding what information during unsupervised learning is used to link among object views. Specifically, researchers argue whether temporal proximity, motion, or spatiotemporal continuity among object views during unsupervised learning is beneficial. Here, we untangled the role of each of these factors in unsupervised learning of novel three-dimensional (3-D) objects. We found that after unsupervised training with 24 object views spanning a 180° view space, participants showed significant improvement in their ability to recognize 3-D objects across rotation. Surprisingly, there was no advantage to unsupervised learning with spatiotemporal continuity or motion information than training with temporal proximity. However, we discovered that when participants were trained with just a third of the views spanning the same view space, unsupervised learning via spatiotemporal continuity yielded significantly better recognition performance on novel views than learning via temporal proximity. These results suggest that while it is possible to obtain view-invariant recognition just from observing many views of an object presented in temporal proximity, spatiotemporal information enhances performance by producing representations with broader view tuning than learning via temporal association. Our findings have important implications for theories of object recognition and for the development of computational algorithms that learn from examples. PMID:26024454

Modeling the spread of polio in an IPV-vaccinated population: lessons learned from the 2013 silent outbreak in southern Israel.

PubMed

Yaari, Rami; Kaliner, Ehud; Grotto, Itamar; Katriel, Guy; Moran-Gilad, Jacob; Sofer, Danit; Mendelson, Ella; Miller, Elizabeth; Huppert, Amit; Anis, E; Kopel, E; Manor, Y; Mor, O; Shulman, L; Singer, R; Weil, M

2016-06-23

Polio eradication is an extraordinary globally coordinated health program in terms of its magnitude and reach, leading to the elimination of wild poliovirus (WPV) in most parts of the world. In 2013, a silent outbreak of WPV was detected in Israel, a country using an inactivated polio vaccine (IPV) exclusively since 2005. The outbreak was detected using environmental surveillance (ES) of sewage reservoirs. Stool surveys indicated the outbreak to be restricted mainly to children under the age of 10 in the Bedouin population of southern Israel. In order to curtail the outbreak, a nationwide vaccination campaign using oral polio vaccine (OPV) was conducted, targeting all children under 10. A transmission model, fitted to the results of the stool surveys, with additional conditions set by the ES measurements, was used to evaluate the prevalence of WPV in Bedouin children and the effectiveness of the vaccination campaign. Employing the parameter estimates of the model fitting, the model was used to investigate the effect of alternative timings, coverages and dosages of the OPV campaign on the outcome of the outbreak. The mean estimate for the mean reproductive number was 1.77 (95 % credible interval, 1.46-2.30). With seasonal variation, the reproductive number maximum range was between zero and six. The mean estimate for the mean infectious periods was 16.8 (8.6-24.9) days. The modeling indicates the OPV campaign was effective in curtailing the outbreak. The mean estimate for the attack rate in Bedouin children under 10 at the end of 2014 was 42 % (22-65 %), whereas without the campaign the mean projected attack rate was 57 % (35-74 %). The campaign also likely shortened the duration of the outbreak by a mean estimate of 309 (2-846) days. A faster initiation of the OPV campaign could have reduced the incidence of WPV even if a lower coverage was reached, at the risk of prolonging the outbreak. OPV campaigns are essential for interrupting WPV transmission, even in a developed country setting with a high coverage of IPV. In this setting, establishing ES of WPV circulation is particularly crucial for early detection and containment of an outbreak.

African Swine Fever in Uganda: Qualitative Evaluation of Three Surveillance Methods with Implications for Other Resource-Poor Settings.

PubMed

Chenais, Erika; Sternberg-Lewerin, Susanna; Boqvist, Sofia; Emanuelson, Ulf; Aliro, Tonny; Tejler, Emma; Cocca, Giampaolo; Masembe, Charles; Ståhl, Karl

2015-01-01

Animal diseases impact negatively on households and on national economies. In low-income countries, this pertains especially to socio-economic effects on household level. To control animal diseases and mitigate their impact, it is necessary to understand the epidemiology of the disease in its local context. Such understanding, gained through disease surveillance, is often lacking in resource-poor settings. Alternative surveillance methods have been developed to overcome some of the hurdles obstructing surveillance. The objective of this study was to evaluate and qualitatively compare three methods for surveillance of acute infectious diseases using African swine fever in northern Uganda as an example. Report-driven outbreak investigations, participatory rural appraisals (PRAs), and a household survey using a smartphone application were evaluated. All three methods had good disease-detecting capacity, and each of them detected many more outbreaks compared to those reported to the World Organization for Animal Health during the same time period. Apparent mortality rates were similar for the three methods although highest for the report-driven outbreak investigations, followed by the PRAs, and then the household survey. The three methods have different characteristics and the method of choice will depend on the surveillance objective. The optimal situation might be achieved by a combination of the methods: outbreak detection via smartphone-based real-time surveillance, outbreak investigation for collection of biological samples, and a PRA for a better understanding of the epidemiology of the specific outbreak. All three methods require initial investments and continuous efforts. The sustainability of the surveillance system should, therefore, be carefully evaluated before making such investments.

Report: Unsupervised identification of malaria parasites using computer vision.

PubMed

Khan, Najeed Ahmed; Pervaz, Hassan; Latif, Arsalan; Musharaff, Ayesha

2017-01-01

Malaria in human is a serious and fatal tropical disease. This disease results from Anopheles mosquitoes that are infected by Plasmodium species. The clinical diagnosis of malaria based on the history, symptoms and clinical findings must always be confirmed by laboratory diagnosis. Laboratory diagnosis of malaria involves identification of malaria parasite or its antigen / products in the blood of the patient. Manual diagnosis of malaria parasite by the pathologists has proven to become cumbersome. Therefore, there is a need of automatic, efficient and accurate identification of malaria parasite. In this paper, we proposed a computer vision based approach to identify the malaria parasite from light microscopy images. This research deals with the challenges involved in the automatic detection of malaria parasite tissues. Our proposed method is based on the pixel-based approach. We used K-means clustering (unsupervised approach) for the segmentation to identify malaria parasite tissues.

Unsupervised and self-mapping category formation and semantic object recognition for mobile robot vision used in an actual environment

NASA Astrophysics Data System (ADS)

Madokoro, H.; Tsukada, M.; Sato, K.

2013-07-01

This paper presents an unsupervised learning-based object category formation and recognition method for mobile robot vision. Our method has the following features: detection of feature points and description of features using a scale-invariant feature transform (SIFT), selection of target feature points using one class support vector machines (OC-SVMs), generation of visual words using self-organizing maps (SOMs), formation of labels using adaptive resonance theory 2 (ART-2), and creation and classification of categories on a category map of counter propagation networks (CPNs) for visualizing spatial relations between categories. Classification results of dynamic images using time-series images obtained using two different-size robots and according to movements respectively demonstrate that our method can visualize spatial relations of categories while maintaining time-series characteristics. Moreover, we emphasize the effectiveness of our method for category formation of appearance changes of objects.

Unsupervised feature learning for autonomous rock image classification

NASA Astrophysics Data System (ADS)

Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

2017-09-01

Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

Chromosomal Rearrangements in Salmonella enterica Serotype Typhi Affecting Molecular Typing in Outbreak Investigations

PubMed Central

Echeita, M. A.; Usera, M. A.

1998-01-01

Salmonella enterica serotype Typhi strains belonging to eight different outbreaks of typhoid fever that occurred in Spain between 1989 and 1994 were analyzed by ribotyping and pulsed-field gel electrophoresis. For three outbreaks, two different patterns were detected for each outbreak. The partial digestion analysis by the intron-encoded endonuclease I-CeuI of the two different strains from each outbreak provided an excellent tool for examining the organization of the genomes of epidemiologically related strains. S. enterica serotype Typhi seems to be more susceptible than other serotypes to genetic rearrangements produced by homologous recombinations between rrn operons; these rearrangements do not substantially alter the stability or survival of the bacterium. We conclude that genetic rearrangements can occur during the emergence of an outbreak. PMID:9650981

Pseudo-Outbreak of Actinomyces graevenitzii Associated with Bronchoscopy

PubMed Central

Peaper, David R.; Havill, Nancy L.; Aniskiewicz, Michael; Callan, Deborah; Pop, Olivia; Towle, Dana

2014-01-01

Outbreaks and pseudo-outbreaks of infection related to bronchoscopy typically involve Gram-negative bacteria, Mycobacterium species or Legionella species. We report an unusual bronchoscopy-related pseudo-outbreak due to Actinomyces graevenitzii. Extensive epidemiological and microbiological investigation failed to identify a common source. Strain typing revealed that the cluster was comprised of heterogeneous strains of A. graevenitzii. A change in laboratory procedures for Actinomyces cultures was coincident with the emergence of the pseudo-outbreak, and we determined that A. graevenitzii isolates more readily adopted a white, dry, molar tooth appearance on anaerobic colistin nalidixic acid (CNA) agar which likely facilitated its detection and identification in bronchoscopic specimens. This unusual pseudo-outbreak was related to frequent requests of bronchoscopists for Actinomyces cultures combined with a change in microbiology laboratory practices. PMID:25355767

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.

The first canine visceral leishmaniasis outbreak in Campinas, State of São Paulo Southeastern Brazil.

PubMed

von Zuben, Andrea Paula Bruno; Angerami, Rodrigo Nogueira; Castagna, Claudio; Baldini, Marisa Bevilacqua Denardi; Donalisio, Maria Rita

2014-01-01

Early detection of American visceral leishmaniasis (AVL) outbreak in animals is crucial for controlling this disease in non-endemic areas. Epidemiological surveillance (2009-2012) was performed in Campinas, State of São Paulo, Brazil. In 2009, Leishmania chagasi was positively identified in four dogs. Entomological research and three serological studies (2010-2012) were undertaken as monitoring measures; these approaches revealed a moderate prevalence of Leishmania present in 4% of the canine population. Nyssomyia whitmani and Lutzomyia longipalpis were the predominant species identified. Detection of an AVL outbreak in dogs in an area with an evolving natural landscape containing sand flies is crucial for control programs.

Detection of dominant flow and abnormal events in surveillance video

NASA Astrophysics Data System (ADS)

Kwak, Sooyeong; Byun, Hyeran

2011-02-01

We propose an algorithm for abnormal event detection in surveillance video. The proposed algorithm is based on a semi-unsupervised learning method, a kind of feature-based approach so that it does not detect the moving object individually. The proposed algorithm identifies dominant flow without individual object tracking using a latent Dirichlet allocation model in crowded environments. It can also automatically detect and localize an abnormally moving object in real-life video. The performance tests are taken with several real-life databases, and their results show that the proposed algorithm can efficiently detect abnormally moving objects in real time. The proposed algorithm can be applied to any situation in which abnormal directions or abnormal speeds are detected regardless of direction.

Control Measures Used during Lymphogranuloma Venereum Outbreak, Europe

PubMed Central

Hulscher, Marlies E.J.L.; Vos, Dieuwke; van de Laar, Marita J.W.; Fenton, Kevin A.; van Steenbergen, Jim E.; van der Meer, Jos W.M.; Grol, Richard P.T.M.

2008-01-01

To assess the response to the reemergence of lymphogranuloma venereum, we conducted a cross-sectional survey by administering a structured questionnaire to representatives from 26 European countries. Responses were received from 18 countries. The ability to respond quickly and the measures used for outbreak detection and control varied. Evidence-based criteria were not consistently used to develop recommendations. We did not develop criteria to determine the effectiveness of the recommendations. The degree of preparedness for an unexpected outbreak, as well as the ability of countries to respond quickly to alerts, varied, which indicates weaknesses in the ability to control an outbreak. More guidance is needed to implement and evaluate control measures used during international outbreaks. PMID:18394274

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.

Yellow Fever outbreak in Darfur, Sudan in October 2012; the initial outbreak investigation report.

PubMed

Soghaier, Mohammed A; Hagar, Ahmed; Abbas, Mohammed A; Elmangory, Mutasim M; Eltahir, Khalid M; Sall, Amadou A

2013-10-01

Sudan is subject to repeated outbreaks, including Viral Hemorrhagic Fever (VHF), which is considered to be a very serious illness. Yellow Fever (YF) outbreaks in Sudan have been reported from the 1940s through 2005. In 2012, a new outbreak of YF occurred in the Darfur region. To identify the potential for an outbreak, to diagnose the disease and to be able to recognize its cause among the initial reported cases. >This is a descriptive and investigative field study that applies standard communicable disease outbreak investigation steps. The study involved clinical, serological, entomological and environmental surveys. The field investigation confirmed the outbreak and identified its cause to be YF. National surveillance systems should be strong enough to detect VHFs in a timely manner. Local health facilities should be prepared to promptly treat the initial cases because the case fatality ratios (CFRs) are usually very high among the index cases. Copyright © 2013 King Saud Bin Abdulaziz University for Health Sciences. Published by Elsevier Ltd. All rights reserved.

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

PubMed Central

Waytowich, Nicholas R.; Lawhern, Vernon J.; Bohannon, Addison W.; Ball, Kenneth R.; Lance, Brent J.

2016-01-01

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as outperform traditional within-subject calibration techniques when limited data is available. This method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system. PMID:27713685

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface.

PubMed

Waytowich, Nicholas R; Lawhern, Vernon J; Bohannon, Addison W; Ball, Kenneth R; Lance, Brent J

2016-01-01

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as outperform traditional within-subject calibration techniques when limited data is available. This method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.

Unsupervised EEG analysis for automated epileptic seizure detection

NASA Astrophysics Data System (ADS)

Birjandtalab, Javad; Pouyan, Maziyar Baran; Nourani, Mehrdad

2016-07-01

Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

Two outbreaks of classical swine fever in wild boar in France.

PubMed

Pol, F; Rossi, S; Mesplède, A; Kuntz-Simon, G; Le Potier, M-F

2008-06-21

In 2002 and 2003, two successive outbreaks of classical swine fever were declared in wild boar in northern France. The first was in Moselle, near the town of Thionville and the border with Luxembourg, and the second was in the northern Vosges area, near the German border. The outbreaks were investigated by serological and virological diagnosis of dead or shot animals. Hunting restrictions were applied to limit the spread of the outbreaks. The virus was detected eight times between April and July 2002 in the Thionville area, an area well delimited by natural or artificial barriers such as rivers or highways. Cooperation between the authorities concerned was good, and hunting restrictions were applied for one year. No virus was detected after July 2002 and the Thionville outbreak was officially considered over in March 2005. In the northern Vosges the situation was different, with no barriers to animal movements, continuous forest, difficulties in establishing hunting restrictions in this huge area, and the circulation of the virus in Germany close to the frontier. Virus of a different strain from that isolated in the Thionville outbreak was still being isolated in the northern Vosges in 2004, and owing to the failure of the hunting restrictions, the French health authorities decided to vaccinate wild boar.

An outbreak of food poisoning due to egg yolk reaction-negative Staphylococcus aureus.

PubMed

Miwa, N; Kawamura, A; Masuda, T; Akiyama, M

2001-03-20

An outbreak of staphylococcal food poisoning due to an egg yolk (EY) reaction-negative strain occurred in Japan. Twenty-one of 53 dam construction workers who ate boxed lunches prepared at their company cafeteria became ill, and eight required hospital treatment. The outbreak showed a typical incubation time (1.5-4 h with a median time of 2.7 h) and symptoms (vomiting and diarrhea) of staphylococcal food poisoning. Staphylococcus aureus, which produces staphylococcal enterotoxin (SE) A, was isolated from four fecal specimens of eight patients tested. Scrambled egg in the boxed lunches contained 20-40 ng/g of SEA, and 3.0 x 10(9)/g of viable S. aureus cells that produced this toxin. All isolates from patients and the food were EY reaction-negative, coagulase type II, and showed the same restriction fragment length polymorphism (RFLP) pattern. We concluded that the outbreak was caused by scrambled egg contaminated with EY reaction-negative S. aureus. In Japan, outbreaks of staphylococcal food poisoning are mainly caused by EY reaction-positive S. aureus, and EY reaction-negative colonies grown on agar plates containing EY are usually not analyzed further for detection of S. aureus. The present outbreak suggested that EY reaction-negative isolates should be subjected to further analysis to detect the causative agents of staphylococcal food poisoning.

Value of syndromic surveillance within the Armed Forces for early warning during a dengue fever outbreak in French Guiana in 2006

PubMed Central

Meynard, Jean-Baptiste; Chaudet, Hervé; Texier, Gaetan; Ardillon, Vanessa; Ravachol, Françoise; Deparis, Xavier; Jefferson, Henry; Dussart, Philippe; Morvan, Jacques; Boutin, Jean-Paul

2008-01-01

Background A dengue fever outbreak occured in French Guiana in 2006. The objectives were to study the value of a syndromic surveillance system set up within the armed forces, compared to the traditional clinical surveillance system during this outbreak, to highlight issues involved in comparing military and civilian surveillance systems and to discuss the interest of syndromic surveillance for public health response. Methods Military syndromic surveillance allows the surveillance of suspected dengue fever cases among the 3,000 armed forces personnel. Within the same population, clinical surveillance uses several definition criteria for dengue fever cases, depending on the epidemiological situation. Civilian laboratory surveillance allows the surveillance of biologically confirmed cases, within the 200,000 inhabitants. Results It was shown that syndromic surveillance detected the dengue fever outbreak several weeks before clinical surveillance, allowing quick and effective enhancement of vector control within the armed forces. Syndromic surveillance was also found to have detected the outbreak before civilian laboratory surveillance. Conclusion Military syndromic surveillance allowed an early warning for this outbreak to be issued, enabling a quicker public health response by the armed forces. Civilian surveillance system has since introduced syndromic surveillance as part of its surveillance strategy. This should enable quicker public health responses in the future. PMID:18597694

Laboratory-Based Prospective Surveillance for Community Outbreaks of Shigella spp. in Argentina

PubMed Central

Viñas, María R.; Tuduri, Ezequiel; Galar, Alicia; Yih, Katherine; Pichel, Mariana; Stelling, John; Brengi, Silvina P.; Della Gaspera, Anabella; van der Ploeg, Claudia; Bruno, Susana; Rogé, Ariel; Caffer, María I.; Kulldorff, Martin; Galas, Marcelo

2013-01-01

Background To implement effective control measures, timely outbreak detection is essential. Shigella is the most common cause of bacterial diarrhea in Argentina. Highly resistant clones of Shigella have emerged, and outbreaks have been recognized in closed settings and in whole communities. We hereby report our experience with an evolving, integrated, laboratory-based, near real-time surveillance system operating in six contiguous provinces of Argentina during April 2009 to March 2012. Methodology To detect localized shigellosis outbreaks timely, we used the prospective space-time permutation scan statistic algorithm of SaTScan, embedded in WHONET software. Twenty three laboratories sent updated Shigella data on a weekly basis to the National Reference Laboratory. Cluster detection analysis was performed at several taxonomic levels: for all Shigella spp., for serotypes within species and for antimicrobial resistance phenotypes within species. Shigella isolates associated with statistically significant signals (clusters in time/space with recurrence interval ≥365 days) were subtyped by pulsed field gel electrophoresis (PFGE) using PulseNet protocols. Principal Findings In three years of active surveillance, our system detected 32 statistically significant events, 26 of them identified before hospital staff was aware of any unexpected increase in the number of Shigella isolates. Twenty-six signals were investigated by PFGE, which confirmed a close relationship among the isolates for 22 events (84.6%). Seven events were investigated epidemiologically, which revealed links among the patients. Seventeen events were found at the resistance profile level. The system detected events of public health importance: infrequent resistance profiles, long-lasting and/or re-emergent clusters and events important for their duration or size, which were reported to local public health authorities. Conclusions/Significance The WHONET-SaTScan system may serve as a model for surveillance and can be applied to other pathogens, implemented by other networks, and scaled up to national and international levels for early detection and control of outbreaks. PMID:24349586

Laboratory-based prospective surveillance for community outbreaks of Shigella spp. in Argentina.

PubMed

Viñas, María R; Tuduri, Ezequiel; Galar, Alicia; Yih, Katherine; Pichel, Mariana; Stelling, John; Brengi, Silvina P; Della Gaspera, Anabella; van der Ploeg, Claudia; Bruno, Susana; Rogé, Ariel; Caffer, María I; Kulldorff, Martin; Galas, Marcelo

2013-01-01

To implement effective control measures, timely outbreak detection is essential. Shigella is the most common cause of bacterial diarrhea in Argentina. Highly resistant clones of Shigella have emerged, and outbreaks have been recognized in closed settings and in whole communities. We hereby report our experience with an evolving, integrated, laboratory-based, near real-time surveillance system operating in six contiguous provinces of Argentina during April 2009 to March 2012. To detect localized shigellosis outbreaks timely, we used the prospective space-time permutation scan statistic algorithm of SaTScan, embedded in WHONET software. Twenty three laboratories sent updated Shigella data on a weekly basis to the National Reference Laboratory. Cluster detection analysis was performed at several taxonomic levels: for all Shigella spp., for serotypes within species and for antimicrobial resistance phenotypes within species. Shigella isolates associated with statistically significant signals (clusters in time/space with recurrence interval ≥365 days) were subtyped by pulsed field gel electrophoresis (PFGE) using PulseNet protocols. In three years of active surveillance, our system detected 32 statistically significant events, 26 of them identified before hospital staff was aware of any unexpected increase in the number of Shigella isolates. Twenty-six signals were investigated by PFGE, which confirmed a close relationship among the isolates for 22 events (84.6%). Seven events were investigated epidemiologically, which revealed links among the patients. Seventeen events were found at the resistance profile level. The system detected events of public health importance: infrequent resistance profiles, long-lasting and/or re-emergent clusters and events important for their duration or size, which were reported to local public health authorities. The WHONET-SaTScan system may serve as a model for surveillance and can be applied to other pathogens, implemented by other networks, and scaled up to national and international levels for early detection and control of outbreaks.

Hospital-acquired listeriosis outbreak caused by contaminated diced celery--Texas, 2010.

PubMed

Gaul, Linda Knudson; Farag, Noha H; Shim, Trudi; Kingsley, Monica A; Silk, Benjamin J; Hyytia-Trees, Eija

2013-01-01

Listeria monocytogenes causes often-fatal infections affecting mainly immunocompromised persons. Sources of hospital-acquired listeriosis outbreaks can be difficult to identify. We investigated a listeriosis outbreak spanning 7 months and involving 5 hospitals. Outbreak-related cases were identified by pulsed-field gel electrophoresis (PFGE) and confirmed by multiple-locus variable-number tandem-repeat analysis (MLVA). We conducted patient interviews, medical records reviews, and hospital food source evaluations. Food and environmental specimens were collected at a hospital (hospital A) where 6 patients had been admitted before listeriosis onset; these specimens were tested by culture, polymerase chain reaction (PCR), and PFGE. We collected and tested food and environmental samples at the implicated processing facility. Ten outbreak-related patients were immunocompromised by ≥1 underlying conditions or treatments; 5 died. All patients had been admitted to or visited an acute-care hospital during their possible incubation periods. The outbreak strain of L. monocytogenes was isolated from chicken salad and its diced celery ingredient at hospital A, and in 19 of >200 swabs of multiple surfaces and in 8 of 11 diced celery products at the processing plant. PCR testing detected Listeria in only 3 of 10 environmental and food samples from which it was isolated by culturing. The facility was closed, products were recalled, and the outbreak ended. Contaminated diced celery caused a baffling, lengthy outbreak of hospital-acquired listeriosis. PCR testing often failed to detect the pathogen, suggesting its reliability should be further evaluated. Listeriosis risk should be considered in fresh produce selections for immunocompromised patients.

[Investigation of a measles outbreak caused by genotype D8 virus in Pinghu city of Zhejiang province, 2017].

PubMed

Yan, R; He, B; Yao, F Y; Xiang, Z L; He, H Q; Xie, S Y; Feng, Y

2018-03-10

Objective: To investigate the epidemiological characteristics of measles outbreak caused by genotype D8 virus in Pinghu city of Zhejiang province, and provide evidence for the control of the outbreak. Methods: The measles outbreak data were collected through National Measles Surveillance System. The outpatient records and admission records were checked, field investigation and outbreak response were conducted. Blood samples in acute phase and swab specimens were collected from the patients for laboratory testing, including serology test, RNA extraction and amplification, measles virus isolation and genotype identification. Software SPSS 17.0 and Excel 2016 were used for data analysis. Results: A total of 10 confirmed measles cases were reported in Pinghu city, and 8 cases were aged >40 years. Six blood samples were collected, in which 5 were measles D8 virus positive and 1 was negative in measles virus detection. There were epidemiological links among 10 cases which occurred in a factory, a hospital and a family at the same time. There was no statistical difference in symptoms among cases caused by D8 virus and H1a virus. After the emergent measles vaccination, the measles outbreak was effectively controlled. Conclusion: Untimely response due to the uneasy detection of measles cases in the early stage, nosocomial infection and weak barrier of measles immunity in adults might be the main reasons for this outbreak. Measles vaccination is effective in the prevention of measles D8 virus infection. It is necessary to strengthen measles genotype monitoring for the tracing of infection source and control of outbreaks.

Satellite data based method for general survey of forest insect disturbance in British Columbia

NASA Astrophysics Data System (ADS)

Ranson, J.; Montesano, P.

2008-12-01

Regional forest disturbances caused by insects are important to monitor and quantify because of their influence on local ecosystems and the global carbon cycle. Local damage to forest trees disrupts food supplies and shelter for a variety of organisms. Changes in the global carbon budget, its sources and its sinks affect the way the earth functions as a whole, and has an impact on global climate. Furthermore, the ability to detect nascent outbreaks and monitor the spread of regional infestations helps managers mitigate the damage done by catastrophic insect outbreaks. While detection is needed at a fine scale to support local mitigation efforts, detection at a broad regional scale is important for carbon flux modeling on the landscape scale, and needed to direct the local efforts. This paper presents a method for routinely detecting insect damage to coniferous forests using MODIS vegetation indices, thermal anomalies and land cover. The technique is validated using insect outbreak maps and accounts for fire disturbance effects. The range of damage detected may be used to interpret and quantify possible forest damage by insects.

Predicting category intuitiveness with the rational model, the simplicity model, and the generalized context model.

PubMed

Pothos, Emmanuel M; Bailey, Todd M

2009-07-01

Naïve observers typically perceive some groupings for a set of stimuli as more intuitive than others. The problem of predicting category intuitiveness has been historically considered the remit of models of unsupervised categorization. In contrast, this article develops a measure of category intuitiveness from one of the most widely supported models of supervised categorization, the generalized context model (GCM). Considering different category assignments for a set of instances, the authors asked how well the GCM can predict the classification of each instance on the basis of all the other instances. The category assignment that results in the smallest prediction error is interpreted as the most intuitive for the GCM-the authors refer to this way of applying the GCM as "unsupervised GCM." The authors systematically compared predictions of category intuitiveness from the unsupervised GCM and two models of unsupervised categorization: the simplicity model and the rational model. The unsupervised GCM compared favorably with the simplicity model and the rational model. This success of the unsupervised GCM illustrates that the distinction between supervised and unsupervised categorization may need to be reconsidered. However, no model emerged as clearly superior, indicating that there is more work to be done in understanding and modeling category intuitiveness.

Effects of Supervised vs. Unsupervised Training Programs on Balance and Muscle Strength in Older Adults: A Systematic Review and Meta-Analysis.

PubMed

Lacroix, André; Hortobágyi, Tibor; Beurskens, Rainer; Granacher, Urs

2017-11-01

Balance and resistance training can improve healthy older adults' balance and muscle strength. Delivering such exercise programs at home without supervision may facilitate participation for older adults because they do not have to leave their homes. To date, no systematic literature analysis has been conducted to determine if supervision affects the effectiveness of these programs to improve healthy older adults' balance and muscle strength/power. The objective of this systematic review and meta-analysis was to quantify the effectiveness of supervised vs. unsupervised balance and/or resistance training programs on measures of balance and muscle strength/power in healthy older adults. In addition, the impact of supervision on training-induced adaptive processes was evaluated in the form of dose-response relationships by analyzing randomized controlled trials that compared supervised with unsupervised trials. A computerized systematic literature search was performed in the electronic databases PubMed, Web of Science, and SportDiscus to detect articles examining the role of supervision in balance and/or resistance training in older adults. The initially identified 6041 articles were systematically screened. Studies were included if they examined balance and/or resistance training in adults aged ≥65 years with no relevant diseases and registered at least one behavioral balance (e.g., time during single leg stance) and/or muscle strength/power outcome (e.g., time for 5-Times-Chair-Rise-Test). Finally, 11 studies were eligible for inclusion in this meta-analysis. Weighted mean standardized mean differences between subjects (SMD bs ) of supervised vs. unsupervised balance/resistance training studies were calculated. The included studies were coded for the following variables: number of participants, sex, age, number and type of interventions, type of balance/strength tests, and change (%) from pre- to post-intervention values. Additionally, we coded training according to the following modalities: period, frequency, volume, modalities of supervision (i.e., number of supervised/unsupervised sessions within the supervised or unsupervised training groups, respectively). Heterogeneity was computed using I 2 and χ 2 statistics. The methodological quality of the included studies was evaluated using the Physiotherapy Evidence Database scale. Our analyses revealed that in older adults, supervised balance/resistance training was superior compared with unsupervised balance/resistance training in improving measures of static steady-state balance (mean SMD bs  = 0.28, p = 0.39), dynamic steady-state balance (mean SMD bs  = 0.35, p = 0.02), proactive balance (mean SMD bs  = 0.24, p = 0.05), balance test batteries (mean SMD bs  = 0.53, p = 0.02), and measures of muscle strength/power (mean SMD bs  = 0.51, p = 0.04). Regarding the examined dose-response relationships, our analyses showed that a number of 10-29 additional supervised sessions in the supervised training groups compared with the unsupervised training groups resulted in the largest effects for static steady-state balance (mean SMD bs  = 0.35), dynamic steady-state balance (mean SMD bs  = 0.37), and muscle strength/power (mean SMD bs  = 1.12). Further, ≥30 additional supervised sessions in the supervised training groups were needed to produce the largest effects on proactive balance (mean SMD bs  = 0.30) and balance test batteries (mean SMD bs  = 0.77). Effects in favor of supervised programs were larger for studies that did not include any supervised sessions in their unsupervised programs (mean SMD bs : 0.28-1.24) compared with studies that implemented a few supervised sessions in their unsupervised programs (e.g., three supervised sessions throughout the entire intervention program; SMD bs : -0.06 to 0.41). The present findings have to be interpreted with caution because of the low number of eligible studies and the moderate methodological quality of the included studies, which is indicated by a median Physiotherapy Evidence Database scale score of 5. Furthermore, we indirectly compared dose-response relationships across studies and not from single controlled studies. Our analyses suggest that supervised balance and/or resistance training improved measures of balance and muscle strength/power to a greater extent than unsupervised programs in older adults. Owing to the small number of available studies, we were unable to establish a clear dose-response relationship with regard to the impact of supervision. However, the positive effects of supervised training are particularly prominent when compared with completely unsupervised training programs. It is therefore recommended to include supervised sessions (i.e., two out of three sessions/week) in balance/resistance training programs to effectively improve balance and muscle strength/power in older adults.

Supervised detection of exoplanets in high-contrast imaging sequences

NASA Astrophysics Data System (ADS)

Gomez Gonzalez, C. A.; Absil, O.; Van Droogenbroeck, M.

2018-06-01

Context. Post-processing algorithms play a key role in pushing the detection limits of high-contrast imaging (HCI) instruments. State-of-the-art image processing approaches for HCI enable the production of science-ready images relying on unsupervised learning techniques, such as low-rank approximations, for generating a model point spread function (PSF) and subtracting the residual starlight and speckle noise. Aims: In order to maximize the detection rate of HCI instruments and survey campaigns, advanced algorithms with higher sensitivities to faint companions are needed, especially for the speckle-dominated innermost region of the images. Methods: We propose a reformulation of the exoplanet detection task (for ADI sequences) that builds on well-established machine learning techniques to take HCI post-processing from an unsupervised to a supervised learning context. In this new framework, we present algorithmic solutions using two different discriminative models: SODIRF (random forests) and SODINN (neural networks). We test these algorithms on real ADI datasets from VLT/NACO and VLT/SPHERE HCI instruments. We then assess their performances by injecting fake companions and using receiver operating characteristic analysis. This is done in comparison with state-of-the-art ADI algorithms, such as ADI principal component analysis (ADI-PCA). Results: This study shows the improved sensitivity versus specificity trade-off of the proposed supervised detection approach. At the diffraction limit, SODINN improves the true positive rate by a factor ranging from 2 to 10 (depending on the dataset and angular separation) with respect to ADI-PCA when working at the same false-positive level. Conclusions: The proposed supervised detection framework outperforms state-of-the-art techniques in the task of discriminating planet signal from speckles. In addition, it offers the possibility of re-processing existing HCI databases to maximize their scientific return and potentially improve the demographics of directly imaged exoplanets.

Causes of Pneumonia Epizootics among Bighorn Sheep, Western United States, 2008–2010

PubMed Central

Highland, Margaret A.; Baker, Katherine; Cassirer, E. Frances; Anderson, Neil J.; Ramsey, Jennifer M.; Mansfield, Kristin; Bruning, Darren L.; Wolff, Peregrine; Smith, Joshua B.; Jenks, Jonathan A.

2012-01-01

Epizootic pneumonia of bighorn sheep is a devastating disease of uncertain etiology. To help clarify the etiology, we used culture and culture-independent methods to compare the prevalence of the bacterial respiratory pathogens Mannheimia haemolytica, Bibersteinia trehalosi, Pasteurella multocida, and Mycoplasma ovipneumoniae in lung tissue from 44 bighorn sheep from herds affected by 8 outbreaks in the western United States. M. ovipneumoniae, the only agent detected at significantly higher prevalence in animals from outbreaks (95%) than in animals from unaffected healthy populations (0%), was the most consistently detected agent and the only agent that exhibited single strain types within each outbreak. The other respiratory pathogens were frequently but inconsistently detected, as were several obligate anaerobic bacterial species, all of which might represent secondary or opportunistic infections that could contribute to disease severity. These data provide evidence that M. ovipneumoniae plays a primary role in the etiology of epizootic pneumonia of bighorn sheep. PMID:22377321

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.

Unsupervised Structure Detection in Biomedical Data.

PubMed

Vogt, Julia E

2015-01-01

A major challenge in computational biology is to find simple representations of high-dimensional data that best reveal the underlying structure. In this work, we present an intuitive and easy-to-implement method based on ranked neighborhood comparisons that detects structure in unsupervised data. The method is based on ordering objects in terms of similarity and on the mutual overlap of nearest neighbors. This basic framework was originally introduced in the field of social network analysis to detect actor communities. We demonstrate that the same ideas can successfully be applied to biomedical data sets in order to reveal complex underlying structure. The algorithm is very efficient and works on distance data directly without requiring a vectorial embedding of data. Comprehensive experiments demonstrate the validity of this approach. Comparisons with state-of-the-art clustering methods show that the presented method outperforms hierarchical methods as well as density based clustering methods and model-based clustering. A further advantage of the method is that it simultaneously provides a visualization of the data. Especially in biomedical applications, the visualization of data can be used as a first pre-processing step when analyzing real world data sets to get an intuition of the underlying data structure. We apply this model to synthetic data as well as to various biomedical data sets which demonstrate the high quality and usefulness of the inferred structure.

Geospatiotemporal Data Mining of Remotely Sensed Phenology for Unsupervised Forest Threat Detection

NASA Astrophysics Data System (ADS)

Mills, R. T.; Hoffman, F. M.; Kumar, J.; Vulli, S. S.; Hargrove, W. W.; Spruce, J.

2010-12-01

Hargrove and Hoffman have previously developed and applied a scalable geospatiotemporal data mining approach to define a set of categorical, multivariate classes or states for describing and tracking the behavior of ecosystem properties through time within a multi-dimensional phase or state space. The method employs a standard k-means cluster analysis with enhancements that reduce the number of required comparisons, dramatically accelerating iterative convergence. In support of efforts by the USDA Forest Service to develop a National Early Warning System for Forest Disturbances, we have applied this geospatiotemporal cluster analysis procedure to annual phenology patterns derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) for unsupervised change detection. We will present initial results from the analysis of seven years of 250-m MODIS NDVI data for the conterminous United States. While determining what constitutes a "normal" phenological pattern for any given location is challenging due to interannual climate variability, a spatially varying climate change trend, and the relatively short record of MODIS NDVI observations, these results demonstrate the utility of the method for detecting significant mortality events, like the progressive damage from mountain pine beetle, and suggest that the technique may be successfully implemented as a key component in an early warning system for identifying forest threats from natural and anthropogenic disturbances at a continental scale.

The influence of unsupervised time on elementary school children at high risk for inattention and problem behaviors.

PubMed

Na, Kyoung-Sae; Lee, Soyoung Irene; Hong, Hyun Ju; Oh, Myoung-Ja; Bahn, Geon Ho; Ha, Kyunghee; Shin, Yun Mi; Song, Jungeun; Park, Eun Jin; Yoo, Heejung; Kim, Hyunsoo; Kyung, Yun-Mi

2014-06-01

In the last few decades, changing socioeconomic and family structures have increasingly left children alone without adult supervision. Carefully prepared and limited periods of unsupervised time are not harmful for children. However, long unsupervised periods have harmful effects, particularly for those children at high risk for inattention and problem behaviors. In this study, we examined the influence of unsupervised time on behavior problems by studying a sample of elementary school children at high risk for inattention and problem behaviors. The study analyzed data from the Children's Mental Health Promotion Project, which was conducted in collaboration with education, government, and mental health professionals. The child behavior checklist (CBCL) was administered to assess problem behaviors among first- and fourth-grade children. Multivariate logistic regression analysis was used to evaluate the influence of unsupervised time on children's behavior. A total of 3,270 elementary school children (1,340 first-graders and 1,930 fourth-graders) were available for this study; 1,876 of the 3,270 children (57.4%) reportedly spent a significant amount of time unsupervised during the day. Unsupervised time that exceeded more than 2h per day increased the risk of delinquency, aggressive behaviors, and somatic complaints, as well as externalizing and internalizing problems. Carefully planned afterschool programming and care should be provided to children at high risk for inattention and problem behaviors. Also, a more comprehensive approach is needed to identify the possible mechanisms by which unsupervised time aggravates behavior problems in children predisposed for these behaviors. Copyright © 2013 Elsevier Ltd. All rights reserved.

Unsupervised learning in persistent sensing for target recognition by wireless ad hoc networks of ground-based sensors

NASA Astrophysics Data System (ADS)

Hortos, William S.

2008-04-01

In previous work by the author, effective persistent and pervasive sensing for recognition and tracking of battlefield targets were seen to be achieved, using intelligent algorithms implemented by distributed mobile agents over a composite system of unmanned aerial vehicles (UAVs) for persistence and a wireless network of unattended ground sensors for pervasive coverage of the mission environment. While simulated performance results for the supervised algorithms of the composite system are shown to provide satisfactory target recognition over relatively brief periods of system operation, this performance can degrade by as much as 50% as target dynamics in the environment evolve beyond the period of system operation in which the training data are representative. To overcome this limitation, this paper applies the distributed approach using mobile agents to the network of ground-based wireless sensors alone, without the UAV subsystem, to provide persistent as well as pervasive sensing for target recognition and tracking. The supervised algorithms used in the earlier work are supplanted by unsupervised routines, including competitive-learning neural networks (CLNNs) and new versions of support vector machines (SVMs) for characterization of an unknown target environment. To capture the same physical phenomena from battlefield targets as the composite system, the suite of ground-based sensors can be expanded to include imaging and video capabilities. The spatial density of deployed sensor nodes is increased to allow more precise ground-based location and tracking of detected targets by active nodes. The "swarm" mobile agents enabling WSN intelligence are organized in a three processing stages: detection, recognition and sustained tracking of ground targets. Features formed from the compressed sensor data are down-selected according to an information-theoretic algorithm that reduces redundancy within the feature set, reducing the dimension of samples used in the target recognition and tracking routines. Target tracking is based on simplified versions of Kalman filtration. Accuracy of recognition and tracking of implemented versions of the proposed suite of unsupervised algorithms is somewhat degraded from the ideal. Target recognition and tracking by supervised routines and by unsupervised SVM and CLNN routines in the ground-based WSN is evaluated in simulations using published system values and sensor data from vehicular targets in ground-surveillance scenarios. Results are compared with previously published performance for the system of the ground-based sensor network (GSN) and UAV swarm.

Environmental Survey of Drinking Water Sources in Kampala, Uganda, during a Typhoid Fever Outbreak

PubMed Central

Kahler, A. M.; Nansubuga, I.; Nanyunja, E. M.; Kaplan, B.; Jothikumar, N.; Routh, J.; Gómez, G. A.; Mintz, E. D.; Hill, V. R.

2017-01-01

ABSTRACT In 2015, a typhoid fever outbreak began in downtown Kampala, Uganda, and spread into adjacent districts. In response, an environmental survey of drinking water source types was conducted in areas of the city with high case numbers. A total of 122 samples was collected from 12 source types and tested for Escherichia coli, free chlorine, and conductivity. An additional 37 grab samples from seven source types and 16 paired large volume (20 liter) samples from wells and springs were also collected and tested for the presence of Salmonella enterica serovar Typhi. Escherichia coli was detected in 60% of kaveras (drinking water sold in plastic bags) and 80% of refilled water bottles; free chlorine was not detected in either source type. Most jerry cans (68%) contained E. coli and had free chlorine residuals below the WHO-recommended level of 0.5 mg/liter during outbreaks. Elevated conductivity readings for kaveras, refilled water bottles, and jerry cans (compared to treated surface water supplied by the water utility) suggested that they likely contained untreated groundwater. All unprotected springs and wells and more than 60% of protected springs contained E. coli. Water samples collected from the water utility were found to have acceptable free chlorine levels and no detectable E. coli. While S. Typhi was not detected in water samples, Salmonella spp. were detected in samples from two unprotected springs, one protected spring, and one refilled water bottle. These data provided clear evidence that unregulated vended water and groundwater represented a risk for typhoid transmission. IMPORTANCE Despite the high incidence of typhoid fever globally, relatively few outbreak investigations incorporate drinking water testing. During waterborne disease outbreaks, measurement of physical-chemical parameters, such as free chlorine residual and electrical conductivity, and of microbiological parameters, such as the presence of E. coli or the implicated etiologic agent, in drinking water samples can identify contaminated sources. This investigation indicated that unregulated vended water and groundwater sources were contaminated and were therefore a risk to consumers during the 2015 typhoid fever outbreak in Kampala. Identification of contaminated drinking water sources and sources that do not contain adequate disinfectant levels can lead to rapid targeted interventions. PMID:28970225

Increased information on waterborne outbreaks through efficient notification system enforces actions towards safe drinking water.

PubMed

Zacheus, Outi; Miettinen, Ilkka T

2011-12-01

In 1997, a compulsory notification system for waterborne outbreaks was introduced in Finland. The main aim of this notification is to obtain immediate information on suspected waterborne outbreaks in order to restrict and manage the outbreak promptly. During the past ten years, there have been 67 waterborne outbreaks in Finland, mainly associated with small groundwater supplies or private wells. The number of reported waterborne outbreaks has increased since the launch of the notification system indicating that the threshold limit of outbreak detection has most probably decreased. The number of cases of illness has fulfilled the national health target, which is below 0.01% of the population, but more action is still needed to ensure the production of safe drinking water under all circumstances. Ten years accumulation of knowledge on outbreaks has revealed that a compulsory notification system is an effective tool to gather information on waterborne outbreaks. The system has also increased awareness of possible problems related to the quality of drinking water. This article summarises management and legislative actions and policy measures taken so far in Finland to reduce the number of outbreaks and cases of illness related to them.

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.

Development of methods to detect "Norwalk-like viruses" (NLVs) and hepatitis A virus in delicatessen foods: application to a food-borne NLV outbreak.

PubMed

Schwab, K J; Neill, F H; Fankhauser, R L; Daniels, N A; Monroe, S S; Bergmire-Sweat, D A; Estes, M K; Atmar, R L

2000-01-01

"Norwalk-like viruses" (NLVs) and hepatitis A virus (HAV) are the most common causes of virus-mediated food-borne illness. Epidemiological investigations of outbreaks associated with these viruses have been hindered by the lack of available methods for the detection of NLVs and HAV in foodstuffs. Although reverse transcription (RT)-PCR methods have been useful in detecting NLVs and HAV in bivalve mollusks implicated in outbreaks, to date such methods have not been available for other foods. To address this need, we developed a method to detect NLVs and HAV recovered from food samples. The method involves washing of food samples with a guanidinium-phenol-based reagent, extraction with chloroform, and precipitation in isopropanol. Recovered viral RNA is amplified with HAV- or NLV-specific primers in RT-PCRs, using a viral RNA internal standard control to identify potential sample inhibition. By this method, 10 to 100 PCR units (estimated to be equivalent to 10(2) to 10(3) viral genome copies) of HAV and Norwalk virus seeded onto ham, turkey, and roast beef were detected. The method was applied to food samples implicated in an NLV-associated outbreak at a university cafeteria. Sliced deli ham was positive for a genogroup II NLV as determined by using both polymerase- and capsid-specific primers and probes. Sequence analysis of the PCR-amplified capsid region of the genome indicated that the sequence was identical to the sequence from virus detected in the stools of ill students. The developed method is rapid, simple, and efficient.

Development of Methods To Detect “Norwalk-Like Viruses” (NLVs) and Hepatitis A Virus in Delicatessen Foods: Application to a Food-Borne NLV Outbreak

PubMed Central

Schwab, Kellogg J.; Neill, Frederick H.; Fankhauser, Rebecca L.; Daniels, Nicholas A.; Monroe, Stephan S.; Bergmire-Sweat, David A.; Estes, Mary K.; Atmar, Robert L.

2000-01-01

“Norwalk-like viruses” (NLVs) and hepatitis A virus (HAV) are the most common causes of virus-mediated food-borne illness. Epidemiological investigations of outbreaks associated with these viruses have been hindered by the lack of available methods for the detection of NLVs and HAV in foodstuffs. Although reverse transcription (RT)-PCR methods have been useful in detecting NLVs and HAV in bivalve mollusks implicated in outbreaks, to date such methods have not been available for other foods. To address this need, we developed a method to detect NLVs and HAV recovered from food samples. The method involves washing of food samples with a guanidinium-phenol-based reagent, extraction with chloroform, and precipitation in isopropanol. Recovered viral RNA is amplified with HAV- or NLV-specific primers in RT-PCRs, using a viral RNA internal standard control to identify potential sample inhibition. By this method, 10 to 100 PCR units (estimated to be equivalent to 102 to 103 viral genome copies) of HAV and Norwalk virus seeded onto ham, turkey, and roast beef were detected. The method was applied to food samples implicated in an NLV-associated outbreak at a university cafeteria. Sliced deli ham was positive for a genogroup II NLV as determined by using both polymerase- and capsid-specific primers and probes. Sequence analysis of the PCR-amplified capsid region of the genome indicated that the sequence was identical to the sequence from virus detected in the stools of ill students. The developed method is rapid, simple, and efficient. PMID:10618226

Follow-Up of Norovirus Contamination in an Oyster Production Area Linked to Repeated Outbreaks.

PubMed

Le Mennec, Cécile; Parnaudeau, Sylvain; Rumebe, Myriam; Le Saux, Jean-Claude; Piquet, Jean-Côme; Le Guyader, S Françoise

2017-03-01

A production area repeatedly implicated in oyster-related gastroenteritis in France was studied for several months over 2 years. Outbreaks and field samples were analyzed by undertaking triplicate extractions, followed by norovirus (NoV) detection using triplicate wells for genomic amplification. This approach allowed us to demonstrate that some variabilities can be observed for samples with a low level of contamination, but most samples analyzed gave reproducible results. At the first outbreak, implicated oysters were collected at the beginning of the contamination event, which was reflected by the higher NoV levels during the first month of the study. During the second year, NoV concentrations in samples implicated in outbreaks and collected from the production area were similar, confirming the failure of the shellfish depuration process. Contamination was detected mainly during winter-spring months, and a high prevalence of NoV GI contamination was observed. A half-life of 18 days was calculated from NoV concentrations detected in oysters during this study, showing a very slow decrease of the contamination in the production area. Preventing the contamination of coastal waters should be a priority.

Efficient detection of contagious outbreaks in massive metropolitan encounter networks

PubMed Central

Sun, Lijun; Axhausen, Kay W.; Lee, Der-Horng; Cebrian, Manuel

2014-01-01

Physical contact remains difficult to trace in large metropolitan networks, though it is a key vehicle for the transmission of contagious outbreaks. Co-presence encounters during daily transit use provide us with a city-scale time-resolved physical contact network, consisting of 1 billion contacts among 3 million transit users. Here, we study the advantage that knowledge of such co-presence structures may provide for early detection of contagious outbreaks. We first examine the “friend sensor” scheme - a simple, but universal strategy requiring only local information - and demonstrate that it provides significant early detection of simulated outbreaks. Taking advantage of the full network structure, we then identify advanced “global sensor sets”, obtaining substantial early warning times savings over the friends sensor scheme. Individuals with highest number of encounters are the most efficient sensors, with performance comparable to individuals with the highest travel frequency, exploratory behavior and structural centrality. An efficiency balance emerges when testing the dependency on sensor size and evaluating sensor reliability; we find that substantial and reliable lead-time could be attained by monitoring only 0.01% of the population with the highest degree. PMID:24903017

Pseudo-outbreak of Actinomyces graevenitzii associated with bronchoscopy.

PubMed

Peaper, David R; Havill, Nancy L; Aniskiewicz, Michael; Callan, Deborah; Pop, Olivia; Towle, Dana; Boyce, John M

2015-01-01

Outbreaks and pseudo-outbreaks of infection related to bronchoscopy typically involve Gram-negative bacteria, Mycobacterium species or Legionella species. We report an unusual bronchoscopy-related pseudo-outbreak due to Actinomyces graevenitzii. Extensive epidemiological and microbiological investigation failed to identify a common source. Strain typing revealed that the cluster was comprised of heterogeneous strains of A. graevenitzii. A change in laboratory procedures for Actinomyces cultures was coincident with the emergence of the pseudo-outbreak, and we determined that A. graevenitzii isolates more readily adopted a white, dry, molar tooth appearance on anaerobic colistin nalidixic acid (CNA) agar which likely facilitated its detection and identification in bronchoscopic specimens. This unusual pseudo-outbreak was related to frequent requests of bronchoscopists for Actinomyces cultures combined with a change in microbiology laboratory practices. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Importance of molecular typing in confirmation of the source of a national hepatitis A virus outbreak in Norway and the detection of a related cluster in Germany.

PubMed

Guzman-Herrador, Bernardo R; Panning, Marcus; Stene-Johansen, Kathrine; Borgen, Katrine; Einöder-Moreno, Margot; Huzly, Daniela; Jensvoll, Laila; Lange, Heidi; Maassen, Sigrid; Myking, Solveig; Myrmel, Mette; Neumann-Haefelin, Christoph; Nygård, Karin; Wenzel, Jürgen J; Øye, Ann Kristin; Vold, Line

2015-11-01

In March 2014, after an increase of notifications of domestically acquired hepatitis A virus infections, an outbreak investigation was launched in Norway. Sequenced-based typing results showed that these cases were associated with a strain that was identical to one causing an ongoing multinational outbreak in Europe linked to frozen mixed berries. Thirty-three confirmed cases with the outbreak strain were notified in Norway from November 2013 to June 2014. Epidemiological evidence and trace-back investigations linked the outbreak to the consumption of a berry mix cake. Identification of the hepatitis A virus outbreak strain in berries from one of the implicated cakes confirmed the cake to be the source. Subsequently, a cluster in Germany linked to the cake was also identified.

TESTING METHODS FOR DETECTION OF CRYPTOSPORIDIUM SPP. IN WATER SAMPLES

EPA Science Inventory

A large waterborne outbreak of cryptosporidiosis in Milwaukee, Wisconsin, U.S.A. in 1993 prompted a search for ways to prevent large scale waterborne outbreaks of protozoan parasitoses. Two principle strategies have emerged: risk assessment leading to appropriate treatment and ...

Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity.

PubMed

Bichler, Olivier; Querlioz, Damien; Thorpe, Simon J; Bourgoin, Jean-Philippe; Gamrat, Christian

2012-08-01

A biologically inspired approach to learning temporally correlated patterns from a spiking silicon retina is presented. Spikes are generated from the retina in response to relative changes in illumination at the pixel level and transmitted to a feed-forward spiking neural network. Neurons become sensitive to patterns of pixels with correlated activation times, in a fully unsupervised scheme. This is achieved using a special form of Spike-Timing-Dependent Plasticity which depresses synapses that did not recently contribute to the post-synaptic spike activation, regardless of their activation time. Competitive learning is implemented with lateral inhibition. When tested with real-life data, the system is able to extract complex and overlapping temporally correlated features such as car trajectories on a freeway, after only 10 min of traffic learning. Complete trajectories can be learned with a 98% detection rate using a second layer, still with unsupervised learning, and the system may be used as a car counter. The proposed neural network is extremely robust to noise and it can tolerate a high degree of synaptic and neuronal variability with little impact on performance. Such results show that a simple biologically inspired unsupervised learning scheme is capable of generating selectivity to complex meaningful events on the basis of relatively little sensory experience. Copyright © 2012 Elsevier Ltd. All rights reserved.

Sharing experiences: towards an evidence based model of dengue surveillance and outbreak response in Latin America and Asia

PubMed Central

2013-01-01

Background The increasing frequency and intensity of dengue outbreaks in endemic and non-endemic countries requires a rational, evidence based response. To this end, we aimed to collate the experiences of a number of affected countries, identify strengths and limitations in dengue surveillance, outbreak preparedness, detection and response and contribute towards the development of a model contingency plan adaptable to country needs. Methods The study was undertaken in five Latin American (Brazil, Colombia, Dominican Republic, Mexico, Peru) and five in Asian countries (Indonesia, Malaysia, Maldives, Sri Lanka, Vietnam). A mixed-methods approach was used which included document analysis, key informant interviews, focus-group discussions, secondary data analysis and consensus building by an international dengue expert meeting organised by the World Health Organization, Special Program for Research and Training in Tropical Diseases (WHO-TDR). Results Country information on dengue is based on compulsory notification and reporting (“passive surveillance”), with laboratory confirmation (in all participating Latin American countries and some Asian countries) or by using a clinical syndromic definition. Seven countries additionally had sentinel sites with active dengue reporting, some also had virological surveillance. Six had agreed a formal definition of a dengue outbreak separate to seasonal variation in case numbers. Countries collected data on a range of warning signs that may identify outbreaks early, but none had developed a systematic approach to identifying and responding to the early stages of an outbreak. Outbreak response plans varied in quality, particularly regarding the early response. The surge capacity of hospitals with recent dengue outbreaks varied; those that could mobilise additional staff, beds, laboratory support and resources coped best in comparison to those improvising a coping strategy during the outbreak. Hospital outbreak management plans were present in 9/22 participating hospitals in Latin-America and 8/20 participating hospitals in Asia. Conclusions Considerable variation between countries was observed with regard to surveillance, outbreak detection, and response. Through discussion at the expert meeting, suggestions were made for the development of a more standardised approach in the form of a model contingency plan, with agreed outbreak definitions and country-specific risk assessment schemes to initiate early response activities according to the outbreak phase. This would also allow greater cross-country sharing of ideas. PMID:23800243

Sharing experiences: towards an evidence based model of dengue surveillance and outbreak response in Latin America and Asia.

PubMed

Badurdeen, Shiraz; Valladares, David Benitez; Farrar, Jeremy; Gozzer, Ernesto; Kroeger, Axel; Kuswara, Novia; Ranzinger, Silvia Runge; Tinh, Hien Tran; Leite, Priscila; Mahendradhata, Yodi; Skewes, Ronald; Verrall, Ayesha

2013-06-24

The increasing frequency and intensity of dengue outbreaks in endemic and non-endemic countries requires a rational, evidence based response. To this end, we aimed to collate the experiences of a number of affected countries, identify strengths and limitations in dengue surveillance, outbreak preparedness, detection and response and contribute towards the development of a model contingency plan adaptable to country needs. The study was undertaken in five Latin American (Brazil, Colombia, Dominican Republic, Mexico, Peru) and five in Asian countries (Indonesia, Malaysia, Maldives, Sri Lanka, Vietnam). A mixed-methods approach was used which included document analysis, key informant interviews, focus-group discussions, secondary data analysis and consensus building by an international dengue expert meeting organised by the World Health Organization, Special Program for Research and Training in Tropical Diseases (WHO-TDR). Country information on dengue is based on compulsory notification and reporting ("passive surveillance"), with laboratory confirmation (in all participating Latin American countries and some Asian countries) or by using a clinical syndromic definition. Seven countries additionally had sentinel sites with active dengue reporting, some also had virological surveillance. Six had agreed a formal definition of a dengue outbreak separate to seasonal variation in case numbers. Countries collected data on a range of warning signs that may identify outbreaks early, but none had developed a systematic approach to identifying and responding to the early stages of an outbreak. Outbreak response plans varied in quality, particularly regarding the early response. The surge capacity of hospitals with recent dengue outbreaks varied; those that could mobilise additional staff, beds, laboratory support and resources coped best in comparison to those improvising a coping strategy during the outbreak. Hospital outbreak management plans were present in 9/22 participating hospitals in Latin-America and 8/20 participating hospitals in Asia. Considerable variation between countries was observed with regard to surveillance, outbreak detection, and response. Through discussion at the expert meeting, suggestions were made for the development of a more standardised approach in the form of a model contingency plan, with agreed outbreak definitions and country-specific risk assessment schemes to initiate early response activities according to the outbreak phase. This would also allow greater cross-country sharing of ideas.

Gaussian-based filters for detecting Martian dust devils

USGS Publications Warehouse

Yang, F.; Mlsna, P.A.; Geissler, P.

2006-01-01

The ability to automatically detect dust devils in the Martian atmosphere from orbital imagery is becoming important both for scientific studies of the planet and for the planning of future robotic and manned missions. This paper describes our approach for the unsupervised detection of dust devils and the preliminary results achieved to date. The algorithm centers upon the use of a filter constructed from Gaussian profiles to match dust devil characteristics over a range of scale and orientation. The classification step is designed to reduce false positive errors caused by static surface features such as craters. A brief discussion of planned future work is included. ?? 2006 IEEE.

The evaluation and application of multilocus variable number tandem repeat analysis (MLVA) for the molecular epidemiological study of Salmonella enterica subsp. enterica serovar Enteritidis infection.

PubMed

Liu, Yao; Shi, Xiaolu; Li, Yinghui; Chen, Qiongcheng; Jiang, Min; Li, Wanli; Qiu, Yaqun; Lin, Yiman; Jiang, Yixiang; Kan, Biao; Sun, Qun; Hu, Qinghua

2016-01-29

Salmonella enterica subsp. enterica serovar Enteritidis (S. Enteritidis) is one of the most prevalent Salmonella serotypes that cause gastroenteritis worldwide and the most prevalent serotype causing Salmonella infections in China. A rapid molecular typing method with high throughput and good epidemiological discrimination is urgently needed for detecting the outbreaks and finding the source for effective control of S. Enteritidis infections. In this study, 194 strains which included 47 from six outbreaks that were well-characterized epidemiologically were analyzed with pulse field gel electrophoresis (PFGE) and multilocus variable number tandem repeat analysis (MLVA). Seven VNTR loci published by the US Center for Disease Control and Prevention (CDC) were used to evaluate and develop MLVA scheme for S. Enteritidis molecular subtyping by comparing with PFGE, and then MLVA was applied to the suspected outbreaks detection. All S. Enteritidis isolates were analyzed with MLVA to establish a MLVA database in Shenzhen, Guangdong province, China to facilitate the detection of S. Enteritidis infection clusters. There were 33 MLVA types and 29 PFGE patterns among 147 sporadic isolates. These two measures had Simpson indices of 0.7701 and 0.8043, respectively, which did not differ significantly. Epidemiological concordance was evaluated by typing 47 isolates from six epidemiologically well-characterized outbreaks and it did not differ for PFGE and MLVA. We applied the well established MLVA method to detect two S. Enteritidis foodborne outbreaks and find their sources successfully in 2014. A MLVA database of 491 S. Enteritidis strains isolated from 2004 to 2014 was established for the surveillance of clusters in the future. MLVA typing of S. Enteritidis would be an effective tool for early warning and epidemiological surveillance of S. Enteritidis infections.

Genetic characterization of measles virus in the Philippines, 2008-2011.

PubMed

Centeno, Rex; Fuji, Naoko; Okamoto, Michiko; Dapat, Clyde; Saito, Mariko; Tandoc, Amado; Lupisan, Socorro; Oshitani, Hitoshi

2015-06-03

Large outbreaks of measles occurred in the Philippines in 2010 and 2011. Genetic analysis was performed to identify the genotype of measles virus (MeV) that was responsible for the large outbreaks. A total of 114 representative MeVs that were detected in the Philippines from 2008 to 2011 were analyzed by sequencing the C-terminal region of nucleocapsid (N) gene and partial hemagglutinin (H) gene and by inferring the phylogenetic trees. Genetic analysis showed that genotype D9 was the predominant circulating strain during the 4-year study period. Genotype D9 was detected in 23 samples (92%) by N gene sequencing and 93 samples (94%) by H gene analysis. Sporadic cases of genotype G3 MeV were identified in 2 samples (8%) by N gene sequencing and 6 samples (6%) by H gene analysis. Genotype G3 MeV was detected mainly in Panay Island in 2009 and 2010. Molecular clock analysis of N gene showed that the recent genotype D9 viruses that caused the big outbreaks in 2010 and 2011 diverged from a common ancestor in 2005 in one of the neighboring Southeast Asian countries, where D9 was endemic. These big outbreaks of measles resulted in a spillover and were associated with genotype D9 MeV importation to Japan and the USA. Genotype D9 MeV became endemic and caused two big outbreaks in the Philippines in 2010 and 2011. Genotype G3 MeV was detected sporadically with limited geographic distribution. This study highlights the importance of genetic analysis not only in helping with the assessment of measles elimination program in the country but also in elucidating the transmission dynamics of measles virus.

The Global Public Health Intelligence Network and early warning outbreak detection: a Canadian contribution to global public health.

PubMed

Mykhalovskiy, Eric; Weir, Lorna

2006-01-01

The recent SARS epidemic has renewed widespread concerns about the global transmission of infectious diseases. In this commentary, we explore novel approaches to global infectious disease surveillance through a focus on an important Canadian contribution to the area--the Global Public Health Intelligence Network (GPHIN). GPHIN is a cutting-edge initiative that draws on the capacity of the Internet and newly available 24/7 global news coverage of health events to create a unique form of early warning outbreak detection. This commentary outlines the operation and development of GPHIN and compares it to ProMED-mail, another Internet-based approach to global health surveillance. We argue that GPHIN has created an important shift in the relationship of public health and news information. By exiting the pyramid of official reporting, GPHIN has created a new monitoring technique that has disrupted national boundaries of outbreak notification, while creating new possibilities for global outbreak response. By incorporating news within the emerging apparatus of global infectious disease surveillance, GPHIN has effectively responded to the global media's challenge to official country reporting of outbreak and enhanced the effectiveness and credibility of international public health.

The economic burden of a Salmonella Thompson outbreak caused by smoked salmon in the Netherlands, 2012-2013.

PubMed

Suijkerbuijk, Anita W M; Bouwknegt, Martijn; Mangen, Marie-Josee J; de Wit, G Ardine; van Pelt, Wilfrid; Bijkerk, Paul; Friesema, Ingrid H M

2017-04-01

In 2012, the Netherlands experienced the most extensive food-related outbreak of Salmonella ever recorded. It was caused by smoked salmon contaminated with Salmonella Thompson during processing. In total, 1149 cases of salmonellosis were laboratory confirmed and reported to RIVM. Twenty percent of cases was hospitalised and four cases were reported to be fatal. The purpose of this study was to estimate total costs of the Salmonella Thompson outbreak. Data from a case-control study were used to estimate the cost-of-illness of reported cases (i.e. healthcare costs, patient costs and production losses). Outbreak control costs were estimated based on interviews with staff from health authorities. Using the Dutch foodborne disease burden and cost-of-illness model, we estimated the number of underestimated cases and the associated cost-of-illness. The estimated number of cases, including reported and underestimated cases was 21 123. Adjusted for underestimation, the total cost-of-illness would be €6.8 million (95% CI €2.5-€16.7 million) with productivity losses being the main cost driver. Adding outbreak control costs, the total outbreak costs are estimated at €7.5 million. In the Netherlands, measures are taken to reduce salmonella concentrations in food, but detection of contamination during food processing remains difficult. As shown, Salmonella outbreaks have the potential for a relatively high disease and economic burden for society. Early warning and close cooperation between the industry, health authorities and laboratories is essential for rapid detection, control of outbreaks, and to reduce disease and economic burden. © The Author 2016. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

Outbreak of invasive group A streptococcus: investigations using agar settle plates detect perineal shedding from a healthcare worker.

PubMed

Mahida, N; Prescott, K; Yates, C; Spencer, F; Weston, V; Boswell, T

2018-03-29

Outbreaks of group A streptococcus (GAS) infections may occur in healthcare settings. Transmission to patients is sometimes linked to colonized healthcare workers (HCWs) and/or a contaminated environment. To describe the investigation and control of an outbreak of healthcare-associated GAS on an elderly care medical ward, over six months. Four patients developed septicaemia due to GAS infection without a clinically obvious site of infection. The outbreak team undertook an investigation involving a retrospective review of GAS cases, prospective case finding, HCW screening and environmental sampling using both swabs and settle plates. Immediate control measures included source isolation and additional cleaning of the ward environment with a chlorine disinfectant and hydrogen peroxide. Prospective patient screening identified one additional patient with throat GAS carriage. Settle plate positivity for GAS was strongly associated with the presence of one individual HCW on the ward, who was subsequently found to have GAS perineal carriage. Contamination of a fabric-upholstered chair in an office adjacent to the ward, used by the HCW, was also detected. In total, three asymptomatic HCWs had throat GAS carriage and one HCW had both perineal and throat carriage. All isolates were typed as emm 28. This is the first outbreak report demonstrating the use of settle plates in a GAS outbreak investigation on a medical ward, to identify the likely source of the outbreak. Based on this report we recommend that both throat and perineal sites should be sampled if HCW screening is undertaken during an outbreak of GAS. Fabric, soft furnishings should be excluded from clinical areas as well as any adjacent offices because pathogenic bacteria such as GAS may contaminate this environment. Copyright © 2018 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

Reported waterborne outbreaks of gastrointestinal disease in Australia are predominantly associated with recreational exposure.

PubMed

Dale, Katie; Kirk, Martyn; Sinclair, Martha; Hall, Robert; Leder, Karin

2010-10-01

To examine the frequency and circumstances of reported waterborne outbreaks of gastroenteritis in Australia. Examination of data reported to OzFoodNet between 2001 and 2007. During these seven years, 6,515 gastroenteritis outbreaks were reported to OzFoodNet, most of which were classified as being transmitted person-to-person or from an unknown source. Fifty-four (0.83%) outbreaks were classified as either 'waterborne' or 'suspected waterborne', of which 78% (42/54) were attributed to recreational water and 19% (10/54) to drinking water. Of the drinking water outbreaks, implicated pathogens were found on all but one occasion and included Salmonella sp. (five outbreaks), Campylobacter jejuni (three outbreaks) and Giardia (one outbreak). There have been few waterborne outbreaks detected in Australia, and most of those reported have been associated with recreational exposure. However, there are difficulties in identifying and categorising gastroenteritis outbreaks, as well as in obtaining microbiological and epidemiological evidence, which can result in misclassification or underestimation of water-associated events. Gastroenteritis surveillance data show that, among reported water-associated gastroenteritis outbreaks in Australia, recreational exposure is currently more common than a drinking water source. However, ongoing surveillance for waterborne outbreaks is important, especially as drought conditions may necessitate replacement of conventional drinking water supplies with alternative water sources, which could incur potential for new health risks. © 2010 The Authors. Journal Compilation © 2010 Public Health Association of Australia.

Epidemiology and estimated costs of a large waterborne outbreak of norovirus infection in Sweden.

PubMed

Larsson, C; Andersson, Y; Allestam, G; Lindqvist, A; Nenonen, N; Bergstedt, O

2014-03-01

A large outbreak of norovirus (NoV) gastroenteritis caused by contaminated municipal drinking water occurred in Lilla Edet, Sweden, 2008. Epidemiological investigations performed using a questionnaire survey showed an association between consumption of municipal drinking water and illness (odds ratio 4·73, 95% confidence interval 3·53-6·32), and a strong correlation between the risk of being sick and the number of glasses of municipal water consumed. Diverse NoV strains were detected in stool samples from patients, NoV genotype I strains predominating. Although NoVs were not detected in water samples, coliphages were identified as a marker of viral contamination. About 2400 (18·5%) of the 13,000 inhabitants in Lilla Edet became ill. Costs associated with the outbreak were collected via a questionnaire survey given to organizations and municipalities involved in or affected by the outbreak. Total costs including sick leave, were estimated to be ∼8,700,000 Swedish kronor (∼€0·87 million).

Outbreak of viral gastroenteritis due to drinking water contaminated by Norwalk-like viruses.

PubMed

Kukkula, M; Maunula, L; Silvennoinen, E; von Bonsdorff, C H

1999-12-01

Heinävesi, a Finnish municipality with a population of 4860 inhabitants, had an outbreak of gastroenteritis in March 1998. On the basis of an epidemiologic survey, an estimated 1700-3000 cases of acute gastroenteritis occurred during the outbreak. Municipal water consumption was found to be associated with illness (risk ratio [RR]=3.5, 95% confidence interval, 3.11>RR>3.96). Norwalk-like virus (NLV) genogroup II (GGII) was identified in untreated water, treated water, and 4 tap water samples by use of reverse transcription-polymerase chain reaction. This was the first time NLVs had been detected in municipal tap water. Fifteen of 27 patient stool samples had NLV GGII, with an identical amplification product to that found in the water samples, indicating that the outbreak was caused by this virus. In some patients, NLV genogroup I was also encountered. This virus, however, could not be detected in the water samples. Inadequate chlorination contributed to the survival of the virus in the water.

Highly Pathogenic Avian Influenza Virus (H5N1) in Frozen Duck Carcasses, Germany, 2007

PubMed Central

Harder, Timm C.; Teuffert, Jürgen; Starick, Elke; Gethmann, Jörn; Grund, Christian; Fereidouni, Sasan; Durban, Markus; Bogner, Karl-Heinz; Neubauer-Juric, Antonie; Repper, Reinhard; Hlinak, Andreas; Engelhardt, Andreas; Nöckler, Axel; Smietanka, Krzysztof; Minta, Zenon; Kramer, Matthias; Globig, Anja; Mettenleiter, Thomas C.; Conraths, Franz J.

2009-01-01

We conducted phylogenetic and epidemiologic analyses to determine sources of outbreaks of highly pathogenic avian influenza virus (HPAIV), subtype H5N1, in poultry holdings in 2007 in Germany, and a suspected incursion of HPAIV into the food chain through contaminated deep-frozen duck carcasses. In summer 2007, HPAIV (H5N1) outbreaks in 3 poultry holdings in Germany were temporally, spatially, and phylogenetically linked to outbreaks in wild aquatic birds. Detection of HPAIV (H5N1) in frozen duck carcass samples of retained slaughter batches of 1 farm indicated that silent infection had occurred for some time before the incidental detection. Phylogenetic analysis established a direct epidemiologic link between HPAIV isolated from duck meat and strains isolated from 3 further outbreaks in December 2007 in backyard chickens that had access to uncooked offal from commercial deep-frozen duck carcasses. Measures that will prevent such undetected introduction of HPAIV (H5N1) into the food chain are urgently required. PMID:19193272

Unsupervised self-care predicts conduct problems: The moderating roles of hostile aggression and gender.

PubMed

Atherton, Olivia E; Schofield, Thomas J; Sitka, Angela; Conger, Rand D; Robins, Richard W

2016-04-01

Despite widespread speculation about the detrimental effect of unsupervised self-care on adolescent outcomes, little is known about which children are particularly prone to problem behaviors when left at home without adult supervision. The present research used data from a longitudinal study of 674 Mexican-origin children residing in the United States to examine the prospective effect of unsupervised self-care on conduct problems, and the moderating roles of hostile aggression and gender. Results showed that unsupervised self-care was related to increases over time in conduct problems such as lying, stealing, and bullying. However, unsupervised self-care only led to conduct problems for boys and for children with an aggressive temperament. The main and interactive effects held for both mother-reported and observational-rated hostile aggression and after controlling for potential confounds. Copyright © 2016 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Most Common Foodborne Pathogens and Mycotoxins on Fresh Produce: A Review of Recent Outbreaks.

PubMed

Yeni, F; Yavaş, S; Alpas, H; Soyer, Y

2016-07-03

Every year millions of people are affected and thousands of them die due to infections and intoxication as a result of foodborne outbreaks, which also cause billions of dollars' worth of damage, public health problems, and agricultural product loss. A considerable portion of these outbreaks is related to fresh produce and caused by foodborne pathogens on fresh produce and mycotoxins. Escherichia coli O104:H4 outbreak, occurred in Germany in 2011, has attracted a great attention on foodborne outbreaks caused by contaminated fresh produce, and especially the vulnerability and gaps in the early warning and notification networks in the surveillance systems in all around the world. In the frame of this paper, we reviewed the most common foodborne pathogens on fresh produce, traceback investigations of the outbreaks caused by these pathogens, and lastly international early warning and notification systems, including PulseNet International and Rapid Alert System for Food and Feed, aiming to detect foodborne outbreaks.

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.

Food-Borne Outbreak Investigation and Molecular Typing: High Diversity of Staphylococcus aureus Strains and Importance of Toxin Detection

PubMed Central

Denayer, Sarah; Nia, Yacine; Botteldoorn, Nadine

2017-01-01

Staphylococcus aureus is an important aetiological agent of food intoxications in the European Union as it can cause gastro-enteritis through the production of various staphylococcal enterotoxins (SEs) in foods. Reported enterotoxin dose levels causing food-borne illness are scarce and varying. Three food poisoning outbreaks due to enterotoxin-producing S. aureus strains which occurred in 2013 in Belgium are described. The outbreaks occurred in an elderly home, at a barbecue event and in a kindergarten and involved 28, 18, and six cases, respectively. Various food leftovers contained coagulase positive staphylococci (CPS). Low levels of staphylococcal enterotoxins ranging between 0.015 ng/g and 0.019 ng/g for enterotoxin A (SEA), and corresponding to 0.132 ng/g for SEC were quantified in the food leftovers for two of the reported outbreaks. Molecular typing of human and food isolates using pulsed-field gel electrophoresis (PFGE) and enterotoxin gene typing, confirmed the link between patients and the suspected foodstuffs. This also demonstrated the high diversity of CPS isolates both in the cases and in healthy persons carrying enterotoxin genes encoding emetic SEs for which no detection methods currently exist. For one outbreak, the investigation pointed out to the food handler who transmitted the outbreak strain to the food. Tools to improve staphylococcal food poisoning (SFP) investigations are presented. PMID:29261162

Evaluating Hospital-Based Surveillance for Outbreak Detection in Bangladesh: Analysis of Healthcare Utilization Data

PubMed Central

Nikolay, Birgit; Salje, Henrik; Sturm-Ramirez, Katharine; Azziz-Baumgartner, Eduardo; Homaira, Nusrat; Iuliano, A. Danielle; Paul, Repon C.; Hossain, M. Jahangir; Cauchemez, Simon; Gurley, Emily S.

2017-01-01

Background The International Health Regulations outline core requirements to ensure the detection of public health threats of international concern. Assessing the capacity of surveillance systems to detect these threats is crucial for evaluating a country’s ability to meet these requirements. Methods and Findings We propose a framework to evaluate the sensitivity and representativeness of hospital-based surveillance and apply it to severe neurological infectious diseases and fatal respiratory infectious diseases in Bangladesh. We identified cases in selected communities within surveillance hospital catchment areas using key informant and house-to-house surveys and ascertained where cases had sought care. We estimated the probability of surveillance detecting different sized outbreaks by distance from the surveillance hospital and compared characteristics of cases identified in the community and cases attending surveillance hospitals. We estimated that surveillance detected 26% (95% CI 18%–33%) of severe neurological disease cases and 18% (95% CI 16%–21%) of fatal respiratory disease cases residing at 10 km distance from a surveillance hospital. Detection probabilities decreased markedly with distance. The probability of detecting small outbreaks (three cases) dropped below 50% at distances greater than 26 km for severe neurological disease and at distances greater than 7 km for fatal respiratory disease. Characteristics of cases attending surveillance hospitals were largely representative of all cases; however, neurological disease cases aged <5 y or from the lowest socioeconomic group and fatal respiratory disease cases aged ≥60 y were underrepresented. Our estimates of outbreak detection rely on suspected cases that attend a surveillance hospital receiving laboratory confirmation of disease and being reported to the surveillance system. The extent to which this occurs will depend on disease characteristics (e.g., severity and symptom specificity) and surveillance resources. Conclusion We present a new approach to evaluating the sensitivity and representativeness of hospital-based surveillance, making it possible to predict its ability to detect emerging threats. PMID:28095468

Molecular Characterization of Two Major Dengue Outbreaks in Costa Rica.

PubMed

Soto-Garita, Claudio; Somogyi, Teresita; Vicente-Santos, Amanda; Corrales-Aguilar, Eugenia

2016-07-06

Dengue virus (DENV) (Flavivirus, Flaviviridae) is a reemerging arthropod-borne virus with a worldwide circulation, transmitted mainly by Aedes aegypti and Aedes albopictus mosquitoes. Since the first detection of its main transmitting vector in 1992 and the invasion of DENV-1 in 1993, Costa Rica has faced dengue outbreaks yearly. In 2007 and 2013, Costa Rica experienced two of the largest outbreaks in terms of total and severe cases. To provide genetic information about the etiologic agents producing these outbreaks, we conducted phylogenetic analysis of viruses isolated from human samples. A total of 23 DENV-1 and DENV-2 sequences were characterized. These analyses signaled that DENV-1 genotype V and DENV-2 American/Asian genotype were circulating in those outbreaks. Our results suggest that the 2007 and 2013 outbreak viral strains of DENV-1 and DENV-2 originated from nearby countries and underwent in situ microevolution. © The American Society of Tropical Medicine and Hygiene.

Giardiasis Outbreak Associated with Asymptomatic Food Handlers in New York State, 2015.

PubMed

Figgatt, Mary; Mergen, Kimberly; Kimelstein, Deborah; Mahoney, Danielle M; Newman, Alexandra; Nicholas, David; Ricupero, Kristen; Cafiero, Theresa; Corry, Daniel; Ade, Julius; Kurpiel, Philip; Madison-Antenucci, Susan; Anand, Madhu

2017-04-12

Giardia duodenalis is a protozoan that causes a gastrointestinal illness called giardiasis. Giardiasis outbreaks in the United States are most commonly associated with waterborne transmission and are less commonly associated with food, person-to-person, and zoonotic transmission. During June to September 2015, an outbreak of 20 giardiasis cases occurred and were epidemiologically linked to a local grocery store chain on Long Island, New York. Further investigation revealed three asymptomatic food handlers were infected with G. duodenalis , and one food handler and one case were coinfected with Cryptosporidium spp. Although G. duodenalis was not detected in food samples, Cryptosporidium was identified in samples of spinach dip and potato salad. The G. duodenalis assemblage and subtype from one of the food handlers matched two outbreak cases for which genotyping could be performed. This outbreak highlights the potential role of asymptomatically infected food handlers in giardiasis outbreaks.

Larval outbreaks in West Greenland: Instant and subsequent effects on tundra ecosystem productivity and CO2 exchange.

PubMed

Lund, Magnus; Raundrup, Katrine; Westergaard-Nielsen, Andreas; López-Blanco, Efrén; Nymand, Josephine; Aastrup, Peter

2017-02-01

Insect outbreaks can have important consequences for tundra ecosystems. In this study, we synthesise available information on outbreaks of larvae of the noctuid moth Eurois occulta in Greenland. Based on an extensive dataset from a monitoring programme in Kobbefjord, West Greenland, we demonstrate effects of a larval outbreak in 2011 on vegetation productivity and CO 2 exchange. We estimate a decreased carbon (C) sink strength in the order of 118-143 g C m -2 , corresponding to 1210-1470 tonnes C at the Kobbefjord catchment scale. The decreased C sink was, however, counteracted the following years by increased primary production, probably facilitated by the larval outbreak increasing nutrient turnover rates. Furthermore, we demonstrate for the first time in tundra ecosystems, the potential for using remote sensing to detect and map insect outbreak events.

Molecular Characterization of Two Major Dengue Outbreaks in Costa Rica

PubMed Central

Soto-Garita, Claudio; Somogyi, Teresita; Vicente-Santos, Amanda; Corrales-Aguilar, Eugenia

2016-01-01

Dengue virus (DENV) (Flavivirus, Flaviviridae) is a reemerging arthropod-borne virus with a worldwide circulation, transmitted mainly by Aedes aegypti and Aedes albopictus mosquitoes. Since the first detection of its main transmitting vector in 1992 and the invasion of DENV-1 in 1993, Costa Rica has faced dengue outbreaks yearly. In 2007 and 2013, Costa Rica experienced two of the largest outbreaks in terms of total and severe cases. To provide genetic information about the etiologic agents producing these outbreaks, we conducted phylogenetic analysis of viruses isolated from human samples. A total of 23 DENV-1 and DENV-2 sequences were characterized. These analyses signaled that DENV-1 genotype V and DENV-2 American/Asian genotype were circulating in those outbreaks. Our results suggest that the 2007 and 2013 outbreak viral strains of DENV-1 and DENV-2 originated from nearby countries and underwent in situ microevolution. PMID:27139442

Simulation-Based Evaluation of the Performances of an Algorithm for Detecting Abnormal Disease-Related Features in Cattle Mortality Records.

PubMed

Perrin, Jean-Baptiste; Durand, Benoît; Gay, Emilie; Ducrot, Christian; Hendrikx, Pascal; Calavas, Didier; Hénaux, Viviane

2015-01-01

We performed a simulation study to evaluate the performances of an anomaly detection algorithm considered in the frame of an automated surveillance system of cattle mortality. The method consisted in a combination of temporal regression and spatial cluster detection which allows identifying, for a given week, clusters of spatial units showing an excess of deaths in comparison with their own historical fluctuations. First, we simulated 1,000 outbreaks of a disease causing extra deaths in the French cattle population (about 200,000 herds and 20 million cattle) according to a model mimicking the spreading patterns of an infectious disease and injected these disease-related extra deaths in an authentic mortality dataset, spanning from January 2005 to January 2010. Second, we applied our algorithm on each of the 1,000 semi-synthetic datasets to identify clusters of spatial units showing an excess of deaths considering their own historical fluctuations. Third, we verified if the clusters identified by the algorithm did contain simulated extra deaths in order to evaluate the ability of the algorithm to identify unusual mortality clusters caused by an outbreak. Among the 1,000 simulations, the median duration of simulated outbreaks was 8 weeks, with a median number of 5,627 simulated deaths and 441 infected herds. Within the 12-week trial period, 73% of the simulated outbreaks were detected, with a median timeliness of 1 week, and a mean of 1.4 weeks. The proportion of outbreak weeks flagged by an alarm was 61% (i.e. sensitivity) whereas one in three alarms was a true alarm (i.e. positive predictive value). The performances of the detection algorithm were evaluated for alternative combination of epidemiologic parameters. The results of our study confirmed that in certain conditions automated algorithms could help identifying abnormal cattle mortality increases possibly related to unidentified health events.

Simulation-Based Evaluation of the Performances of an Algorithm for Detecting Abnormal Disease-Related Features in Cattle Mortality Records

PubMed Central

Perrin, Jean-Baptiste; Durand, Benoît; Gay, Emilie; Ducrot, Christian; Hendrikx, Pascal; Calavas, Didier; Hénaux, Viviane

2015-01-01

We performed a simulation study to evaluate the performances of an anomaly detection algorithm considered in the frame of an automated surveillance system of cattle mortality. The method consisted in a combination of temporal regression and spatial cluster detection which allows identifying, for a given week, clusters of spatial units showing an excess of deaths in comparison with their own historical fluctuations. First, we simulated 1,000 outbreaks of a disease causing extra deaths in the French cattle population (about 200,000 herds and 20 million cattle) according to a model mimicking the spreading patterns of an infectious disease and injected these disease-related extra deaths in an authentic mortality dataset, spanning from January 2005 to January 2010. Second, we applied our algorithm on each of the 1,000 semi-synthetic datasets to identify clusters of spatial units showing an excess of deaths considering their own historical fluctuations. Third, we verified if the clusters identified by the algorithm did contain simulated extra deaths in order to evaluate the ability of the algorithm to identify unusual mortality clusters caused by an outbreak. Among the 1,000 simulations, the median duration of simulated outbreaks was 8 weeks, with a median number of 5,627 simulated deaths and 441 infected herds. Within the 12-week trial period, 73% of the simulated outbreaks were detected, with a median timeliness of 1 week, and a mean of 1.4 weeks. The proportion of outbreak weeks flagged by an alarm was 61% (i.e. sensitivity) whereas one in three alarms was a true alarm (i.e. positive predictive value). The performances of the detection algorithm were evaluated for alternative combination of epidemiologic parameters. The results of our study confirmed that in certain conditions automated algorithms could help identifying abnormal cattle mortality increases possibly related to unidentified health events. PMID:26536596

Case-based surveillance enhanced with measles virus detection/genotyping is essential to maintain measles elimination in Aichi Prefecture, Japan.

PubMed

Minagawa, Hiroko; Yasui, Yoshihiro; Adachi, Hirokazu; Ito, Miyabi; Hirose, Emi; Nakamura, Noriko; Hata, Mami; Kobayashi, Shinichi; Yamashita, Teruo

2015-11-09

Japan was verified as having achieved measles elimination by the Measles Regional Verification Commission in the Western Pacific Region in March 2015. Verification of measles elimination implies the absence of continuous endemic transmission. After the last epidemic in 2007 with an estimated 18,000 cases, Japan introduced nationwide case-based measles surveillance in January 2008. Laboratory diagnosis for all suspected measles cases is essentially required by law, and virus detection tests are mostly performed by municipal public health institutes. Despite relatively high vaccination coverage and vigorous response to every case by the local health center staff, outbreak of measles is repeatedly observed in Aichi Prefecture, Japan. Measles virus N and H gene detection by nested double RT-PCR was performed with all specimens collected from suspected cases and transferred to our institute. Genotyping and further molecular epidemiological analyses were performed with the direct nucleotide sequence data of appropriate PCR products. Between 2010 and 2014, specimens from 389 patients suspected for measles were tested in our institute. Genotypes D9, D8, H1 and B3 were detected. Further molecular epidemiological analyses were helpful to establish links between patients, and sometimes useful to discriminate one outbreak from another. All virus-positive cases, including 49 cases involved in three outbreaks without any obvious epidemiological link with importation, were considered as import-related based on the nucleotide sequence information. Chain of transmission in the latest outbreak in 2014 terminated after the third generations, much earlier than the 2010-11 outbreak (6th generations). Since 2010, almost all measles cases reported in Aichi Prefecture are either import or import-related, based primarily on genotypes and nucleotide sequences of measles virus detected. In addition, genotyping and molecular epidemiological analyses are indispensable to prove the interruption of endemic transmission when the importations of measles are repeatedly observed. Copyright © 2015 Elsevier Ltd. All rights reserved.

Neglected waterborne parasitic protozoa and their detection in water.

PubMed

Plutzer, Judit; Karanis, Panagiotis

2016-09-15

Outbreak incidents raise the question of whether the less frequent aetiological agents of outbreaks are really less frequent in water. Alternatively, waterborne transmission could be relevant, but the lack of attention and rapid, sensitive methods to recover and detect the exogenous stages in water may keep them under-recognized. High quality information on the prevalence and detection of less frequent waterborne protozoa, such as Cyclospora cayetanensis, Toxoplasma gondii, Isospora belli, Balantidium coli, Blastocystis hominis, Entamoeba histolytica and other free-living amoebae (FLA), are not available. This present paper discusses the detection tools applied for the water surveillance of the neglected waterborne protozoa mentioned above and provides future perspectives. Copyright © 2016 Elsevier Ltd. All rights reserved.

Fresh Produce-Associated Listeriosis Outbreaks, Sources of Concern, Teachable Moments, and Insights.

PubMed

Garner, Danisha; Kathariou, Sophia

2016-02-01

Foodborne transmission of Listeria monocytogenes was first demonstrated through the investigation of the 1981 Maritime Provinces outbreak involving coleslaw. In the following two decades, most listeriosis outbreaks involved foods of animal origin, e.g., deli meats, hot dogs, and soft cheeses. L. monocytogenes serotype 4b, especially epidemic clones I, II, and Ia, were frequently implicated in these outbreaks. However, since 2008 several outbreaks have been linked to diverse types of fresh produce: sprouts, celery, cantaloupe, stone fruit, and apples. The 2011 cantaloupe-associated outbreak was one of the deadliest foodborne outbreaks in recent U.S. history. This review discusses produce-related outbreaks of listeriosis with a focus on special trends, unusual findings, and potential paradigm shifts. With the exception of sprouts, implicated produce types were novel, and outbreaks were one-time events. Several involved serotype 1/2a, and in the 2011 cantaloupe-associated outbreak, serotype 1/2b was for the first time conclusively linked to a common-source outbreak of invasive listeriosis. Also in this outbreak, for the first time multiple strains were implicated in a common-source outbreak. In 2014, deployment of whole genome sequencing as part of outbreak investigation validated this technique as a pivotal tool for outbreak detection and speedy resolution. In spite of the unusual attributes of produce-related outbreaks, in all but one of the investigated cases (the possible exception being the coleslaw outbreak) contamination was traced to the same sources as those for outbreaks associated with other vehicles (e.g., deli meats), i.e., the processing environment and equipment. The public health impact of farm-level contamination remains uncharacterized. This review highlights knowledge gaps regarding virulence and other potentially unique attributes of produce outbreak strains, the potential for novel fresh produce items to become unexpectedly implicated in outbreaks, and the key role of good control strategies in the processing environment.

Waterborne Pathogens: Detection Methods and Challenges

PubMed Central

Ramírez-Castillo, Flor Yazmín; Loera-Muro, Abraham; Jacques, Mario; Garneau, Philippe; Avelar-González, Francisco Javier; Harel, Josée; Guerrero-Barrera, Alma Lilián

2015-01-01

Waterborne pathogens and related diseases are a major public health concern worldwide, not only by the morbidity and mortality that they cause, but by the high cost that represents their prevention and treatment. These diseases are directly related to environmental deterioration and pollution. Despite the continued efforts to maintain water safety, waterborne outbreaks are still reported globally. Proper assessment of pathogens on water and water quality monitoring are key factors for decision-making regarding water distribution systems’ infrastructure, the choice of best water treatment and prevention waterborne outbreaks. Powerful, sensitive and reproducible diagnostic tools are developed to monitor pathogen contamination in water and be able to detect not only cultivable pathogens but also to detect the occurrence of viable but non-culturable microorganisms as well as the presence of pathogens on biofilms. Quantitative microbial risk assessment (QMRA) is a helpful tool to evaluate the scenarios for pathogen contamination that involve surveillance, detection methods, analysis and decision-making. This review aims to present a research outlook on waterborne outbreaks that have occurred in recent years. This review also focuses in the main molecular techniques for detection of waterborne pathogens and the use of QMRA approach to protect public health. PMID:26011827

Governance and One Health: Exploring the Impact of Federalism and Bureaucracy on Zoonotic Disease Detection and Reporting.

PubMed

Allen, Heather A

2015-05-13

The merits of One Health have been thoroughly described in the literature, but how One Health operates in the United States federal system of government is rarely discussed or analyzed. Through a comparative case-study approach, this research explores how federalism, bureaucratic behavior, and institutional design in the United States may influence zoonotic disease outbreak detection and reporting, a key One Health activity. Using theoretical and empirical literature, as well as a survey/interview instrument for individuals directly involved in a past zoonotic disease outbreak, the impacts of governance are discussed. As predicted in the theoretical literature, empirical findings suggest that federalism, institutional design, and bureaucracy may play a role in facilitating or impeding zoonotic disease outbreak detection and reporting. Regulatory differences across states as well as compartmentalization of information within agencies may impede disease detection. However, the impact may not always be negative: bureaucracies can also be adaptive; federalism allows states important opportunities for innovation. While acknowledging there are many other factors that also matter in zoonotic disease detection and reporting, this research is one of the first attempts to raise awareness in the literature and stimulate discussion on the intersection of governance and One Health.

Low West Nile virus circulation in wild birds in an area of recurring outbreaks in Southern France.

PubMed

Balança, Gilles; Gaidet, Nicolas; Savini, Giovanni; Vollot, Benjamin; Foucart, Antoine; Reiter, Paul; Boutonnier, Alain; Lelli, Rossella; Monicat, François

2009-12-01

West Nile virus (WNV) has a history of irregular but recurrent epizootics in countries of Mediterranean and of Central and Eastern Europe. We have investigated the temporal enzootic activity of WNV in free-ranging birds over a 3-year period in an area with sporadic occurrences of WNV outbreaks in Southern France. We conducted an intensive serologic survey on several wild bird populations (>4000 serum samples collected from 3300 birds) selected as potential indicators of the WNV circulation. WNV antibodies were detected by seroneutralization and/or plaque reduction neutralization in house sparrows, black-billed magpies, and scops owls, but these species appeared to be insufficient indicators of WNV circulation. Overall seroprevalence was low (<1%), including in birds that had been potentially exposed to the virus during recent outbreaks. However, the detection of a seroconversion in one bird, as well as the detection of seropositive birds in all years of our monitoring, including juveniles, indicate a constant annual circulation of WNV at a low level, including in years without any detectable emergence of WN fever in horses or humans.

Development and evaluation of a real-time RT-PCR assay for the detection of Ebola virus (Zaire) during an Ebola outbreak in Guinea in 2014-2015.

PubMed

Dedkov, V G; Magassouba, N' F; Safonova, M V; Deviatkin, A A; Dolgova, A S; Pyankov, O V; Sergeev, A A; Utkin, D V; Odinokov, G N; Safronov, V A; Agafonov, A P; Maleev, V V; Shipulin, G A

2016-02-01

In early February 2014, an outbreak of the Ebola virus disease caused by Zaire ebolavirus (EBOV) occurred in Guinea; cases were also recorded in other West African countries with a combined population of approximately 25 million. A rapid, sensitive and inexpensive method for detecting EBOV is needed to effectively control such outbreak. Here, we report a real-time reverse-transcription PCR assay for Z. ebolavirus detection used by the Specialized Anti-epidemic Team of the Russian Federation during the Ebola virus disease prevention mission in the Republic of Guinea. The analytical sensitivity of the assay is 5 × 10(2) viral particles per ml, and high specificity is demonstrated using representative sampling of viral, bacterial and human nucleic acids. This assay can be applied successfully for detecting the West African strains of Z. ebolavirus as well as on strains isolated in the Democratic Republic of the Congo in 2014. Copyright © 2015 Elsevier B.V. All rights reserved.

DETECTION OF OUTBREAK-ASSOCIATED HUMAN CALICIVIRUSES IN GROUNDWATER BY RT-PCR

EPA Science Inventory

Human caliciviruses (HuCV) are a major worldwide cause of food and waterborne outbreaks of acute nonbacterial gastroenteritis, and have been placed on the U.S. Environmental Protection Agency's (U.S. EPA) Contaminant Candidate List (CCL) of agents to be considered for regulatory ...

Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields.

PubMed

Yousefi, Siamak; Balasubramanian, Madhusudhanan; Goldbaum, Michael H; Medeiros, Felipe A; Zangwill, Linda M; Weinreb, Robert N; Liebmann, Jeffrey M; Girkin, Christopher A; Bowd, Christopher

2016-05-01

To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM-progression of patterns (POP) and VIM-POP). To compare GEM-POP and VIM-POP with other methods. GEM and VIM models separated cross-sectional abnormal VFs from 859 eyes and normal VFs from 1117 eyes into abnormal and normal clusters. Clusters were decomposed into independent axes. The confidence limit (CL) of stability was established for each axis with a set of 84 stable eyes. Sensitivity for detecting progression was assessed in a sample of 83 eyes with known progressive glaucomatous optic neuropathy (PGON). Eyes were classified as progressed if any defect pattern progressed beyond the CL of stability. Performance of GEM-POP and VIM-POP was compared to point-wise linear regression (PLR), permutation analysis of PLR (PoPLR), and linear regression (LR) of mean deviation (MD), and visual field index (VFI). Sensitivity and specificity for detecting glaucomatous VFs were 89.9% and 93.8%, respectively, for GEM and 93.0% and 97.0%, respectively, for VIM. Receiver operating characteristic (ROC) curve areas for classifying progressed eyes were 0.82 for VIM-POP, 0.86 for GEM-POP, 0.81 for PoPLR, 0.69 for LR of MD, and 0.76 for LR of VFI. GEM-POP was significantly more sensitive to PGON than PoPLR and linear regression of MD and VFI in our sample, while providing localized progression information. Detection of glaucomatous progression can be improved by assessing longitudinal changes in localized patterns of glaucomatous defect identified by unsupervised machine learning.

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.

Automated classification of dolphin echolocation click types from the Gulf of Mexico.

PubMed

Frasier, Kaitlin E; Roch, Marie A; Soldevilla, Melissa S; Wiggins, Sean M; Garrison, Lance P; Hildebrand, John A

2017-12-01

Delphinids produce large numbers of short duration, broadband echolocation clicks which may be useful for species classification in passive acoustic monitoring efforts. A challenge in echolocation click classification is to overcome the many sources of variability to recognize underlying patterns across many detections. An automated unsupervised network-based classification method was developed to simulate the approach a human analyst uses when categorizing click types: Clusters of similar clicks were identified by incorporating multiple click characteristics (spectral shape and inter-click interval distributions) to distinguish within-type from between-type variation, and identify distinct, persistent click types. Once click types were established, an algorithm for classifying novel detections using existing clusters was tested. The automated classification method was applied to a dataset of 52 million clicks detected across five monitoring sites over two years in the Gulf of Mexico (GOM). Seven distinct click types were identified, one of which is known to be associated with an acoustically identifiable delphinid (Risso's dolphin) and six of which are not yet identified. All types occurred at multiple monitoring locations, but the relative occurrence of types varied, particularly between continental shelf and slope locations. Automatically-identified click types from autonomous seafloor recorders without verifiable species identification were compared with clicks detected on sea-surface towed hydrophone arrays in the presence of visually identified delphinid species. These comparisons suggest potential species identities for the animals producing some echolocation click types. The network-based classification method presented here is effective for rapid, unsupervised delphinid click classification across large datasets in which the click types may not be known a priori.

Automated classification of dolphin echolocation click types from the Gulf of Mexico

PubMed Central

Roch, Marie A.; Soldevilla, Melissa S.; Wiggins, Sean M.; Garrison, Lance P.; Hildebrand, John A.

2017-01-01

Delphinids produce large numbers of short duration, broadband echolocation clicks which may be useful for species classification in passive acoustic monitoring efforts. A challenge in echolocation click classification is to overcome the many sources of variability to recognize underlying patterns across many detections. An automated unsupervised network-based classification method was developed to simulate the approach a human analyst uses when categorizing click types: Clusters of similar clicks were identified by incorporating multiple click characteristics (spectral shape and inter-click interval distributions) to distinguish within-type from between-type variation, and identify distinct, persistent click types. Once click types were established, an algorithm for classifying novel detections using existing clusters was tested. The automated classification method was applied to a dataset of 52 million clicks detected across five monitoring sites over two years in the Gulf of Mexico (GOM). Seven distinct click types were identified, one of which is known to be associated with an acoustically identifiable delphinid (Risso’s dolphin) and six of which are not yet identified. All types occurred at multiple monitoring locations, but the relative occurrence of types varied, particularly between continental shelf and slope locations. Automatically-identified click types from autonomous seafloor recorders without verifiable species identification were compared with clicks detected on sea-surface towed hydrophone arrays in the presence of visually identified delphinid species. These comparisons suggest potential species identities for the animals producing some echolocation click types. The network-based classification method presented here is effective for rapid, unsupervised delphinid click classification across large datasets in which the click types may not be known a priori. PMID:29216184

Typhoid fever outbreak associated with frozen mamey pulp imported from Guatemala to the western United States, 2010.

PubMed

Loharikar, Anagha; Newton, Anna; Rowley, Patricia; Wheeler, Charlotte; Bruno, Tami; Barillas, Haroldo; Pruckler, James; Theobald, Lisa; Lance, Susan; Brown, Jeffrey M; Barzilay, Ezra J; Arvelo, Wences; Mintz, Eric; Fagan, Ryan

2012-07-01

Fifty-four outbreaks of domestically acquired typhoid fever were reported between 1960 and 1999. In 2010, the Southern Nevada Health District detected an outbreak of typhoid fever among persons who had not recently travelled abroad. We conducted a case-control study to examine the relationship between illness and exposures. A case was defined as illness with the outbreak strain of Salmonella serotype Typhi, as determined by pulsed-field gel electrophoresis (PFGE), with onset during 2010. Controls were matched by neighborhood, age, and sex. Bivariate and multivariate statistical analyses were completed using logistic regression. Traceback investigation was completed. We identified 12 cases in 3 states with onset from 15 April 2010 to 4 September 2010. The median age of case patients was 18 years (range, 4-48 years), 8 (67%) were female, and 11 (92%) were Hispanic. Nine (82%) were hospitalized; none died. Consumption of frozen mamey pulp in a fruit shake was reported by 6 of 8 case patients (75%) and none of the 33 controls (matched odds ratio, 33.9; 95% confidence interval, 4.9). Traceback investigations implicated 2 brands of frozen mamey pulp from a single manufacturer in Guatemala, which was also implicated in a 1998-1999 outbreak of typhoid fever in Florida. Reporting of individual cases of typhoid fever and subtyping of isolates by PFGE resulted in rapid detection of an outbreak associated with a ready-to-eat frozen food imported from a typhoid-endemic region. Improvements in food manufacturing practices and monitoring will prevent additional outbreaks.

What is the utility of using syndromic surveillance systems during large subnational infectious gastrointestinal disease outbreaks? An observational study using case studies from the past 5 years in England.

PubMed

Todkill, D; Elliot, A J; Morbey, R; Harris, J; Hawker, J; Edeghere, O; Smith, G E

2016-08-01

Syndromic surveillance systems in England have demonstrated utility in the early identification of seasonal gastrointestinal illness (GI) tracking its spatio-temporal distribution and enabling early public health action. There would be additional public health utility if syndromic surveillance systems could detect or track subnational infectious disease outbreaks. To investigate using syndromic surveillance for this purpose we retrospectively identified eight large GI outbreaks between 2009 and 2014 (four randomly and four purposively sampled). We then examined syndromic surveillance information prospectively collected by the Real-time Syndromic Surveillance team within Public Health England for evidence of possible outbreak-related changes. None of the outbreaks were identified contemporaneously and no alerts were made to relevant public health teams. Retrospectively, two of the outbreaks - which happened at similar times and in proximal geographical locations - demonstrated changes in the local trends of relevant syndromic indicators and exhibited a clustering of statistical alarms, but did not warrant alerting local health protection teams. Our suite of syndromic surveillance systems may be more suited to their original purposes than as means of detecting or monitoring localized, subnational GI outbreaks. This should, however, be considered in the context of this study's limitations; further prospective work is needed to fully explore the use of syndromic surveillance for this purpose. Provided geographical coverage is sufficient, syndromic surveillance systems could be able to provide reassurance of no or minor excess healthcare systems usage during localized GI incidents.

Partial Failure of Milk Pasteurization as a Risk for the Transmission of Campylobacter From Cattle to Humans

PubMed Central

Fernandes, Anand M.; Balasegaram, Sooria; Willis, Caroline; Wimalarathna, Helen M. L.; Maiden, Martin C.; McCarthy, Noel D.

2015-01-01

Background. Cattle are the second most common source of human campylobacteriosis. However, routes to account for this scale of transmission have not been identified. In contrast to chicken, red meat is not heavily contaminated at point of sale. Although effective pasteurization prevents milk-borne infection, apparently sporadic infections may include undetected outbreaks from raw or perhaps incompletely pasteurized milk. Methods. A rise in Campylobacter gastroenteritis in an isolated population was investigated using whole-genome sequencing (WGS), an epidemiological study, and environmental investigations. Results. A single strain was identified in 20 cases, clearly distinguishable from other local strains and a reference population by WGS. A case-case analysis showed association of infection with the outbreak strain and milk from a single dairy (odds ratio, 8; Fisher exact test P value = .023). Despite temperature records indicating effective pasteurization, mechanical faults likely to lead to incomplete pasteurization of part of the milk were identified by further testing and examination of internal components of dairy equipment. Conclusions. Here, milk distribution concentrated on a small area, including school-aged children with low background incidence of campylobacteriosis, facilitated outbreak identification. Low-level contamination of widely distributed milk would not produce as detectable an outbreak signal. Such hidden outbreaks may contribute to the substantial burden of apparently sporadic Campylobacter from cattle where transmission routes are not certain. The effective discrimination of outbreak isolates from a reference population using WGS shows that integrating these data and approaches into surveillance could support the detection as well as investigation of such outbreaks. PMID:26063722

Detection of Zika virus using reverse-transcription LAMP coupled with reverse dot blot analysis in saliva

PubMed Central

Yasmin, Rubina; Barber, Cheryl A.; Castro, Talita; Malamud, Daniel; Kim, Beum Jun; Zhu, Hui; Montagna, Richard A.; Abrams, William R.

2018-01-01

In recent years, there have been increasing numbers of infectious disease outbreaks that spread rapidly to population centers resulting from global travel, population vulnerabilities, environmental factors, and ecological disasters such as floods and earthquakes. Some examples of the recent outbreaks are the Ebola epidemic in West Africa, Middle East respiratory syndrome coronavirus (MERS-Co) in the Middle East, and the Zika outbreak through the Americas. We have created a generic protocol for detection of pathogen RNA and/or DNA using loop-mediated isothermal amplification (LAMP) and reverse dot-blot for detection (RDB) and processed automatically in a microfluidic device. In particular, we describe how a microfluidic assay to detect HIV viral RNA was converted to detect Zika virus (ZIKV) RNA. We first optimized the RT-LAMP assay to detect ZIKV RNA using a benchtop isothermal amplification device. Then we implemented the assay in a microfluidic device that will allow analyzing 24 samples simultaneously and automatically from sample introduction to detection by RDB technique. Preliminary data using saliva samples spiked with ZIKV showed that our diagnostic system detects ZIKV RNA in saliva. These results will be validated in further experiments with well-characterized ZIKV human specimens of saliva. The described strategy and methodology to convert the HIV diagnostic assay and platform to a ZIKV RNA detection assay provides a model that can be readily utilized for detection of the next emerging or re-emerging infectious disease. PMID:29401479

Detection of Zika virus using reverse-transcription LAMP coupled with reverse dot blot analysis in saliva.

PubMed

Sabalza, Maite; Yasmin, Rubina; Barber, Cheryl A; Castro, Talita; Malamud, Daniel; Kim, Beum Jun; Zhu, Hui; Montagna, Richard A; Abrams, William R

2018-01-01

In recent years, there have been increasing numbers of infectious disease outbreaks that spread rapidly to population centers resulting from global travel, population vulnerabilities, environmental factors, and ecological disasters such as floods and earthquakes. Some examples of the recent outbreaks are the Ebola epidemic in West Africa, Middle East respiratory syndrome coronavirus (MERS-Co) in the Middle East, and the Zika outbreak through the Americas. We have created a generic protocol for detection of pathogen RNA and/or DNA using loop-mediated isothermal amplification (LAMP) and reverse dot-blot for detection (RDB) and processed automatically in a microfluidic device. In particular, we describe how a microfluidic assay to detect HIV viral RNA was converted to detect Zika virus (ZIKV) RNA. We first optimized the RT-LAMP assay to detect ZIKV RNA using a benchtop isothermal amplification device. Then we implemented the assay in a microfluidic device that will allow analyzing 24 samples simultaneously and automatically from sample introduction to detection by RDB technique. Preliminary data using saliva samples spiked with ZIKV showed that our diagnostic system detects ZIKV RNA in saliva. These results will be validated in further experiments with well-characterized ZIKV human specimens of saliva. The described strategy and methodology to convert the HIV diagnostic assay and platform to a ZIKV RNA detection assay provides a model that can be readily utilized for detection of the next emerging or re-emerging infectious disease.

Evaluation of a Multivariate Syndromic Surveillance System for West Nile Virus.

PubMed

Faverjon, Céline; Andersson, M Gunnar; Decors, Anouk; Tapprest, Jackie; Tritz, Pierre; Sandoz, Alain; Kutasi, Orsolya; Sala, Carole; Leblond, Agnès

2016-06-01

Various methods are currently used for the early detection of West Nile virus (WNV) but their outputs are not quantitative and/or do not take into account all available information. Our study aimed to test a multivariate syndromic surveillance system to evaluate if the sensitivity and the specificity of detection of WNV could be improved. Weekly time series data on nervous syndromes in horses and mortality in both horses and wild birds were used. Baselines were fitted to the three time series and used to simulate 100 years of surveillance data. WNV outbreaks were simulated and inserted into the baselines based on historical data and expert opinion. Univariate and multivariate syndromic surveillance systems were tested to gauge how well they detected the outbreaks; detection was based on an empirical Bayesian approach. The systems' performances were compared using measures of sensitivity, specificity, and area under receiver operating characteristic curve (AUC). When data sources were considered separately (i.e., univariate systems), the best detection performance was obtained using the data set of nervous symptoms in horses compared to those of bird and horse mortality (AUCs equal to 0.80, 0.75, and 0.50, respectively). A multivariate outbreak detection system that used nervous symptoms in horses and bird mortality generated the best performance (AUC = 0.87). The proposed approach is suitable for performing multivariate syndromic surveillance of WNV outbreaks. This is particularly relevant, given that a multivariate surveillance system performed better than a univariate approach. Such a surveillance system could be especially useful in serving as an alert for the possibility of human viral infections. This approach can be also used for other diseases for which multiple sources of evidence are available.

Specificity of coliphages in evaluating marker efficacy: a new insight for water quality indicators.

PubMed

Mookerjee, Subham; Batabyal, Prasenjit; Halder, Madhumanti; Palit, Anup

2014-11-01

Conventional procedures for qualitative assessment of coliphage are time consuming multiple step approach for achieving results. A modified and rapid technique has been introduced for determination of coliphage contamination among potable water sources during water borne outbreaks. During December 2013, 40 water samples from different potable water sources, were received for water quality analyses, from a jaundice affected Municipality of West Bengal, India. Altogether, 30% water samples were contaminated with coliform (1-20 cfu/ml) and 5% with E. coli (2-5 cfu/ml). Among post-outbreak samples, preponderance of coliform has decreased (1-4 cfu/ml) with total absence of E. coli. While standard technique has detected 55% outbreak samples with coliphage contamination, modified technique revealed that 80%, double than that of bacteriological identification rate, were contaminated with coliphages (4-20 pfu/10 ml). However, post-outbreak samples were detected with 1-5 pfu/10 ml coliphages among 20% samples. Coliphage detection rate through modified technique was nearly double (50%) than that of standard technique (27.5%). In few samples (with coliform load of 10-100 cfu/ml), while modified technique could detect coliphages among six samples (10-20 pfu/10 ml), standard protocol failed to detect coliphage in any of them. An easy, rapid and accurate modified technique has thereby been implemented for coliphage assessment from water samples. Coliform free water does not always signify pathogen free potable water and it is demonstrated that coliphage is a more reliable 'biomarker' to ascertain contamination level in potable water. Copyright © 2014 Elsevier B.V. All rights reserved.

Salmonella enteritidis surveillance by egg immunology: impact of the sampling scheme on the release of contaminated table eggs.

PubMed

Klinkenberg, Don; Thomas, Ekelijn; Artavia, Francisco F Calvo; Bouma, Annemarie

2011-08-01

Design of surveillance programs to detect infections could benefit from more insight into sampling schemes. We address the effect of sampling schemes for Salmonella Enteritidis surveillance in laying hens. Based on experimental estimates for the transmission rate in flocks, and the characteristics of an egg immunological test, we have simulated outbreaks with various sampling schemes, and with the current boot swab program with a 15-week sampling interval. Declaring a flock infected based on a single positive egg was not possible because test specificity was too low. Thus, a threshold number of positive eggs was defined to declare a flock infected, and, for small sample sizes, eggs from previous samplings had to be included in a cumulative sample to guarantee a minimum flock level specificity. Effectiveness of surveillance was measured by the proportion of outbreaks detected, and by the number of contaminated table eggs brought on the market. The boot swab program detected 90% of the outbreaks, with 75% fewer contaminated eggs compared to no surveillance, whereas the baseline egg program (30 eggs each 15 weeks) detected 86%, with 73% fewer contaminated eggs. We conclude that a larger sample size results in more detected outbreaks, whereas a smaller sampling interval decreases the number of contaminated eggs. Decreasing sample size and interval simultaneously reduces the number of contaminated eggs, but not indefinitely: the advantage of more frequent sampling is counterbalanced by the cumulative sample including less recently laid eggs. Apparently, optimizing surveillance has its limits when test specificity is taken into account. © 2011 Society for Risk Analysis.

Increased norovirus activity was associated with a novel norovirus GII.17 variant in Beijing, China during winter 2014-2015.

PubMed

Gao, Zhiyong; Liu, Baiwei; Huo, Da; Yan, Hanqiu; Jia, Lei; Du, Yiwei; Qian, Haikun; Yang, Yang; Wang, Xiaoli; Li, Jie; Wang, Quanyi

2015-12-18

Norovirus (NoV) is a leading cause of sporadic cases and outbreaks of acute gastroenteritis (AGE). Increased NoV activity was observed in Beijing, China during winter 2014-2015; therefore, we examined the epidemiological patterns and genetic characteristics of NoV in the sporadic cases and outbreaks. The weekly number of infectious diarrhea cases reported by all hospitals in Beijing was analyzed through the China information system for disease control and prevention. Fecal specimens were collected from the outbreaks and outpatients with AGE, and GI and GII NoVs were detected using real time reverse transcription polymerase chain reaction. The partial capsid genes and RNA-dependent RNA polymerase (RdRp) genes of NoV were both amplified and sequenced, and genotyping and phylogenetic analyses were performed. Between December 2014 and March 2015, the number of infectious diarrhea cases in Beijing (10,626 cases) increased by 35.6% over that of the previous year (7835 cases), and the detection rate of NoV (29.8%, 191/640) among outpatients with AGE was significantly higher than in the previous year (12.9%, 79/613) (χ(2) = 53.252, P < 0.001). Between November 2014 and March 2015, 35 outbreaks of AGE were reported in Beijing, and NoVs were detected in 33 outbreaks, all of which belonged to the GII genogroup. NoVs were sequenced and genotyped in 22 outbreaks, among which 20 were caused by a novel GII.17 strain. Among outpatients with AGE, this novel GII.17 strain was first detected in an outpatient in August 2014, and it replaced GII.4 Sydney_2012 as the predominant variant between December 2014 and March 2015. A phylogenetic analysis of the capsid genes and RdRp genes revealed that this novel GII.17 strain was distinct from previously identified GII variants, and it was recently designated as GII.P17_GII.17. This variant was further clustered into two sub-groups, named GII.17_2012 and GII.17_2014. During winter 2014-2015, GII.17_2014 caused the majority of AGE outbreaks in China and Japan. During winter 2014-2015, a novel NoV GII.17 variant replaced the GII.4 variant Sydney 2012 as the predominant strain in Beijing, China and caused increased NoV activity.

Detecting European Rabbit ( Oryctolagus cuniculus) Disease Outbreaks by Monitoring Digital Media.

PubMed

Peacock, David E; Grillo, Tiggy L

2018-04-18

  Digital media and digital search tools offer simple and effective means to monitor for pathogens and disease outbreaks in target organisms. Using tools such as Rich Site Summary feeds, and Google News and Google Scholar specific key word searches, international digital media were actively monitored from 2012 to 2016 for pathogens and disease outbreaks in the taxonomic order Lagomorpha, with a specific focus on the European rabbit ( Oryctolagus cuniculus). The primary objective was identifying pathogens for assessment as potential new biocontrol agents for Australia's pest populations of the European rabbit. A number of pathogens were detected in digital media reports. Additional benefits arose in the regular provision of case reports and research on myxomatosis and rabbit haemorrhagic disease virus that assisted with current research.

Trigger events: enviroclimatic coupling of Ebola hemorrhagic fever outbreaks

NASA Technical Reports Server (NTRS)

Pinzon, Jorge E.; Wilson, James M.; Tucker, Compton J.; Arthur, Ray; Jahrling, Peter B.; Formenty, Pierre

2004-01-01

We use spatially continuous satellite data as a correlate of precipitation within tropical Africa and show that the majority of documented Ebola hemorrhagic fever outbreaks were closely associated with sharply drier conditions at the end of the rainy season. We propose that these trigger events may enhance transmission of Ebola virus from its cryptic reservoir to humans. These findings suggest specific directions to help understand the sylvatic cycle of the virus and may provide early warning tools to detect possible future outbreaks of this enigmatic disease.

Detection of a chikungunya outbreak in Central Italy, August to September 2017.

PubMed

Venturi, Giulietta; Di Luca, Marco; Fortuna, Claudia; Remoli, Maria Elena; Riccardo, Flavia; Severini, Francesco; Toma, Luciano; Del Manso, Martina; Benedetti, Eleonora; Caporali, Maria Grazia; Amendola, Antonello; Fiorentini, Cristiano; De Liberato, Claudio; Giammattei, Roberto; Romi, Roberto; Pezzotti, Patrizio; Rezza, Giovanni; Rizzo, Caterina

2017-09-01

An autochthonous chikungunya outbreak is ongoing near Anzio, a coastal town in the province of Rome. The virus isolated from one patient and mosquitoes lacks the A226V mutation and belongs to an East Central South African strain. As of 20 September, 86 cases are laboratory-confirmed. The outbreak proximity to the capital, its late summer occurrence, and diagnostic delays, are favouring transmission. Vector control, enhanced surveillance and restricted blood donations are being implemented in affected areas.

Automated and unsupervised detection of malarial parasites in microscopic images.

PubMed

Purwar, Yashasvi; Shah, Sirish L; Clarke, Gwen; Almugairi, Areej; Muehlenbachs, Atis

2011-12-13

Malaria is a serious infectious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard. However due to the number of steps required in manual assessment, this diagnostic method is time consuming (leading to late diagnosis) and prone to human error (leading to erroneous diagnosis), even in experienced hands. The focus of this study is to develop a robust, unsupervised and sensitive malaria screening technique with low material cost and one that has an advantage over other techniques in that it minimizes human reliance and is, therefore, more consistent in applying diagnostic criteria. A method based on digital image processing of Giemsa-stained thin smear image is developed to facilitate the diagnostic process. The diagnosis procedure is divided into two parts; enumeration and identification. The image-based method presented here is designed to automate the process of enumeration and identification; with the main advantage being its ability to carry out the diagnosis in an unsupervised manner and yet have high sensitivity and thus reducing cases of false negatives. The image based method is tested over more than 500 images from two independent laboratories. The aim is to distinguish between positive and negative cases of malaria using thin smear blood slide images. Due to the unsupervised nature of method it requires minimal human intervention thus speeding up the whole process of diagnosis. Overall sensitivity to capture cases of malaria is 100% and specificity ranges from 50-88% for all species of malaria parasites. Image based screening method will speed up the whole process of diagnosis and is more advantageous over laboratory procedures that are prone to errors and where pathological expertise is minimal. Further this method provides a consistent and robust way of generating the parasite clearance curves.

Doppler weather radar detects emigratory flights of noctuids during a major pest outbreak

USDA-ARS?s Scientific Manuscript database

An outbreak of beet armyworm (Spodoptera exigua (Hübner)), cabbage looper, (Trichoplusia ni (Hübner)), and other lepidopteran pests devastated cotton production in the Lower Rio Grande Valley TX, in 1995. Major infestations occurred later in the year several hundred kilometers away in other cotton ...

A MULTIPLEX REVERSE TRANSCIPTION-PCR METHOD FOR DETECTION OF HUMAN ENTERIC VIRUSES IN GROUNDWATER

EPA Science Inventory

Untreated groundwater is responsible for about half of the waterborne disease outbreaks in the United States. Human enteric viruses are thought to be leading etiological agents of many of these outbreaks, but there is relatively little information on the types and levels of viru...

Economic costs of outbreaks of acute viral gastroenteritis due to norovirus in Catalonia (Spain), 2010-2011.

PubMed

Navas, Encarna; Torner, Nuria; Broner, Sonia; Godoy, Pere; Martínez, Ana; Bartolomé, Rosa; Domínguez, Angela

2015-10-01

To determine the direct and indirect costs of outbreaks of acute viral gastroenteritis (AVG) due to norovirus in closed institutions (hospitals, social health centers or nursing homes) and the community in Catalonia in 2010-11. Information on outbreaks were gathered from the reports made by epidemiological surveillance units. Direct costs (medical visits, hospital stays, drug treatment, sample processing, transport, diagnostic tests, monitoring and control of the outbreaks investigated) and indirect costs (lost productivity due to work absenteeism, caregivers time and working hours lost due to medical visits) were calculated. Twenty-seven outbreaks affecting 816 people in closed institutions and 74 outbreaks affecting 1,940 people in the community were detected. The direct and indirect costs of outbreaks were € 131,997.36 (€ 4,888.79 per outbreak) in closed institutions and € 260,557.16 (€ 3,521.04 per outbreak) in community outbreaks. The cost per case was € 161.76 in outbreaks in closed institutions and € 134.31 in community outbreaks. The main costs were surveillance unit monitoring (€ 116,652.93), laboratory diagnoses (€ 119,950.95), transport of samples (€ 69,970.90), medical visits (€ 25,250.50) and hospitalization (€ 13,400.00). The cost of outbreaks of acute viral gastroenteritis due to norovirus obtained in this study was influenced by the number of people affected and the severity of the outbreak, which determined hospitalizations and work absenteeism. Urgent reporting of outbreaks would allow the implementation of control measures that could reduce the numbers affected and the duration of the illness and thus the costs derived from them.

Value of evidence from syndromic surveillance with cumulative evidence from multiple data streams with delayed reporting.

PubMed

Struchen, R; Vial, F; Andersson, M G

2017-04-26

Delayed reporting of health data may hamper the early detection of infectious diseases in surveillance systems. Furthermore, combining multiple data streams, e.g. aiming at improving a system's sensitivity, can be challenging. In this study, we used a Bayesian framework where the result is presented as the value of evidence, i.e. the likelihood ratio for the evidence under outbreak versus baseline conditions. Based on a historical data set of routinely collected cattle mortality events, we evaluated outbreak detection performance (sensitivity, time to detection, in-control run length) under the Bayesian approach among three scenarios: presence of delayed data reporting, but not accounting for it; presence of delayed data reporting accounted for; and absence of delayed data reporting (i.e. an ideal system). Performance on larger and smaller outbreaks was compared with a classical approach, considering syndromes separately or combined. We found that the Bayesian approach performed better than the classical approach, especially for the smaller outbreaks. Furthermore, the Bayesian approach performed similarly well in the scenario where delayed reporting was accounted for to the scenario where it was absent. We argue that the value of evidence framework may be suitable for surveillance systems with multiple syndromes and delayed reporting of data.

Electronic network for monitoring travellers' diarrhoea and detection of an outbreak caused by Salmonella enteritidis among overseas travellers.

PubMed

Osaka, K; Inouye, S; Okabe, N; Taniguchi, K; Izumiya, H; Watanabe, H; Matsumoto, Y; Yokota, T; Hashimoto, S; Sagara, H

1999-12-01

The Traveller's Diarrhoea Network, by which the Infectious Disease Surveillance Center is electronically connected with two major airport quarantine stations and three infectious disease hospitals, was launched in February 1988 in Japan. The data on travellers' diarrhoea detected is reported weekly by e-mail. Two clusters of infection among travellers returning from Italy were reported by two airport quarantine stations at the end of September 1998. A total of 12 salmonella isolates from 2 clusters were examined. All were identified as Salmonella enteritidis, phage type 4 and showed identical banding patterns on pulsed-field gel electrophoresis. A case-control study showed that the scrambled eggs served at the hotel restaurant in Rome were the likely source of this outbreak. This outbreak could not have been detected promptly and investigated easily without the e-mail network. International exchange of data on travellers' diarrhoea is important for preventing and controlling food-borne illnesses infected abroad.

Detection of land-use and land cover changes in Franklin, Gulf, and Liberty Counties, Florida, with multitemporal landsat thematic mapper images

Treesearch

Shufen Pan; Guiying Li

2007-01-01

Florida Panhandle region has been experiencing rapid land transformation in the recent decades. To quantify land use and land-cover (LULC) changes and other landscape changes in this area, three counties including Franklin, Liberty and Gulf were taken as a case study and an unsupervised classification approach implemented to Landsat TM images acquired from 1985 to 2005...

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

DOE PAGES

Waytowich, Nicholas R.; Lawhern, Vernon J.; Bohannon, Addison W.; ...

2016-09-22

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry,STIG),which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIGmore » method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as out perform traditional within-subject calibration techniques when limited data is available. Here, this method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.« less

Online adaptation of a c-VEP Brain-computer Interface(BCI) based on error-related potentials and unsupervised learning.

PubMed

Spüler, Martin; Rosenstiel, Wolfgang; Bogdan, Martin

2012-01-01

The goal of a Brain-Computer Interface (BCI) is to control a computer by pure brain activity. Recently, BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present a c-VEP BCI that uses online adaptation of the classifier to reduce calibration time and increase performance. We compare two different approaches for online adaptation of the system: an unsupervised method and a method that uses the detection of error-related potentials. Both approaches were tested in an online study, in which an average accuracy of 96% was achieved with adaptation based on error-related potentials. This accuracy corresponds to an average information transfer rate of 144 bit/min, which is the highest bitrate reported so far for a non-invasive BCI. In a free-spelling mode, the subjects were able to write with an average of 21.3 error-free letters per minute, which shows the feasibility of the BCI system in a normal-use scenario. In addition we show that a calibration of the BCI system solely based on the detection of error-related potentials is possible, without knowing the true class labels.

Advanced Unsupervised Classification Methods to Detect Anomalies on Earthen Levees Using Polarimetric SAR Imagery

PubMed Central

Marapareddy, Ramakalavathi; Aanstoos, James V.; Younan, Nicolas H.

2016-01-01

Fully polarimetric Synthetic Aperture Radar (polSAR) data analysis has wide applications for terrain and ground cover classification. The dynamics of surface and subsurface water events can lead to slope instability resulting in slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We used L-band Synthetic Aperture Radar (SAR) to screen levees for anomalies. SAR technology, due to its high spatial resolution and soil penetration capability, is a good choice for identifying problematic areas on earthen levees. Using the parameters entropy (H), anisotropy (A), alpha (α), and eigenvalues (λ, λ1, λ2, and λ3), we implemented several unsupervised classification algorithms for the identification of anomalies on the levee. The classification techniques applied are H/α, H/A, A/α, Wishart H/α, Wishart H/A/α, and H/α/λ classification algorithms. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers. PMID:27322270

An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture.

PubMed

Doulamis, A; Doulamis, N; Ntalianis, K; Kollias, S

2003-01-01

In this paper, an unsupervised video object (VO) segmentation and tracking algorithm is proposed based on an adaptable neural-network architecture. The proposed scheme comprises: 1) a VO tracking module and 2) an initial VO estimation module. Object tracking is handled as a classification problem and implemented through an adaptive network classifier, which provides better results compared to conventional motion-based tracking algorithms. Network adaptation is accomplished through an efficient and cost effective weight updating algorithm, providing a minimum degradation of the previous network knowledge and taking into account the current content conditions. A retraining set is constructed and used for this purpose based on initial VO estimation results. Two different scenarios are investigated. The first concerns extraction of human entities in video conferencing applications, while the second exploits depth information to identify generic VOs in stereoscopic video sequences. Human face/ body detection based on Gaussian distributions is accomplished in the first scenario, while segmentation fusion is obtained using color and depth information in the second scenario. A decision mechanism is also incorporated to detect time instances for weight updating. Experimental results and comparisons indicate the good performance of the proposed scheme even in sequences with complicated content (object bending, occlusion).

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

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

Waytowich, Nicholas R.; Lawhern, Vernon J.; Bohannon, Addison W.

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry,STIG),which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIGmore » method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as out perform traditional within-subject calibration techniques when limited data is available. Here, this method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.« less

Mapping Neglected Swimming Pools from Satellite Data for Urban Vector Control

NASA Astrophysics Data System (ADS)

Barker, C. M.; Melton, F. S.; Reisen, W. K.

2010-12-01

Neglected swimming pools provide suitable breeding habit for mosquitoes, can contain thousands of mosquito larvae, and present both a significant nuisance and public health risk due to their inherent proximity to urban and suburban populations. The rapid increase and sustained rate of foreclosures in California associated with the recent recession presents a challenge for vector control districts seeking to identify, treat, and monitor neglected pools. Commercial high resolution satellite imagery offers some promise for mapping potential neglected pools, and for mapping pools for which routine maintenance has been reestablished. We present progress on unsupervised classification techniques for mapping both neglected pools and clean pools using high resolution commercial satellite data and discuss the potential uses and limitations of this data source in support of vector control efforts. An unsupervised classification scheme that utilizes image segmentation, band thresholds, and a change detection approach was implemented for sample regions in Coachella Valley, CA and the greater Los Angeles area. Comparison with field data collected by vector control personal was used to assess the accuracy of the estimates. The results suggest that the current system may provide some utility for early detection, or cost effective and time efficient annual monitoring, but additional work is required to address spectral and spatial limitations of current commercial satellite sensors for this purpose.

Environmental Survey of Drinking Water Sources in Kampala, Uganda, during a Typhoid Fever Outbreak.

PubMed

Murphy, J L; Kahler, A M; Nansubuga, I; Nanyunja, E M; Kaplan, B; Jothikumar, N; Routh, J; Gómez, G A; Mintz, E D; Hill, V R

2017-12-01

In 2015, a typhoid fever outbreak began in downtown Kampala, Uganda, and spread into adjacent districts. In response, an environmental survey of drinking water source types was conducted in areas of the city with high case numbers. A total of 122 samples was collected from 12 source types and tested for Escherichia coli , free chlorine, and conductivity. An additional 37 grab samples from seven source types and 16 paired large volume (20 liter) samples from wells and springs were also collected and tested for the presence of Salmonella enterica serovar Typhi. Escherichia coli was detected in 60% of kaveras (drinking water sold in plastic bags) and 80% of refilled water bottles; free chlorine was not detected in either source type. Most jerry cans (68%) contained E. coli and had free chlorine residuals below the WHO-recommended level of 0.5 mg/liter during outbreaks. Elevated conductivity readings for kaveras, refilled water bottles, and jerry cans (compared to treated surface water supplied by the water utility) suggested that they likely contained untreated groundwater. All unprotected springs and wells and more than 60% of protected springs contained E. coli Water samples collected from the water utility were found to have acceptable free chlorine levels and no detectable E. coli While S Typhi was not detected in water samples, Salmonella spp. were detected in samples from two unprotected springs, one protected spring, and one refilled water bottle. These data provided clear evidence that unregulated vended water and groundwater represented a risk for typhoid transmission. IMPORTANCE Despite the high incidence of typhoid fever globally, relatively few outbreak investigations incorporate drinking water testing. During waterborne disease outbreaks, measurement of physical-chemical parameters, such as free chlorine residual and electrical conductivity, and of microbiological parameters, such as the presence of E. coli or the implicated etiologic agent, in drinking water samples can identify contaminated sources. This investigation indicated that unregulated vended water and groundwater sources were contaminated and were therefore a risk to consumers during the 2015 typhoid fever outbreak in Kampala. Identification of contaminated drinking water sources and sources that do not contain adequate disinfectant levels can lead to rapid targeted interventions. Copyright © 2017 American Society for Microbiology.

[Enzyme-linked immunosorbent assay (ELISA) for detection of antibodies to Salmonella Typhi lipopolysaccharide O and capsular polysaccharide Vi antigens in persons from outbreak of typhoid fever].

PubMed

Rastawicki, Waldemar; Kałużewski, Stanisław

2015-01-01

The laboratory diagnosis of typhoid fever is dependent upon either isolation of S. Typhi from a clinical sample or the detection of raised titers of serum antibodies in the Widal test or the passive hemagglutination assay (PHA). In this study we evaluated the usefulness of ELISA for detection of antibodies to S. Typhi lipopolysaccharide O and capsular polysaccharide Vi antigens in the sera of persons from outbreak of typhoid fever. Fifteen serum samples from patients with laboratory confirmed typhoid fever and 140 sera from persons suspected for contact with typhoid fever patients from outbreak in 1974/75 in Poland were tested by ELISA. Additionally, as the control group, we tested 115 sera from blood donors for the presence of S. Typhi anti-LPS and anti-Vi antibodies. Anti-LPS and anti-Vi antibodies were detected in 80% and 53.3% of sera obtained from patients with laboratory confirmed typhoid fever, respectively. The high percentages of positive results in ELISA were also noted in the group of persons suspected for contact with typhoid fever patients (51.4% and 45%) but not in the group of blood donors (7.8% and 6.1%, respectively). The ELISA could be a useful tool for the serological diagnosis of typhoid fever in patients who have clinical symptoms but are culture negative, especially during massive outbreaks of typhoid fever.

A neural-visualization IDS for honeynet data.

PubMed

Herrero, Álvaro; Zurutuza, Urko; Corchado, Emilio

2012-04-01

Neural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspection of the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and comparison of the proposed projection methods are performed in a real domain, where two different case studies are defined and analyzed.

Flow Cytometry Data Preparation Guidelines for Improved Automated Phenotypic Analysis.

PubMed

Jimenez-Carretero, Daniel; Ligos, José M; Martínez-López, María; Sancho, David; Montoya, María C

2018-05-15

Advances in flow cytometry (FCM) increasingly demand adoption of computational analysis tools to tackle the ever-growing data dimensionality. In this study, we tested different data input modes to evaluate how cytometry acquisition configuration and data compensation procedures affect the performance of unsupervised phenotyping tools. An analysis workflow was set up and tested for the detection of changes in reference bead subsets and in a rare subpopulation of murine lymph node CD103 + dendritic cells acquired by conventional or spectral cytometry. Raw spectral data or pseudospectral data acquired with the full set of available detectors by conventional cytometry consistently outperformed datasets acquired and compensated according to FCM standards. Our results thus challenge the paradigm of one-fluorochrome/one-parameter acquisition in FCM for unsupervised cluster-based analysis. Instead, we propose to configure instrument acquisition to use all available fluorescence detectors and to avoid integration and compensation procedures, thereby using raw spectral or pseudospectral data for improved automated phenotypic analysis. Copyright © 2018 by The American Association of Immunologists, Inc.

Unsupervised spike sorting based on discriminative subspace learning.

PubMed

Keshtkaran, Mohammad Reza; Yang, Zhi

2014-01-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.

A large outbreak of typhoid fever associated with a high rate of intestinal perforation in Kasese District, Uganda, 2008-2009.

PubMed

Neil, Karen P; Sodha, Samir V; Lukwago, Luswa; O-Tipo, Shikanga; Mikoleit, Matthew; Simington, Sherricka D; Mukobi, Peter; Balinandi, Stephen; Majalija, Samuel; Ayers, Joseph; Kagirita, Atek; Wefula, Edward; Asiimwe, Frank; Kweyamba, Vianney; Talkington, Deborah; Shieh, Wun-Ju; Adem, Patricia; Batten, Brigid C; Zaki, Sherif R; Mintz, Eric

2012-04-01

Salmonella enterica serovar Typhi (Salmonella Typhi) causes an estimated 22 million typhoid fever cases and 216 000 deaths annually worldwide. In Africa, the lack of laboratory diagnostic capacity limits the ability to recognize endemic typhoid fever and to detect outbreaks. We report a large laboratory-confirmed outbreak of typhoid fever in Uganda with a high proportion of intestinal perforations (IPs). A suspected case of typhoid fever was defined as fever and abdominal pain in a person with either vomiting, diarrhea, constipation, headache, weakness, arthralgia, poor response to antimalarial medications, or IP. From March 4, 2009 to April 17, 2009, specimens for blood and stool cultures and serology were collected from suspected cases. Antimicrobial susceptibility testing and pulsed-field gel electrophoresis (PFGE) were performed on Salmonella Typhi isolates. Surgical specimens from patients with IP were examined. A community survey was conducted to characterize the extent of the outbreak. From December 27, 2007 to July 30, 2009, 577 cases, 289 hospitalizations, 249 IPs, and 47 deaths from typhoid fever occurred; Salmonella Typhi was isolated from 27 (33%) of 81 patients. Isolates demonstrated multiple PFGE patterns and uniform susceptibility to ciprofloxacin. Surgical specimens from 30 patients were consistent with typhoid fever. Estimated typhoid fever incidence in the community survey was 8092 cases per 100 000 persons. This typhoid fever outbreak was detected because of an elevated number of IPs. Underreporting of milder illnesses and delayed and inadequate antimicrobial treatment contributed to the high perforation rate. Enhancing laboratory capacity for detection is critical to improving typhoid fever control.

Silent Circulation of St. Louis Encephalitis Virus Prior to an Encephalitis Outbreak in Cordoba, Argentina (2005)

PubMed Central

Díaz, Luis Adrian; Albrieu Llinás, Guillermo; Vázquez, Ana; Tenorio, Antonio; Contigiani, Marta Silvia

2012-01-01

St. Louis encephalitis virus is a complex zoonoses. In 2005, 47 laboratory-confirmed and probable clinical cases of SLEV infection were reported in Córdoba, Argentina. Although the causes of 2005 outbreak remain unknown, they might be related not only to virological factors, but also to ecological and environmental conditions. We hypothesized that one of the factors for SLE reemergence in Córdoba, Argentina, was the introduction of a new SLEV genotype (SLEV genotype III), with no previous activity in the area. In order to evaluate this hypothesis we carried out a molecular characterization of SLEV detections from mosquitoes collected between 2001 and 2004 in Córdoba city. A total of 315 mosquito pools (11,002 individuals) including 12 mosquitoes species were analyzed. Overall, 20 pools (8 mosquitoes species) were positive for SLEV. During this study, genotypes II, V and VII were detected. No mosquito pool infected with genotype III was detected before the 2005 outbreak. Genotype V was found every year and in the 8 sampled sites. Genotypes II and VII showed limited temporal and spatial activities. We cannot dismiss the association of genotype II and V as etiological agents during the outbreak. However, the silent circulation of other SLEV strains in Córdoba city before the 2005 outbreak suggests that the introduction of genotype III was an important factor associated to this event. Not mutually exclusive, other factors such as changes in avian hosts and mosquitoes vectors communities, driven by climatic and environmental modifications, should also be taken into consideration in further studies. PMID:22303490

Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks

PubMed Central

Thompson, Robin N.; Gilligan, Christopher A.; Cunniffe, Nik J.

2016-01-01

We assess how presymptomatic infection affects predictability of infectious disease epidemics. We focus on whether or not a major outbreak (i.e. an epidemic that will go on to infect a large number of individuals) can be predicted reliably soon after initial cases of disease have appeared within a population. For emerging epidemics, significant time and effort is spent recording symptomatic cases. Scientific attention has often focused on improving statistical methodologies to estimate disease transmission parameters from these data. Here we show that, even if symptomatic cases are recorded perfectly, and disease spread parameters are estimated exactly, it is impossible to estimate the probability of a major outbreak without ambiguity. Our results therefore provide an upper bound on the accuracy of forecasts of major outbreaks that are constructed using data on symptomatic cases alone. Accurate prediction of whether or not an epidemic will occur requires records of symptomatic individuals to be supplemented with data concerning the true infection status of apparently uninfected individuals. To forecast likely future behavior in the earliest stages of an emerging outbreak, it is therefore vital to develop and deploy accurate diagnostic tests that can determine whether asymptomatic individuals are actually uninfected, or instead are infected but just do not yet show detectable symptoms. PMID:27046030

Multistate outbreak of Norwalk-like virus gastroenteritis associated with a common caterer.

PubMed

Anderson, A D; Garrett, V D; Sobel, J; Monroe, S S; Fankhauser, R L; Schwab, K J; Bresee, J S; Mead, P S; Higgins, C; Campana, J; Glass, R I

2001-12-01

In February 2000, an outbreak of gastroenteritis occurred among employees of a car dealership in New York. The same meal was also supplied to 52 dealerships nationwide, and 13 states reported illness at dealerships where the banquet was served. A retrospective cohort study was conducted to identify risk factors associated with the illness. Stool samples were collected to detect Norwalk-like virus, and sera were drawn and tested for immunoglobulin A antibodies to the outbreak strain. By univariate analysis, illness was significantly associated with consumption of any of four salads served at the banquet (relative risk = 3.8, 95% confidence interval: 2.5, 5.6). Norwalk-like virus was detected by reverse transcription-polymerase chain reaction assay in 32 of 59 stool samples from eight states. Nucleotide sequences of a 213-base pair fragment from 16 stool specimens collected from cases in eight states were identical, confirming a common source outbreak. Two of 15 workers at caterer A had elevated immunoglobulin A titers to an antigenically related Norwalk-like virus strain. This study highlights the value of molecular techniques to complement classic epidemiologic methods in outbreak investigations and underscores the critical role of food handlers in the spread of foodborne disease associated with Norwalk-like virus.

The utility and public health implications of PCR and whole genome sequencing for the detection and investigation of an outbreak of Shiga toxin-producing Escherichia coli serogroup O26:H11.

PubMed

Dallman, T J; Byrne, L; Launders, N; Glen, K; Grant, K A; Jenkins, C

2015-06-01

Many serogroups of Shiga toxin-producing Escherichia coli (STEC) other than serogroup O157 (non-O157 STEC), for example STEC O26:H11, are highly pathogenic and capable of causing haemolytic uraemic syndrome. A recent increase in non-O157 STEC cases identified in England, resulting from a change in the testing paradigm, prompted a review of the current methods available for detection and typing of non-O157 STEC for surveillance and outbreak investigations. Nineteen STEC O26:H11 strains, including four from a nursery outbreak were selected to assess typing methods. Serotyping and multilocus sequence typing were not able to discriminate between the stx-producing strains in the dataset. However, genome sequencing provided rapid and robust confirmation that isolates of STEC O26:H11 associated with a nursery outbreak were linked at the molecular level, had a common source and were distinct from the other strains analysed. Virulence gene profiling of DNA extracted from a polymerase chain reaction (PCR)-positive/culture-negative faecal specimen from a case that was epidemiologically linked to the STEC O26:H11 nursery outbreak, provided evidence at the molecular level to support that link. During this study, we describe the utility of PCR and the genome sequencing approach in facilitating surveillance and enhancing the response to outbreaks of non-O157 STEC.

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

Outbreak of scarlet fever associated with emm12 type group A Streptococcus in 2011 in Shanghai, China.

PubMed

Chen, Mingliang; Yao, Weilei; Wang, Xiaohong; Li, Yuefang; Chen, Min; Wang, Gangyi; Zhang, Xi; Pan, Hao; Hu, Jiayu; Zeng, Mei

2012-09-01

An unprecedented, large outbreak of childhood scarlet fever occurred in Shanghai between April and July 2011. Investigation of the epidemiology could enhance our understanding of the factors related to the outbreak. We retrospectively analyzed the demographic and seasonal characteristics of children with scarlet fever and the outcome. During the peak month of the 2011 outbreak, 45 GAS isolates recovered from pediatric patients and 13 (43.3%) GAS isolates recovered from 30 asymptomatic student contacts were characterized by emm typing, superantigen profiles, pulsed-field gel electrophoresis genotypes, mutilocus sequence typing and antimicrobial susceptibility. The 2011 outbreak of scarlet fever started in April and peaked in May and June. Boys outnumbered girls (65.1% versus 34.9%). Preschool and primary school children accounted for 96% of cases. No severe outcome was found. emm1, emm12 and emm75 were identified among 58 GAS isolates, and 53 (91.4%) isolates belonged to emm12, st36. Ten pulsed-field gel electrophoresis genotypes were identified among emm12 GAS isolates, 43 (81.1%) shared SPYS16.001 genotype and the remaining 7 genotypes detected were related to SPYS16.001 closely or possibly. No streptococcal pyrogenic exotoxin A and streptococcal pyrogenic exotoxin M were detected in 58 isolates. All emm12 GAS isolates were resistant to azithromycin and clindamycin. emm12 GAS strain caused the large 2011 outbreak of scarlet fever in Shanghai. Antibiotic resistance to macrolides and clindamycin in GAS is prevalent in Shanghai.

Post-Outbreak Investigation of Pseudomonas aeruginosa Faucet Contamination by Quantitative Polymerase Chain Reaction and Environmental Factors Affecting Positivity.

PubMed

Bédard, Emilie; Laferrière, Céline; Charron, Dominique; Lalancette, Cindy; Renaud, Christian; Desmarais, Nadia; Déziel, Eric; Prévost, Michèle

2015-11-01

To perform a post-outbreak prospective study of the Pseudomonas aeruginosa contamination at the faucets (water, aerator and drain) by culture and quantitative polymerase chain reaction (qPCR) and to assess environmental factors influencing occurrence A 450-bed pediatric university hospital in Montreal, Canada Water, aerator swab, and drain swab samples were collected from faucets and analyzed by culture and qPCR for the post-outbreak investigation. Water microbial and physicochemical parameters were measured, and a detailed characterization of the sink environmental and design parameters was performed. The outbreak genotyping investigation identified drains and aerators as the source of infection. The implementation of corrective measures was effective, but post-outbreak sampling using qPCR revealed 50% positivity for P. aeruginosa remaining in the water compared with 7% by culture. P. aeruginosa was recovered in the water, the aerator, and the drain in 21% of sinks. Drain alignment vs the faucet and water microbial quality were significant factors associated with water positivity, whereas P. aeruginosa load in the water was an average of 2 log higher for faucets with a positive aerator. P. aeruginosa contamination in various components of sink environments was still detected several years after the resolution of an outbreak in a pediatric university hospital. Although contamination is often not detectable in water samples by culture, P. aeruginosa is present and can recover its culturability under favorable conditions. The importance of having clear maintenance protocols for water systems, including the drainage components, is highlighted.

Multilocus variable-number tandem repeat analysis distinguishes outbreak and sporadic Escherichia coli O157:H7 isolates.

PubMed

Noller, Anna C; McEllistrem, M Catherine; Pacheco, Antonio G F; Boxrud, David J; Harrison, Lee H

2003-12-01

Escherichia coli O157:H7 is a major cause of food-borne illness in the United States. Outbreak detection involves traditional epidemiological methods and routine molecular subtyping by pulsed-field gel electrophoresis (PFGE). PFGE is labor-intensive, and the results are difficult to analyze and not easily transferable between laboratories. Multilocus variable-number tandem repeat (VNTR) analysis (MLVA) is a fast, portable method that analyzes multiple VNTR loci, which are areas of the bacterial genome that evolve quickly. Eighty isolates, including 21 isolates from five epidemiologically well-characterized outbreaks from Pennsylvania and Minnesota, were analyzed by PFGE and MLVA. Strains in PFGE clusters were defined as strains that differed by less than or equal to one band by using XbaI and the confirmatory enzyme SpeI. MLVA was performed by comparing the number of tandem repeats at seven loci. From 6 to 30 alleles were found at the seven loci, resulting in 64 MLVA types among the 80 isolates. MLVA correctly identified the isolates from all five outbreaks if only a single-locus variant was allowed. MLVA differentiated strains with unique PFGE types. Additionally, MLVA discriminated strains within PFGE-defined clusters that were not known to be part of an outbreak. In addition to being a simple and validated method for E. coli O157:H7 outbreak detection, MLVA appears to have a sensitivity equal to that of PFGE and a specificity superior to that of PFGE.

Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease.

PubMed

Taguchi, Y-h; Iwadate, Mitsuo; Umeyama, Hideaki

2015-04-30

Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems. Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug discovery was performed as translational validation of the methods. Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drug discovery.

A Waterborne Gastroenteritis Outbreak Caused by Norovirus GII.17 in a Hotel, Hebei, China, December 2014.

PubMed

Qin, Meng; Dong, Xiao-Gen; Jing, Yan-Yan; Wei, Xiu-Xia; Wang, Zhao-E; Feng, Hui-Ru; Yu, Hong; Li, Jin-Song; Li, Jie

2016-09-01

Norovirus (NoV) is responsible for an estimated 90 % of all epidemic nonbacterial outbreaks of gastroenteritis worldwide. Waterborne outbreaks of NoV are commonly reported. A novel GII.17 NoV strain emerged as a major cause of gastroenteritis outbreaks in China during the winter of 2014/2015. During this time, an outbreak of gastroenteritis occurred at a hotel in a ski park in Hebei Province, China. Epidemiological investigations indicated that one water well, which had only recently been in use, was the probable source. GII.17 NoV was detected by real-time reverse-transcription polymerase chain reaction from samples taken from cases, from concentrated water samples from water well, and from the nearby sewage settling tank. Nucleotide sequences of NoV extracted from clinical and water specimens were genetically identical and had 99 % homology with Beijing/CHN/2015. All epidemiological data indicated that GII.17 NoV was responsible for this outbreak. This is the first reported laboratory-confirmed waterborne outbreak caused by GII.17 NoV genotype in China. Strengthening management of well drinking water and systematica monitoring of NoV is essential for preventing future outbreaks.

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.

Rapid MALDI-TOF mass spectrometry strain typing during a large outbreak of Shiga-Toxigenic Escherichia coli.

PubMed

Christner, Martin; Trusch, Maria; Rohde, Holger; Kwiatkowski, Marcel; Schlüter, Hartmut; Wolters, Manuel; Aepfelbacher, Martin; Hentschke, Moritz

2014-01-01

In 2011 northern Germany experienced a large outbreak of Shiga-Toxigenic Escherichia coli O104:H4. The large amount of samples sent to microbiology laboratories for epidemiological assessment highlighted the importance of fast and inexpensive typing procedures. We have therefore evaluated the applicability of a MALDI-TOF mass spectrometry based strategy for outbreak strain identification. Specific peaks in the outbreak strain's spectrum were identified by comparative analysis of archived pre-outbreak spectra that had been acquired for routine species-level identification. Proteins underlying these discriminatory peaks were identified by liquid chromatography tandem mass spectrometry and validated against publicly available databases. The resulting typing scheme was evaluated against PCR genotyping with 294 E. coli isolates from clinical samples collected during the outbreak. Comparative spectrum analysis revealed two characteristic peaks at m/z 6711 and m/z 10883. The underlying proteins were found to be of low prevalence among genome sequenced E. coli strains. Marker peak detection correctly classified 292 of 293 study isolates, including all 104 outbreak isolates. MALDI-TOF mass spectrometry allowed for reliable outbreak strain identification during a large outbreak of Shiga-Toxigenic E. coli. The applied typing strategy could probably be adapted to other typing tasks and might facilitate epidemiological surveys as part of the routine pathogen identification workflow.

Outbreaks of human metapneumovirus in two skilled nursing facilities -West Virginia and Idaho, 2011-2012.

PubMed

2013-11-22

During January and February 2012, state and local public health agencies in West Virginia and Idaho, with assistance from facility staff members and CDC, investigated outbreaks of unexplained respiratory illness characterized by high proportions of lower respiratory tract infections (LRTIs) at two skilled nursing facilities (SNFs). Investigations were conducted to determine the extent and etiology of each outbreak and make recommendations to prevent further spread. During both outbreaks, influenza was initially suspected; however, human metapneumovirus (hMPV) was identified as the etiologic agent. Among 57 cases of respiratory illness from both facilities, 45 (79%) patients had evidence of LRTI, of whom 25 (56%) had radiologically confirmed pneumonia; five (9%) had evidence of upper respiratory tract infection (URTI), and seven (12%) could not be classified. Six patients (11%) died. These outbreaks demonstrate that hMPV, a recently described pathogen that would not have been detected without the use of molecular diagnostics in these outbreaks, is associated with severe LRTI and should be considered as a possible etiology of respiratory outbreaks in SNFs.

Characterization of Staphylococcus aureus strains and evidence for the involvement of non-classical enterotoxin genes in food poisoning outbreaks.

PubMed

Ciupescu, Laurentiu-Mihai; Auvray, Frederic; Nicorescu, Isabela Madalina; Meheut, Thomas; Ciupescu, Veronica; Lardeux, Anne-Laure; Tanasuica, Rodica; Hennekinne, Jacques-Antoine

2018-06-05

To an increasing extent, molecular and genetic characterization is now used to investigate foodborne outbreaks. The aim of this study was to seek molecular links among coagulase-positive staphylococci (CPS) isolated from three recent food poisoning outbreaks in Romania using polymerase chain reaction and pulsed-field gel electrophoresis (PFGE) techniques. Nineteen CPS isolates were identified as Staphylococcus aureus by detection of the 23S rDNA gene. Among them, 15 carried at least one staphylococcal enterotoxin-encoding gene (se). The Calarași outbreak strains grouped in pulsotype 2 and were sed/sej/ser-positive, whereas the Arad outbreak strains clustered in pulsotype 17 and were either sed/seg/sei/sej/ser- or seg/sei-positive. The Pitești outbreak strains clustered in pulsotype 1 and, surprisingly, possessed only one enterotoxin gene, i.e. seh. Similar to other European countries, the seh gene has been identified with increasing frequency in Romanian outbreaks; this highlights the importance of considering the application of methods recommended for staphylococcal enterotoxin regulation in Europe.

Polio and Measles Down the Drain: Environmental Enterovirus Surveillance in the Netherlands, 2005 to 2015.

PubMed

Benschop, Kimberley S M; van der Avoort, Harrie G; Jusic, Edin; Vennema, Harry; van Binnendijk, Rob; Duizer, Erwin

2017-07-01

Polioviruses (PVs) are members of the genus Enterovirus In the Netherlands, the exclusion of PV circulation is based on clinical enterovirus (EV) surveillance (CEVS) of EV-positive cases and routine environmental EV surveillance (EEVS) conducted on sewage samples collected in the region of the Netherlands where vaccination coverage is low due to religious reasons. We compared the EEVS data to those of the CEVS to gain insight into the relevance of EEVS for poliovirus and nonpolio enterovirus surveillance. Following the polio outbreak in Syria, EEVS was performed at the primary refugee center in Ter Apel in the Netherlands, and data were compared to those of CEVS and EEVS. Furthermore, we assessed the feasibility of poliovirus detection by EEVS using measles virus detection in sewage during a measles outbreak as a proxy. Two Sabin-like PVs were found in routine EEVS, 11 Sabin-like PVs were detected in the CEVS, and one Sabin-like PV was found in the Ter Apel sewage. We observed significant differences between the three programs regarding which EVs were found. In 6 sewage samples collected during the measles outbreak in 2013, measles virus RNA was detected in regions where measles cases were identified. In conclusion, we detected PVs, nonpolio EVs, and measles virus in sewage and showed that environmental surveillance is useful for poliovirus detection in the Netherlands, where live oral poliovirus vaccine is not used and communities with lower vaccination coverage exist. EEVS led to the detection of EV types not seen in the CEVS, showing that EEVS is complementary to CEVS. IMPORTANCE We show that environmental enterovirus surveillance complements clinical enterovirus surveillance for poliovirus detection, or exclusion, and for nonpolio enterovirus surveillance. Even in the presence of adequate surveillance, only a very limited number of Sabin-like poliovirus strains were detected in a 10-year period, and no signs of transmission of oral polio vaccine (OPV) strains were found in a country using exclusively inactivated polio vaccine (IPV). Measles viruses can be detected during an outbreak in sewage samples collected and concentrated following procedures used for environmental enterovirus surveillance. Copyright © 2017 American Society for Microbiology.

Polio and Measles Down the Drain: Environmental Enterovirus Surveillance in the Netherlands, 2005 to 2015

PubMed Central

Benschop, Kimberley S. M.; van der Avoort, Harrie G.; Jusic, Edin; Vennema, Harry; van Binnendijk, Rob

2017-01-01

ABSTRACT Polioviruses (PVs) are members of the genus Enterovirus. In the Netherlands, the exclusion of PV circulation is based on clinical enterovirus (EV) surveillance (CEVS) of EV-positive cases and routine environmental EV surveillance (EEVS) conducted on sewage samples collected in the region of the Netherlands where vaccination coverage is low due to religious reasons. We compared the EEVS data to those of the CEVS to gain insight into the relevance of EEVS for poliovirus and nonpolio enterovirus surveillance. Following the polio outbreak in Syria, EEVS was performed at the primary refugee center in Ter Apel in the Netherlands, and data were compared to those of CEVS and EEVS. Furthermore, we assessed the feasibility of poliovirus detection by EEVS using measles virus detection in sewage during a measles outbreak as a proxy. Two Sabin-like PVs were found in routine EEVS, 11 Sabin-like PVs were detected in the CEVS, and one Sabin-like PV was found in the Ter Apel sewage. We observed significant differences between the three programs regarding which EVs were found. In 6 sewage samples collected during the measles outbreak in 2013, measles virus RNA was detected in regions where measles cases were identified. In conclusion, we detected PVs, nonpolio EVs, and measles virus in sewage and showed that environmental surveillance is useful for poliovirus detection in the Netherlands, where live oral poliovirus vaccine is not used and communities with lower vaccination coverage exist. EEVS led to the detection of EV types not seen in the CEVS, showing that EEVS is complementary to CEVS. IMPORTANCE We show that environmental enterovirus surveillance complements clinical enterovirus surveillance for poliovirus detection, or exclusion, and for nonpolio enterovirus surveillance. Even in the presence of adequate surveillance, only a very limited number of Sabin-like poliovirus strains were detected in a 10-year period, and no signs of transmission of oral polio vaccine (OPV) strains were found in a country using exclusively inactivated polio vaccine (IPV). Measles viruses can be detected during an outbreak in sewage samples collected and concentrated following procedures used for environmental enterovirus surveillance. PMID:28432101

Outbreak of human metapneumovirus infection in psychiatric inpatients: implications for directly observed use of alcohol hand rub in prevention of nosocomial outbreaks.

PubMed

Cheng, V C C; Wu, A K L; Cheung, C H Y; Lau, S K P; Woo, P C Y; Chan, K H; Li, K S M; Ip, I K S; Dunn, E L W; Lee, R A; Yam, L Y C; Yuen, K Y

2007-12-01

Nosocomial outbreaks of infectious diseases in psychiatric facilities are not uncommon but the implementation of infection control measures is often difficult. Here, we report an outbreak of an acute respiratory illness in a psychiatric ward between 29 July and 20 August 2005 involving 31 patients. Human metapneumovirus was detected in seven (23%) patients by reverse transcription-polymerase chain reaction and nucleotide sequencing. A review of outbreak surveillance records showed that six nosocomial outbreaks occurred in the year 2005, of which four (67%) were confirmed or presumably related to a respiratory viral infection. Directly observed deliveries of alcohol hand rub 4-hourly during daytime to all psychiatric patients was instituted in December 2005. Only one nosocomial respiratory viral outbreak occurred in the following year. The total number of patients and staff involved in nosocomial outbreaks due to presumed or proven respiratory virus infections decreased significantly from 60 to six (P<0.001), whereas those due to all types of nosocomial outbreaks also decreased from 70 to 24 (P=0.004). Alcohol hand rub has been shown to have potent bactericidal and virucidal activity against a wide range of nosocomial pathogens. Regular use of directly observed alcohol hand rub may decrease the incidence and scale of nosocomial outbreaks due to enveloped respiratory viruses especially in mentally incapacitated patients.

Designing and implementing an electronic dashboard for disease outbreaks response - Case study of the 2013-2014 Somalia Polio outbreak response dashboard

PubMed Central

Kamadjeu, Raoul; Gathenji, Caroline

2017-01-01

In April 2013, a case of wild polio virus (WPV) was detected in the Somalia capital Mogadishu. This inaugurated what is now referred to as the 2013-2014 Horn of Africa Polio outbreak with cases reported in Somalia, Kenya and Ethiopia. By the notification of the last polio case in August 2014, 223 cases of WPV had been reported in Somalia, Kenya and Ethiopia of which 199 in Somalia alone. The outbreak response required timely exchange of information between the outbreak response coordination unit (in Nairobi) and local staff located in multiple locations inside the country. The need to track and timely respond to information requests, to satisfy the information/data needs of polio partners and to track key outbreak response performance indicators dictated the need to urgently set up an online dashboard. The Somalia Polio Room dashboard provided a graphical display of the polio outbreak data to track progress and inform decision making. The system was designed using free and open sources components and seamlessly integrated existing polio surveillance data for real time monitoring of key outbreak response performance indicators. In this article, we describe the design and operation of an electronic dashboard for disease surveillance in an outbreak situation and used the lessons learned to propose key design considerations and functional requirements for online electronic dashboards for disease outbreak response. PMID:29296157

Designing and implementing an electronic dashboard for disease outbreaks response - Case study of the 2013-2014 Somalia Polio outbreak response dashboard.

PubMed

Kamadjeu, Raoul; Gathenji, Caroline

2017-01-01

In April 2013, a case of wild polio virus (WPV) was detected in the Somalia capital Mogadishu. This inaugurated what is now referred to as the 2013-2014 Horn of Africa Polio outbreak with cases reported in Somalia, Kenya and Ethiopia. By the notification of the last polio case in August 2014, 223 cases of WPV had been reported in Somalia, Kenya and Ethiopia of which 199 in Somalia alone. The outbreak response required timely exchange of information between the outbreak response coordination unit (in Nairobi) and local staff located in multiple locations inside the country. The need to track and timely respond to information requests, to satisfy the information/data needs of polio partners and to track key outbreak response performance indicators dictated the need to urgently set up an online dashboard. The Somalia Polio Room dashboard provided a graphical display of the polio outbreak data to track progress and inform decision making. The system was designed using free and open sources components and seamlessly integrated existing polio surveillance data for real time monitoring of key outbreak response performance indicators. In this article, we describe the design and operation of an electronic dashboard for disease surveillance in an outbreak situation and used the lessons learned to propose key design considerations and functional requirements for online electronic dashboards for disease outbreak response.

Dead or alive: animal sampling during Ebola hemorrhagic fever outbreaks in humans

PubMed Central

Olson, Sarah H.; Reed, Patricia; Cameron, Kenneth N.; Ssebide, Benard J.; Johnson, Christine K.; Morse, Stephen S.; Karesh, William B.; Mazet, Jonna A. K.; Joly, Damien O.

2012-01-01

There are currently no widely accepted animal surveillance guidelines for human Ebola hemorrhagic fever (EHF) outbreak investigations to identify potential sources of Ebolavirus (EBOV) spillover into humans and other animals. Animal field surveillance during and following an outbreak has several purposes, from helping identify the specific animal source of a human case to guiding control activities by describing the spatial and temporal distribution of wild circulating EBOV, informing public health efforts, and contributing to broader EHF research questions. Since 1976, researchers have sampled over 10,000 individual vertebrates from areas associated with human EHF outbreaks and tested for EBOV or antibodies. Using field surveillance data associated with EHF outbreaks, this review provides guidance on animal sampling for resource-limited outbreak situations, target species, and in some cases which diagnostics should be prioritized to rapidly assess the presence of EBOV in animal reservoirs. In brief, EBOV detection was 32.7% (18/55) for carcasses (animals found dead) and 0.2% (13/5309) for live captured animals. Our review indicates that for the purposes of identifying potential sources of transmission from animals to humans and isolating suspected virus in an animal in outbreak situations, (1) surveillance of free-ranging non-human primate mortality and morbidity should be a priority, (2) any wildlife morbidity or mortality events should be investigated and may hold the most promise for locating virus or viral genome sequences, (3) surveillance of some bat species is worthwhile to isolate and detect evidence of exposure, and (4) morbidity, mortality, and serology studies of domestic animals should prioritize dogs and pigs and include testing for virus and previous exposure. PMID:22558004

The important role of early diagnosis and preventive management during a large-scale outbreak of hepatitis A in Thailand

PubMed Central

Poovorawan, Kittiyod; Chattakul, Paiboon; Chattakul, Sirirat; Thongmee, Thanunrat; Theamboonlers, Apiradee; Komolmit, Piyawat; Poovorawan, Yong

2013-01-01

Introduction Acute hepatitis A is a worldwide public health problem especially in developing countries. Recently, a large, community-wide outbreak of hepatitis A occurred in the northeast part of Thailand. Methods Demographic and clinical data as well as blood samples were collected and analyzed from patients with acute hepatitis who attended the Buengkan Provincial Hospital from June to September 2012. About 1619 patients with clinical symptoms of hepatitis A visited the hospital during the outbreak which manifested in three waves. Blood samples were collected from 205 patients. Results One hundred and seventy eight patients had hepatitis A confirmed by the presence of anti-hepatitis A virus (HAV) IgM and/or HAV-RNA. The sensitivities for anti-HAV IgM and HAV-RNA were 95.5% (170/178) and 61.8% (110/178), respectively. When HAV-RNA was combined with anti-HAV IgM test, this increased the diagnostic yield by 7.2% (8/111) in the early phase of the acute infection (less than 5 days). Investigation of the molecular structure of the detected viruses indicated that all of the infections were caused by HAV genotype IA. There were no fatalities from this outbreak. Rapid detection, health education, sanitation campaigns, and vaccination offered on a voluntary basis have steadily reduced the number of infected patients and stopped the outbreak. Conclusion Occasionally a large-scale outbreak of HAV genotype IA can occur. A combination of HAV-RNA and anti-HAV IgM tests can increase the diagnostic yield during the early phase of the acute infection. Early diagnosis and preventive management campaigns can slow down and stop the outbreak. PMID:24392680

Partial Failure of Milk Pasteurization as a Risk for the Transmission of Campylobacter From Cattle to Humans.

PubMed

Fernandes, Anand M; Balasegaram, Sooria; Willis, Caroline; Wimalarathna, Helen M L; Maiden, Martin C; McCarthy, Noel D

2015-09-15

Cattle are the second most common source of human campylobacteriosis. However, routes to account for this scale of transmission have not been identified. In contrast to chicken, red meat is not heavily contaminated at point of sale. Although effective pasteurization prevents milk-borne infection, apparently sporadic infections may include undetected outbreaks from raw or perhaps incompletely pasteurized milk. A rise in Campylobacter gastroenteritis in an isolated population was investigated using whole-genome sequencing (WGS), an epidemiological study, and environmental investigations. A single strain was identified in 20 cases, clearly distinguishable from other local strains and a reference population by WGS. A case-case analysis showed association of infection with the outbreak strain and milk from a single dairy (odds ratio, 8; Fisher exact test P value = .023). Despite temperature records indicating effective pasteurization, mechanical faults likely to lead to incomplete pasteurization of part of the milk were identified by further testing and examination of internal components of dairy equipment. Here, milk distribution concentrated on a small area, including school-aged children with low background incidence of campylobacteriosis, facilitated outbreak identification. Low-level contamination of widely distributed milk would not produce as detectable an outbreak signal. Such hidden outbreaks may contribute to the substantial burden of apparently sporadic Campylobacter from cattle where transmission routes are not certain. The effective discrimination of outbreak isolates from a reference population using WGS shows that integrating these data and approaches into surveillance could support the detection as well as investigation of such outbreaks. © The Author 2015. Published by Oxford University Press on behalf of the Infectious Diseases Society of America.

Health of whitebark pine forests after mountain pine beetle outbreaks

Treesearch

Sandra Kegley; John Schwandt; Ken Gibson; Dana Perkins

2011-01-01

Whitebark pine (Pinus albicaulis), a keystone high-elevation species, is currently at risk due to a combination of white pine blister rust (WPBR) (Cronartium ribicola), forest succession, and outbreaks of mountain pine beetle (MPB) (Dendroctonus ponderosae). While recent mortality is often quantified by aerial detection surveys (ADS) or ground surveys, little...

Molecular typing of Salmonella enterica serovar typhi.

PubMed Central

Navarro, F; Llovet, T; Echeita, M A; Coll, P; Aladueña, A; Usera, M A; Prats, G

1996-01-01

The efficiencies of different tests for epidemiological markers--phage typing, ribotyping, IS200 typing, and pulsed-field gel electrophoresis (PFGE)--were evaluated for strains from sporadic cases of typhoid fever and a well-defined outbreak. Ribotyping and PFGE proved to be the most discriminating. Both detected two different patterns among outbreak-associated strains. PMID:8897193

Spectral evidence of early-stage spruce beetle infestation in Engelmann spruce

Treesearch

Adrianna C. Foster; Jonathan A. Walter; Herman H. Shugart; Jason Sibold; Jose Negron

2017-01-01

Spruce beetle (Dendroctonus rufipennis (Kirby)) outbreaks cause widespread mortality of Engelmann spruce (Picea engelmannii (Parry ex Engelm)) within the subalpine forests of the western United States. Early detection of infestations could allow forest managers to mitigate outbreaks or anticipate a response to tree mortality and the potential effects on ecosystem...

Landscape-scale genetic variation in a forest outbreak species, the mountain pine beetle (Dendroctonus ponderosae)

Treesearch

K. E. Mock; B. J. Bentz; E. M. O' Neill; J. P. Chong; J. Orwin; M. E. Pfrender

2007-01-01

The mountain pine beetle Dendroctonus ponderosae is a native species currently experiencing large-scale outbreaks in western North American pine forests. We sought to describe the pattern of genetic variation across the range of this species, to determine whether there were detectable genetic differences between D. ponderosae...

Outbreak of sorghum/sugarcane aphid on sorghum: First detections, distribution, and notes on management

USDA-ARS?s Scientific Manuscript database

An outbreak of an invasive aphid was discovered damaging grain sorghum in Texas and neighboring states in 2013. It may be a new variant of sugarcane aphid, Melanaphis sacchari, that has a high preference for sorghum, or a very closely related species (M. sorghi). We designate it sorghum/sugarcane ...

Evaluation of Strategies to Control a Potential Outbreak of Foot-and-Mouth Disease in Sweden.

PubMed

Dórea, Fernanda C; Nöremark, Maria; Widgren, Stefan; Frössling, Jenny; Boklund, Anette; Halasa, Tariq; Ståhl, Karl

2017-01-01

To minimize the potential consequences of an introduction of foot-and-mouth disease (FMD) in Europe, European Union (EU) member states are required to present a contingency plan. This study used a simulation model to study potential outbreak scenarios in Sweden and evaluate the best control strategies. The model was informed by the Swedish livestock structure using herd information from cattle, pig, and small ruminant holdings in the country. The contact structure was based on animal movement data and studies investigating the movements between farms of veterinarians, service trucks, and other farm visitors. All scenarios of outbreak control included depopulation of detected herds, 3 km protection and 10 km surveillance zones, movement tracing, and 3 days national standstill. The effect of availability of surveillance resources, i.e., number of field veterinarians per day, and timeliness of enforcement of interventions, was assessed. With the estimated currently available resources, an FMD outbreak in Sweden is expected to be controlled (i.e., last infected herd detected) within 3 weeks of detection in any evaluated scenario. The density of farms in the area where the epidemic started would have little impact on the time to control the outbreak, but spread in high density areas would require more surveillance resources, compared to areas of lower farm density. The use of vaccination did not result in a reduction in the expected number of infected herds. Preemptive depopulation was able to reduce the number of infected herds in extreme scenarios designed to test a combination of worst-case conditions of virus introduction and spread, but at the cost of doubling the number of herds culled. This likely resulted from a combination of the small outbreaks predicted by the spread model, and the high efficacy of the basic control measures evaluated, under the conditions of the Swedish livestock industry, and considering the assumed control resources available. The results indicate that the duration and extent of FMD outbreaks could be kept limited in Sweden using the EU standard control strategy and a 3 days national standstill.

Evaluation of Strategies to Control a Potential Outbreak of Foot-and-Mouth Disease in Sweden

PubMed Central

Dórea, Fernanda C.; Nöremark, Maria; Widgren, Stefan; Frössling, Jenny; Boklund, Anette; Halasa, Tariq; Ståhl, Karl

2017-01-01

To minimize the potential consequences of an introduction of foot-and-mouth disease (FMD) in Europe, European Union (EU) member states are required to present a contingency plan. This study used a simulation model to study potential outbreak scenarios in Sweden and evaluate the best control strategies. The model was informed by the Swedish livestock structure using herd information from cattle, pig, and small ruminant holdings in the country. The contact structure was based on animal movement data and studies investigating the movements between farms of veterinarians, service trucks, and other farm visitors. All scenarios of outbreak control included depopulation of detected herds, 3 km protection and 10 km surveillance zones, movement tracing, and 3 days national standstill. The effect of availability of surveillance resources, i.e., number of field veterinarians per day, and timeliness of enforcement of interventions, was assessed. With the estimated currently available resources, an FMD outbreak in Sweden is expected to be controlled (i.e., last infected herd detected) within 3 weeks of detection in any evaluated scenario. The density of farms in the area where the epidemic started would have little impact on the time to control the outbreak, but spread in high density areas would require more surveillance resources, compared to areas of lower farm density. The use of vaccination did not result in a reduction in the expected number of infected herds. Preemptive depopulation was able to reduce the number of infected herds in extreme scenarios designed to test a combination of worst-case conditions of virus introduction and spread, but at the cost of doubling the number of herds culled. This likely resulted from a combination of the small outbreaks predicted by the spread model, and the high efficacy of the basic control measures evaluated, under the conditions of the Swedish livestock industry, and considering the assumed control resources available. The results indicate that the duration and extent of FMD outbreaks could be kept limited in Sweden using the EU standard control strategy and a 3 days national standstill. PMID:28791298

Molecular identification of the first local dengue fever outbreak in Shenzhen city, China: a potential imported vertical transmission from Southeast Asia?

PubMed

Yang, F; Guo, G Z; Chen, J Q; Ma, H W; Liu, T; Huang, D N; Yao, C H; Zhang, R L; Xue, C F; Zhang, L

2014-02-01

A suspected dengue fever outbreak occurred in 2010 at a solitary construction site in Shenzhen city, China. To investigate this epidemic, we used serological, molecular biological, and bioinformatics techniques. Of nine serum samples from suspected patients, we detected seven positive for dengue virus (DENV) antibodies, eight for DENV-1 RNA, and three containing live viruses. The isolated virus, SZ1029 strain, was sequenced and confirmed as DENV-1, showing the highest E-gene homology to D1/Malaysia/36000/05 and SG(EHI)DED142808 strains recently reported in Southeast Asia. Further phylogenetic tree analysis confirmed their close relationship. At the epidemic site, we also detected 14 asymptomatic co-workers (out of 291) positive for DENV antibody, and DENV-1-positive mosquitoes. Thus, we concluded that DENV-1 caused the first local dengue fever outbreak in Shenzhen. Because no imported case was identified, the molecular fingerprints of the SZ1029 strain suggest this outbreak may be due to vertical transmission imported from Southeast Asia.

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

Nosocomial outbreak of Legionella pneumophila serogroup 3 pneumonia in a new bone marrow transplant unit: evaluation, treatment and control.

PubMed

Oren, I; Zuckerman, T; Avivi, I; Finkelstein, R; Yigla, M; Rowe, J M

2002-08-01

A nosocomial outbreak of pneumonia caused by Legionella pneumophila serogroup 3 occurred in four patients following hematopoietic stem cell transplantation (HSCT) in a new bone marrow transplantation (BMT) unit during a 2 week period. The causative organism was recovered from the water supply system to the same unit just before the outbreak. Nineteen other BMT patients were hospitalized in the same unit at the same time, giving a frequency of proven infection of 4/23 = 17%. Immediately after recognition of the outbreak, use of tap water was forbidden, humidifiers were disconnected, and ciprofloxacin prophylaxis was started for all patients in the unit, until decontamination of the water was achieved. No other cases were detected. In conclusion, contamination of the hospital water supply system with legionella carries a high risk for legionella pneumonia among BMT patients. Early recognition of the outbreak, immediate restrictions of water use, antibiotic prophylaxis for all non-infected patients, and water decontamination, successfully terminated the outbreak.

Epidemiological and molecular analysis of a waterborne outbreak of norovirus GII.4.

PubMed

Zhou, X; Li, H; Sun, L; Mo, Y; Chen, S; Wu, X; Liang, J; Zheng, H; Ke, C; Varma, J K; Klena, J D; Chen, Q; Zou, L; Yang, X

2012-12-01

Contaminated water is one of the main sources of norovirus (NoV) gastroenteritis outbreaks globally. Waterborne NoV outbreaks are infrequently attributed to GII.4 NoV. In September 2009, a NoV outbreak affected a small school in Guangdong Province, China. Epidemiological investigations indicated that household use water, supplied by a well, was the probable source (relative risk 1·9). NoV nucleic acid material in concentrated well-water samples was detected using real-time RT-PCR. Nucleotide sequences of NoV extracted from diarrhoea and well-water specimens were identical and had the greatest sequence identity to corresponding sequences from the epidemic strain GII.4-2006b. Our report documents the first laboratory-confirmed waterborne outbreak caused by GII.4 NoV genotype in China. Our investigations indicate that well water, intended exclusively for household use but not for consumption, caused this outbreak. The results of this report serve as a reminder that private well water intended for household use should be tested for NoV.

Detection of Multiple Parallel Transmission Outbreak of Streptococcus suis Human Infection by Use of Genome Epidemiology, China, 2005

PubMed Central

Du, Pengcheng; Zheng, Han; Zhou, Jieping; Lan, Ruiting; Ye, Changyun; Jing, Huaiqi; Jin, Dong; Cui, Zhigang; Bai, Xuemei; Liang, Jianming; Liu, Jiantao; Xu, Lei; Zhang, Wen; Chen, Chen

2017-01-01

Streptococcus suis sequence type 7 emerged and caused 2 of the largest human infection outbreaks in China in 1998 and 2005. To determine the major risk factors and source of the infections, we analyzed whole genomes of 95 outbreak-associated isolates, identified 160 single nucleotide polymorphisms, and classified them into 6 clades. Molecular clock analysis revealed that clade 1 (responsible for the 1998 outbreak) emerged in October 1997. Clades 2–6 (responsible for the 2005 outbreak) emerged separately during February 2002–August 2004. A total of 41 lineages of S. suis emerged by the end of 2004 and rapidly expanded to 68 genome types through single base mutations when the outbreak occurred in June 2005. We identified 32 identical isolates and classified them into 8 groups, which were distributed in a large geographic area with no transmission link. These findings suggest that persons were infected in parallel in respective geographic sites. PMID:27997331

[Outbreaks of viral hepatitis E in the Czech Republic?].

PubMed

Trmal, Josef; Pavlík, Ivo; Vasícková, Petra; Matejícková, Ladislava; Simůnková, Lenka; Luks, Stanislav; Pazderková, Jana

2012-05-01

Until recently, viral hepatitis E (VHE) has typically been an imported infection, related to travel to developing countries. A number of travel-unrelated VHE cases currently diagnosed in the Czech Republic. Outcomes of the epidemiological investigations of two VHE outbreaks associated with the consumption of pork and pork products at pig-slaughtering feasts are presented. Thirteen cases have been reported in the first outbreak and eight cases in the second outbreak. The epidemiological investigations are described and the experience gained in analysing suspected biological specimens is presented. The source of infection has not been identified in the first outbreak while in the other one, a link between human cases and infection in farm pigs was revealed for the first time. Although the epidemiological investigation may not always lead to the detection of the VHE source, it must be conducted in any outbreak and can only be successful when done in cooperation of the public health authorities with the veterinary health agency.

Serratia marcescens outbreak due to contaminated 2% aqueous chlorhexidine.

PubMed

de Frutos, Mónica; López-Urrutia, Luis; Domínguez-Gil, Marta; Arias, Marta; Muñoz-Bellido, Juan Luis; Eiros, José María; Ramos, Carmen

2017-12-01

An outbreak of Serratia marcescens infections outbreak is described, as well as the epidemiological study that linked the outbreak to the use of 2% aqueous chlorhexidine antiseptic. In late November 2014 an increasing incidence of S. marcescens isolates was detected in patients treated in the emergency department. It was considered a possible outbreak, and an epidemiological investigation was started. S. marcescens was isolated in 23 samples from 16 patients and in all new bottles of two lots of 2% aqueous chlorhexidine. The contaminated disinfectant was withdrawn, and the Spanish Drugs Agency was alerted (COS 2/2014). The epidemiological study showed that strains isolated from clinical samples and from chlorhexidine belonged to the same clone. No further isolates were obtained once the disinfectant was withdrawn. The suspicion of an outbreak and the epidemiological study were essential to control the incidence. Copyright © 2016 Elsevier España, S.L.U. and Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

Investigation of Outbreaks of Salmonella enterica Serovar Typhimurium and Its Monophasic Variants Using Whole-Genome Sequencing, Denmark

PubMed Central

Gymoese, Pernille; Sørensen, Gitte; Litrup, Eva; Olsen, John Elmerdal; Nielsen, Eva Møller

2017-01-01

Whole-genome sequencing is rapidly replacing current molecular typing methods for surveillance purposes. Our study evaluates core-genome single-nucleotide polymorphism analysis for outbreak detection and linking of sources of Salmonella enterica serovar Typhimurium and its monophasic variants during a 7-month surveillance period in Denmark. We reanalyzed and defined 8 previously characterized outbreaks from the phylogenetic relatedness of the isolates, epidemiologic data, and food traceback investigations. All outbreaks were identified, and we were able to exclude unrelated and include additional related human cases. We were furthermore able to link possible food and veterinary sources to the outbreaks. Isolates clustered according to sequence types (STs) 19, 34, and 36. Our study shows that core-genome single-nucleotide polymorphism analysis is suitable for surveillance and outbreak investigation for Salmonella Typhimurium (ST19 and ST36), but whole genome–wide analysis may be required for the tight genetic clone of monophasic variants (ST34). PMID:28930002

Epidemiological situation of measles in Romania, Italy, and Hungary: On what threats should we focus nowadays?

PubMed

Orosz, László; Gáspár, Gábor; Rózsa, Ágnes; Rákos, Nóra; Sziveri, Szilárd; Bosnyákovits, Tünde

2018-02-28

Although the prevalence of wild-type measles virus infection has decreased by >90% in Europe, the disease is still not eliminated and has even reemerged with recurrent outbreaks in different countries, including Romania and Italy. Minor outbreaks of Romanian origin were reported from Hungary as well. In Romania, an outbreak has been ongoing since February 2016. As of October 2017, 9,670 measles cases and 35 deaths were registered in the country. The three most affected counties are located next to the Hungarian border. In Italy, until the end of August 2017, 4,477 cases were reported to the surveillance system. The outbreak affected most of the Italian administrative regions. Until October 2017, three minor measles outbreaks were also detected in Hungary. All of these outbreaks were derived from Romanian cases. Although in these countries, there are vaccination programs running, the spread of the disease raises the possibility of secondary vaccine failure.

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.

Governance and One Health: Exploring the Impact of Federalism and Bureaucracy on Zoonotic Disease Detection and Reporting

PubMed Central

Allen, Heather A.

2015-01-01

The merits of One Health have been thoroughly described in the literature, but how One Health operates in the United States federal system of government is rarely discussed or analyzed. Through a comparative case-study approach, this research explores how federalism, bureaucratic behavior, and institutional design in the United States may influence zoonotic disease outbreak detection and reporting, a key One Health activity. Using theoretical and empirical literature, as well as a survey/interview instrument for individuals directly involved in a past zoonotic disease outbreak, the impacts of governance are discussed. As predicted in the theoretical literature, empirical findings suggest that federalism, institutional design, and bureaucracy may play a role in facilitating or impeding zoonotic disease outbreak detection and reporting. Regulatory differences across states as well as compartmentalization of information within agencies may impede disease detection. However, the impact may not always be negative: bureaucracies can also be adaptive; federalism allows states important opportunities for innovation. While acknowledging there are many other factors that also matter in zoonotic disease detection and reporting, this research is one of the first attempts to raise awareness in the literature and stimulate discussion on the intersection of governance and One Health. PMID:29061932

Characteristics of a dengue outbreak in a remote pacific island chain--Republic of The Marshall Islands, 2011-2012.

PubMed

Sharp, Tyler M; Mackay, Andrew J; Santiago, Gilberto A; Hunsperger, Elizabeth; Nilles, Eric J; Perez-Padilla, Janice; Tikomaidraubuta, Kinisalote S; Colon, Candimar; Amador, Manuel; Chen, Tai-Ho; Lalita, Paul; Muñoz-Jordán, Jorge L; Barrera, Roberto; Langidrik, Justina; Tomashek, Kay M

2014-01-01

Dengue is a potentially fatal acute febrile illness caused by four mosquito-transmitted dengue viruses (DENV-1-4). Although dengue outbreaks regularly occur in many regions of the Pacific, little is known about dengue in the Republic of the Marshall Islands (RMI). To better understand dengue in RMI, we investigated an explosive outbreak that began in October 2011. Suspected cases were reported to the Ministry of Health, serum specimens were tested with a dengue rapid diagnostic test (RDT), and confirmatory testing was performed using RT-PCR and IgM ELISA. Laboratory-positive cases were defined by detection of DENV nonstructural protein 1 by RDT, DENV nucleic acid by RT-PCR, or anti-DENV IgM antibody by RDT or ELISA. Secondary infection was defined by detection of anti-DENV IgG antibody by ELISA in a laboratory-positive acute specimen. During the four months of the outbreak, 1,603 suspected dengue cases (3% of the RMI population) were reported. Of 867 (54%) laboratory-positive cases, 209 (24%) had dengue with warning signs, six (0.7%) had severe dengue, and none died. Dengue incidence was highest in residents of Majuro and individuals aged 10-29 years, and ∼95% of dengue cases were experiencing secondary infection. Only DENV-4 was detected by RT-PCR, which phylogenetic analysis demonstrated was most closely related to a virus previously identified in Southeast Asia. Cases of vertical DENV transmission, and DENV/Salmonella Typhi and DENV/Mycobacterium leprae co-infection were identified. Entomological surveys implicated water storage containers and discarded tires as the most important development sites for Aedes aegypti and Ae. albopictus, respectively. Although this is the first documented dengue outbreak in RMI, the age groups of cases and high prevalence of secondary infection demonstrate prior DENV circulation. Dengue surveillance should continue to be strengthened in RMI and throughout the Pacific to identify and rapidly respond to future outbreaks.

Characteristics of a Dengue Outbreak in a Remote Pacific Island Chain – Republic of the Marshall Islands, 2011–2012

PubMed Central

Sharp, Tyler M.; Mackay, Andrew J.; Santiago, Gilberto A.; Hunsperger, Elizabeth; Nilles, Eric J.; Perez-Padilla, Janice; Tikomaidraubuta, Kinisalote S.; Colon, Candimar; Amador, Manuel; Chen, Tai-Ho; Lalita, Paul; Muñoz-Jordán, Jorge L.; Barrera, Roberto; Langidrik, Justina; Tomashek, Kay M.

2014-01-01

Dengue is a potentially fatal acute febrile illness caused by four mosquito-transmitted dengue viruses (DENV-1–4). Although dengue outbreaks regularly occur in many regions of the Pacific, little is known about dengue in the Republic of the Marshall Islands (RMI). To better understand dengue in RMI, we investigated an explosive outbreak that began in October 2011. Suspected cases were reported to the Ministry of Health, serum specimens were tested with a dengue rapid diagnostic test (RDT), and confirmatory testing was performed using RT-PCR and IgM ELISA. Laboratory-positive cases were defined by detection of DENV nonstructural protein 1 by RDT, DENV nucleic acid by RT-PCR, or anti-DENV IgM antibody by RDT or ELISA. Secondary infection was defined by detection of anti-DENV IgG antibody by ELISA in a laboratory-positive acute specimen. During the four months of the outbreak, 1,603 suspected dengue cases (3% of the RMI population) were reported. Of 867 (54%) laboratory-positive cases, 209 (24%) had dengue with warning signs, six (0.7%) had severe dengue, and none died. Dengue incidence was highest in residents of Majuro and individuals aged 10–29 years, and ∼95% of dengue cases were experiencing secondary infection. Only DENV-4 was detected by RT-PCR, which phylogenetic analysis demonstrated was most closely related to a virus previously identified in Southeast Asia. Cases of vertical DENV transmission, and DENV/Salmonella Typhi and DENV/Mycobacterium leprae co-infection were identified. Entomological surveys implicated water storage containers and discarded tires as the most important development sites for Aedes aegypti and Ae. albopictus, respectively. Although this is the first documented dengue outbreak in RMI, the age groups of cases and high prevalence of secondary infection demonstrate prior DENV circulation. Dengue surveillance should continue to be strengthened in RMI and throughout the Pacific to identify and rapidly respond to future outbreaks. PMID:25268134

Using Combined Diagnostic Test Results to Hindcast Trends of Infection from Cross-Sectional Data

PubMed Central

Rydevik, Gustaf; Innocent, Giles T.; Marion, Glenn; White, Piran C. L.; Billinis, Charalambos; Barrow, Paul; Mertens, Peter P. C.; Gavier-Widén, Dolores; Hutchings, Michael R.

2016-01-01

Infectious disease surveillance is key to limiting the consequences from infectious pathogens and maintaining animal and public health. Following the detection of a disease outbreak, a response in proportion to the severity of the outbreak is required. It is thus critical to obtain accurate information concerning the origin of the outbreak and its forward trajectory. However, there is often a lack of situational awareness that may lead to over- or under-reaction. There is a widening range of tests available for detecting pathogens, with typically different temporal characteristics, e.g. in terms of when peak test response occurs relative to time of exposure. We have developed a statistical framework that combines response level data from multiple diagnostic tests and is able to ‘hindcast’ (infer the historical trend of) an infectious disease epidemic. Assuming diagnostic test data from a cross-sectional sample of individuals infected with a pathogen during an outbreak, we use a Bayesian Markov Chain Monte Carlo (MCMC) approach to estimate time of exposure, and the overall epidemic trend in the population prior to the time of sampling. We evaluate the performance of this statistical framework on simulated data from epidemic trend curves and show that we can recover the parameter values of those trends. We also apply the framework to epidemic trend curves taken from two historical outbreaks: a bluetongue outbreak in cattle, and a whooping cough outbreak in humans. Together, these results show that hindcasting can estimate the time since infection for individuals and provide accurate estimates of epidemic trends, and can be used to distinguish whether an outbreak is increasing or past its peak. We conclude that if temporal characteristics of diagnostics are known, it is possible to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time. PMID:27384712

Nosocomial outbreak of extensively drug-resistant Pseudomonas aeruginosa associated with aromatherapy.

PubMed

Mayr, Astrid; Hinterberger, Guido; Lorenz, Ingo H; Kreidl, Peter; Mutschlechner, Wolfgang; Lass-Flörl, Cornelia

2017-04-01

An increase of extensively drug-resistant Pseudomonas aeruginosa (XDR-PA) in various clinical specimens among intensive care unit patients (n = 7) initiated an outbreak investigation consisting of patient data analyses, control of adherence to infection control guidelines, microbiologic surveys, and molecular-based studies. XDR-PA was detected in a jointly used aroma-oil nursing bottle for aromatherapy. We implemented the restriction of oil sharing among patients. Hence, the outbreak was controlled successfully. Copyright © 2017 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Pan-genome multilocus sequence typing and outbreak-specific reference-based single nucleotide polymorphism analysis to resolve two concurrent Staphylococcus aureus outbreaks in neonatal services.

PubMed

Roisin, S; Gaudin, C; De Mendonça, R; Bellon, J; Van Vaerenbergh, K; De Bruyne, K; Byl, B; Pouseele, H; Denis, O; Supply, P

2016-06-01

We used a two-step whole genome sequencing analysis for resolving two concurrent outbreaks in two neonatal services in Belgium, caused by exfoliative toxin A-encoding-gene-positive (eta+) methicillin-susceptible Staphylococcus aureus with an otherwise sporadic spa-type t209 (ST-109). Outbreak A involved 19 neonates and one healthcare worker in a Brussels hospital from May 2011 to October 2013. After a first episode interrupted by decolonization procedures applied over 7 months, the outbreak resumed concomitantly with the onset of outbreak B in a hospital in Asse, comprising 11 neonates and one healthcare worker from mid-2012 to January 2013. Pan-genome multilocus sequence typing, defined on the basis of 42 core and accessory reference genomes, and single-nucleotide polymorphisms mapped on an outbreak-specific de novo assembly were used to compare 28 available outbreak isolates and 19 eta+/spa-type t209 isolates identified by routine or nationwide surveillance. Pan-genome multilocus sequence typing showed that the outbreaks were caused by independent clones not closely related to any of the surveillance isolates. Isolates from only ten cases with overlapping stays in outbreak A, including four pairs of twins, showed no or only a single nucleotide polymorphism variation, indicating limited sequential transmission. Detection of larger genomic variation, even from the start of the outbreak, pointed to sporadic seeding from a pre-existing exogenous source, which persisted throughout the whole course of outbreak A. Whole genome sequencing analysis can provide unique fine-tuned insights into transmission pathways of complex outbreaks even at their inception, which, with timely use, could valuably guide efforts for early source identification. Copyright © 2016 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

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.

Unsupervised Topic Discovery by Anomaly Detection

DTIC Science & Technology

2013-09-01

Kullback , and R. A. Leibler , “On information and sufficiency,” Annals of Mathematical Statistics, vol. 22, no. 1, pp. 79–86, 1951. [14] S. Basu, A...read known publicly. There is a strong interest in the analysis of these opinions and comments as they provide useful information about the sentiments...them as topics. The difficulty in this approach is finding a good set of keywords that accurately represents the documents. The method used to

Graph Based Models for Unsupervised High Dimensional Data Clustering and Network Analysis

DTIC Science & Technology

2015-01-01

ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for...algorithms we proposed improve the time e ciency signi cantly for large scale datasets. In the last chapter, we also propose an incremental reseeding...plume detection in hyper-spectral video data. These graph based clustering algorithms we proposed improve the time efficiency significantly for large

Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds (PREPRINT)

DTIC Science & Technology

2006-09-01

Medioni, [11], estimates the local dimension using tensor voting . These recent works have clearly shown the necessity to go beyond manifold learning, into...2005. [11] P. Mordohai and G. Medioni. Unsupervised dimensionality estimation and manifold learning in high-dimensional spaces by tensor voting . In...walking, jumping, and arms waving. The whole run took 361 seconds in Matlab , while the classification time (PMM) can be neglected compared to the kNN

Unsupervised Spatial, Temporal and Relational Models for Social Processes

DTIC Science & Technology

2012-02-01

Andrej Mrvar . A partitioning approach to structural balance. Social Networks, 18(2):149 – 168, 1996 . [37] Thi V. Duong, Hung H. Bui, Dinh Q. Phung, and...partitioning provided by Doreian and Mrvar [36], who demonstrate that there was increasing evidence over time that 62 CHAPTER 4. COMMUNITY DETECTION this...foursome was a genuine group. Doreian and Mrvar used a block modeling approach optimiz- ing structural balance, a measure of cohesion incorporating

Evaluation of a Real-Time Reverse Transcription-PCR Assay for Detection of Enterovirus D68 in Clinical Samples from an Outbreak in New York State in 2014.

PubMed

Zhuge, Jian; Vail, Eric; Bush, Jeffrey L; Singelakis, Lauren; Huang, Weihua; Nolan, Sheila M; Haas, Janet P; Engel, Helen; Della Posta, Millicent; Yoon, Esther C; Fallon, John T; Wang, Guiqing

2015-06-01

An outbreak of severe respiratory illness associated with enterovirus D68 (EV-D68) infection was reported in mid-August 2014 in the United States. In this study, we evaluated the diagnostic utility of an EV-D68-specific real-time reverse transcription-PCR (rRT-PCR) that was recently developed by the Centers for Disease Control and Prevention in clinical samples. Nasopharyngeal (NP) swab specimens from patients in a recent outbreak of respiratory illness in the lower Hudson Valley, New York State, were collected and examined for the presence of human rhinovirus or enterovirus using the FilmArray Respiratory Panel (RP) assay. Samples positive by RP were assessed using EV-D68 rRT-PCR, and the data were compared to results from sequencing analysis of partial VP1 and 5' untranslated region (5'-UTR) sequences of the EV genome. A total of 285 RP-positive NP specimens (260 from the 2014 outbreak and 25 from 2013) were analyzed by rRT-PCR; EV-D68 was detected in 74 of 285 (26.0%) specimens examined. Data for comparisons between rRT-PCR and sequencing analysis were obtained from 194 NP specimens. EV-D68 detection was confirmed by sequencing analysis in 71 of 74 positive and in 1 of 120 randomly selected negative specimens by rRT-PCR. The EV-D68 rRT-PCR showed diagnostic sensitivity and specificity of 98.6% and 97.5%, respectively. Our data suggest that the EV-D68 rRT-PCR is a reliable assay for detection of EV-D68 in clinical samples and has a potential to be used as a tool for rapid diagnosis and outbreak investigation of EV-D68-associated infections in clinical and public health laboratories. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Evaluation of a Real-Time Reverse Transcription-PCR Assay for Detection of Enterovirus D68 in Clinical Samples from an Outbreak in New York State in 2014

PubMed Central

Zhuge, Jian; Vail, Eric; Bush, Jeffrey L.; Singelakis, Lauren; Huang, Weihua; Nolan, Sheila M.; Haas, Janet P.; Engel, Helen; Della Posta, Millicent; Yoon, Esther C.; Fallon, John T.

2015-01-01

An outbreak of severe respiratory illness associated with enterovirus D68 (EV-D68) infection was reported in mid-August 2014 in the United States. In this study, we evaluated the diagnostic utility of an EV-D68-specific real-time reverse transcription-PCR (rRT-PCR) that was recently developed by the Centers for Disease Control and Prevention in clinical samples. Nasopharyngeal (NP) swab specimens from patients in a recent outbreak of respiratory illness in the lower Hudson Valley, New York State, were collected and examined for the presence of human rhinovirus or enterovirus using the FilmArray Respiratory Panel (RP) assay. Samples positive by RP were assessed using EV-D68 rRT-PCR, and the data were compared to results from sequencing analysis of partial VP1 and 5′ untranslated region (5′-UTR) sequences of the EV genome. A total of 285 RP-positive NP specimens (260 from the 2014 outbreak and 25 from 2013) were analyzed by rRT-PCR; EV-D68 was detected in 74 of 285 (26.0%) specimens examined. Data for comparisons between rRT-PCR and sequencing analysis were obtained from 194 NP specimens. EV-D68 detection was confirmed by sequencing analysis in 71 of 74 positive and in 1 of 120 randomly selected negative specimens by rRT-PCR. The EV-D68 rRT-PCR showed diagnostic sensitivity and specificity of 98.6% and 97.5%, respectively. Our data suggest that the EV-D68 rRT-PCR is a reliable assay for detection of EV-D68 in clinical samples and has a potential to be used as a tool for rapid diagnosis and outbreak investigation of EV-D68-associated infections in clinical and public health laboratories. PMID:25854481

Usefulness of molecular markers in the diagnosis of occupational and recreational histoplasmosis outbreaks.

PubMed

Frías-De-León, María Guadalupe; Ramírez-Bárcenas, José Antonio; Rodríguez-Arellanes, Gabriela; Velasco-Castrejón, Oscar; Taylor, Maria Lucia; Reyes-Montes, María Del Rocío

2017-03-01

Histoplasmosis is considered the most important systemic mycosis in Mexico, and its diagnosis requires fast and reliable methodologies. The present study evaluated the usefulness of PCR using Hcp100 and 1281-1283 (220) molecular markers in detecting Histoplasma capsulatum in occupational and recreational outbreaks. Seven clinical serum samples of infected individuals from three different histoplasmosis outbreaks were processed by enzyme-linked immunosorbent assay (ELISA) to titre anti-H. capsulatum antibodies and to extract DNA. Fourteen environmental samples were also processed for H. capsulatum isolation and DNA extraction. Both clinical and environmental DNA samples were analysed by PCR with Hcp100 and 1281-1283 (220) markers. Antibodies to H. capsulatum were detected by ELISA in all serum samples using specific antigens, and in six of these samples, the PCR products of both molecular markers were amplified. Four environmental samples amplified one of the two markers, but only one sample amplified both markers and an isolate of H. capsulatum was cultured from this sample. All PCR products were sequenced, and the sequences for each marker were analysed using the Basic Local Alignment Search Tool (BLASTn), which revealed 95-98 and 98-100 % similarities with the reference sequences deposited in the GenBank for Hcp100 and 1281-1283 (220) , respectively. Both molecular markers proved to be useful in studying histoplasmosis outbreaks because they are matched for pathogen detection in either clinical or environmental samples.

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.

Transmission and molecular characterisation of wild measles virus in Romania, 2008 to 2012.

PubMed

Necula, G; Lazar, M; Stanescu, A; Pistol, A; Santibanez, S; Mankertz, A; Lupulescu, E

2013-12-12

Molecular characterisation of measles virus is a powerful tool for tracing transmission. Genotyping may prove the absence of endemic circulation of measles virus, i.e. transmission for more than 12 months, which is one of the criteria for verifying elimination of the disease. We have genetically characterised measles viruses detected in Romania from 2008 to 2012, focusing on the recent outbreaks from 2010 to 2012 that affected mainly groups with limited access to healthcare and schools. The findings emphasise the importance of genotyping during the different phases of an outbreak. A total of 8,170 cases were notified, and 5,093 (62%) of the 7,559 possible cases were serologically confirmed. RT-PCR was performed for 104 samples: from the 101 positive samples obtained from sporadic measles cases or clusters from different counties, 73 were genotyped. Sporadic measles cases associated with D4 and D5 viruses were observed from2008 to 2009. Genotype D4-Manchester was predominant in 2011 and 2012. In addition, the related variant D4-Maramures and MVs/Limoges.FRA/17.10[D4] and a few D4-Hamburg strains were detected. The detection of several distinct MV-D4 genotypes suggests multiple virus importations to Romania. The outbreak associated with D4 genotype is the second largest outbreak in Romania in less than 10 years.

Detection of Clostridium difficile infection clusters, using the temporal scan statistic, in a community hospital in southern Ontario, Canada, 2006-2011.

PubMed

Faires, Meredith C; Pearl, David L; Ciccotelli, William A; Berke, Olaf; Reid-Smith, Richard J; Weese, J Scott

2014-05-12

In hospitals, Clostridium difficile infection (CDI) surveillance relies on unvalidated guidelines or threshold criteria to identify outbreaks. This can result in false-positive and -negative cluster alarms. The application of statistical methods to identify and understand CDI clusters may be a useful alternative or complement to standard surveillance techniques. The objectives of this study were to investigate the utility of the temporal scan statistic for detecting CDI clusters and determine if there are significant differences in the rate of CDI cases by month, season, and year in a community hospital. Bacteriology reports of patients identified with a CDI from August 2006 to February 2011 were collected. For patients detected with CDI from March 2010 to February 2011, stool specimens were obtained. Clostridium difficile isolates were characterized by ribotyping and investigated for the presence of toxin genes by PCR. CDI clusters were investigated using a retrospective temporal scan test statistic. Statistically significant clusters were compared to known CDI outbreaks within the hospital. A negative binomial regression model was used to identify associations between year, season, month and the rate of CDI cases. Overall, 86 CDI cases were identified. Eighteen specimens were analyzed and nine ribotypes were classified with ribotype 027 (n = 6) the most prevalent. The temporal scan statistic identified significant CDI clusters at the hospital (n = 5), service (n = 6), and ward (n = 4) levels (P ≤ 0.05). Three clusters were concordant with the one C. difficile outbreak identified by hospital personnel. Two clusters were identified as potential outbreaks. The negative binomial model indicated years 2007-2010 (P ≤ 0.05) had decreased CDI rates compared to 2006 and spring had an increased CDI rate compared to the fall (P = 0.023). Application of the temporal scan statistic identified several clusters, including potential outbreaks not detected by hospital personnel. The identification of time periods with decreased or increased CDI rates may have been a result of specific hospital events. Understanding the clustering of CDIs can aid in the interpretation of surveillance data and lead to the development of better early detection systems.

European Surveillance for West Nile Virus in Mosquito Populations

PubMed Central

Engler, Olivier; Savini, Giovanni; Papa, Anna; Figuerola, Jordi; Groschup, Martin H.; Kampen, Helge; Medlock, Jolyon; Vaux, Alexander; Wilson, Anthony J.; Werner, Doreen; Jöst, Hanna; Goffredo, Maria; Capelli, Gioia; Federici, Valentina; Tonolla, Mauro; Patocchi, Nicola; Flacio, Eleonora; Portmann, Jasmine; Rossi-Pedruzzi, Anya; Mourelatos, Spiros; Ruiz, Santiago; Vázquez, Ana; Calzolari, Mattia; Bonilauri, Paolo; Dottori, Michele; Schaffner, Francis; Mathis, Alexander; Johnson, Nicholas

2013-01-01

A wide range of arthropod-borne viruses threaten both human and animal health either through their presence in Europe or through risk of introduction. Prominent among these is West Nile virus (WNV), primarily an avian virus, which has caused multiple outbreaks associated with human and equine mortality. Endemic outbreaks of West Nile fever have been reported in Italy, Greece, France, Romania, Hungary, Russia and Spain, with further spread expected. Most outbreaks in Western Europe have been due to infection with WNV Lineage 1. In Eastern Europe WNV Lineage 2 has been responsible for human and bird mortality, particularly in Greece, which has experienced extensive outbreaks over three consecutive years. Italy has experienced co-circulation with both virus lineages. The ability to manage this threat in a cost-effective way is dependent on early detection. Targeted surveillance for pathogens within mosquito populations offers the ability to detect viruses prior to their emergence in livestock, equine species or human populations. In addition, it can establish a baseline of mosquito-borne virus activity and allow monitoring of change to this over time. Early detection offers the opportunity to raise disease awareness, initiate vector control and preventative vaccination, now available for horses, and encourage personal protection against mosquito bites. This would have major benefits through financial savings and reduction in equid morbidity/mortality. However, effective surveillance that predicts virus outbreaks is challenged by a range of factors including limited resources, variation in mosquito capture rates (too few or too many), difficulties in mosquito identification, often reliant on specialist entomologists, and the sensitive, rapid detection of viruses in mosquito pools. Surveillance for WNV and other arboviruses within mosquito populations varies between European countries in the extent and focus of the surveillance. This study reviews the current status of WNV in mosquito populations across Europe and how this is informing our understanding of virus epidemiology. Key findings such as detection of virus, presence of vector species and invasive mosquito species are summarized, and some of the difficulties encountered when applying a cost-effective surveillance programme are highlighted. PMID:24157510

Tactics and strategies for managing Ebola outbreaks and the salience of immunization.

PubMed

Getz, Wayne M; Gonzalez, Jean-Paul; Salter, Richard; Bangura, James; Carlson, Colin; Coomber, Moinya; Dougherty, Eric; Kargbo, David; Wolfe, Nathan D; Wauquier, Nadia

2015-01-01

We present a stochastic transmission chain simulation model for Ebola viral disease (EVD) in West Africa, with the salutary result that the virus may be more controllable than previously suspected. The ongoing tactics to detect cases as rapidly as possible and isolate individuals as safely as practicable is essential to saving lives in the current outbreaks in Guinea, Liberia, and Sierra Leone. Equally important are educational campaigns that reduce contact rates between susceptible and infectious individuals in the community once an outbreak occurs. However, due to the relatively low R 0 of Ebola (around 1.5 to 2.5 next generation cases are produced per current generation case in naïve populations), rapid isolation of infectious individuals proves to be highly efficacious in containing outbreaks in new areas, while vaccination programs, even with low efficacy vaccines, can be decisive in curbing future outbreaks in areas where the Ebola virus is maintained in reservoir populations.

Seven Salmonella Typhimurium Outbreaks in Australia Linked by Trace-Back and Whole Genome Sequencing.

PubMed

Ford, Laura; Wang, Qinning; Stafford, Russell; Ressler, Kelly-Anne; Norton, Sophie; Shadbolt, Craig; Hope, Kirsty; Franklin, Neil; Krsteski, Radomir; Carswell, Adrienne; Carter, Glen P; Seemann, Torsten; Howard, Peter; Valcanis, Mary; Castillo, Cristina Fabiola Sotomayor; Bates, John; Glass, Kathryn; Williamson, Deborah A; Sintchenko, Vitali; Howden, Benjamin P; Kirk, Martyn D

2018-05-01

Salmonella Typhimurium is a common cause of foodborne illness in Australia. We report on seven outbreaks of Salmonella Typhimurium multilocus variable-number tandem-repeat analysis (MLVA) 03-26-13-08-523 (European convention 2-24-12-7-0212) in three Australian states and territories investigated between November 2015 and March 2016. We identified a common egg grading facility in five of the outbreaks. While no Salmonella Typhimurium was detected at the grading facility and eggs could not be traced back to a particular farm, whole genome sequencing (WGS) of isolates from cases from all seven outbreaks indicated a common source. WGS was able to provide higher discriminatory power than MLVA and will likely link more Salmonella Typhimurium cases between states and territories in the future. National harmonization of Salmonella surveillance is important for effective implementation of WGS for Salmonella outbreak investigations.

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.

Anthrax Outbreaks in Bangladesh, 2009–2010

PubMed Central

Chakraborty, Apurba; Khan, Salah Uddin; Hasnat, Mohammed Abul; Parveen, Shahana; Islam, M. Saiful; Mikolon, Andrea; Chakraborty, Ranjit Kumar; Ahmed, Be-Nazir; Ara, Khorsed; Haider, Najmul; Zaki, Sherif R.; Hoffmaster, Alex R.; Rahman, Mahmudur; Luby, Stephen P.; Hossain, M. Jahangir

2012-01-01

During August 2009–October 2010, a multidisciplinary team investigated 14 outbreaks of animal and human anthrax in Bangladesh to identify the etiology, pathway of transmission, and social, behavioral, and cultural factors that led to these outbreaks. The team identified 140 animal cases of anthrax and 273 human cases of cutaneous anthrax. Ninety one percent of persons in whom cutaneous anthrax developed had history of butchering sick animals, handling raw meat, contact with animal skin, or were present at slaughtering sites. Each year, Bacillus anthracis of identical genotypes were isolated from animal and human cases. Inadequate livestock vaccination coverage, lack of awareness of the risk of anthrax transmission from animal to humans, social norms and poverty contributed to these outbreaks. Addressing these challenges and adopting a joint animal and human health approach could contribute to detecting and preventing such outbreaks in the future. PMID:22492157

DETECTION OF CRYPTOSPORIDIUM OOCYSTS IN WATER MATRICES

EPA Science Inventory

Since the advent and recognition of waterborne outbreaks of cryptosporidiosis great effort has been expended on development of methods for detecting Cryptosporidium oocysts in water. Oocysts recovery rates using a method originally developed for detecting Giardia cysts ranged fr...

Best friends' interactions and substance use: The role of friend pressure and unsupervised co-deviancy.

PubMed

Tsakpinoglou, Florence; Poulin, François

2017-10-01

Best friends exert a substantial influence on rising alcohol and marijuana use during adolescence. Two mechanisms occurring within friendship - friend pressure and unsupervised co-deviancy - may partially capture the way friends influence one another. The current study aims to: (1) examine the psychometric properties of a new instrument designed to assess pressure from a youth's best friend and unsupervised co-deviancy; (2) investigate the relative contribution of these processes to alcohol and marijuana use; and (3) determine whether gender moderates these associations. Data were collected through self-report questionnaires completed by 294 Canadian youths (62% female) across two time points (ages 15-16). Principal component analysis yielded a two-factor solution corresponding to friend pressure and unsupervised co-deviancy. Logistic regressions subsequently showed that unsupervised co-deviancy was predictive of an increase in marijuana use one year later. Neither process predicted an increase in alcohol use. Results did not differ as a function of gender. Copyright © 2017 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Role of poultry in the H7N9 influenza outbreaks in China

USDA-ARS?s Scientific Manuscript database

The outbreaks of avian influenza A (H7N9) occurring in China in 2013 and 2014 have resulted in more than 370 human cases with a 30% fatality rate. Most of these infections are believed to result from exposure to infected poultry or contaminated environments as the viruses have been detected in avia...

West Nile Virus Outbreak in Houston and Harris County, Texas, USA, 2014.

PubMed

Martinez, Diana; Murray, Kristy O; Reyna, Martin; Arafat, Raouf R; Gorena, Roberto; Shah, Umair A; Debboun, Mustapha

2017-08-01

Since 2002, West Nile virus (WNV) has been detected every year in Houston and the surrounding Harris County, Texas. In 2014, the largest WNV outbreak to date occurred, comprising 139 cases and causing 2 deaths. Additionally, 1,286 WNV-positive mosquito pools were confirmed, the most reported in a single mosquito season.

Wavelet-based Gaussian-mixture hidden Markov model for the detection of multistage seizure dynamics: A proof-of-concept study

PubMed Central

2011-01-01

Background Epilepsy is a common neurological disorder characterized by recurrent electrophysiological activities, known as seizures. Without the appropriate detection strategies, these seizure episodes can dramatically affect the quality of life for those afflicted. The rationale of this study is to develop an unsupervised algorithm for the detection of seizure states so that it may be implemented along with potential intervention strategies. Methods Hidden Markov model (HMM) was developed to interpret the state transitions of the in vitro rat hippocampal slice local field potentials (LFPs) during seizure episodes. It can be used to estimate the probability of state transitions and the corresponding characteristics of each state. Wavelet features were clustered and used to differentiate the electrophysiological characteristics at each corresponding HMM states. Using unsupervised training method, the HMM and the clustering parameters were obtained simultaneously. The HMM states were then assigned to the electrophysiological data using expert guided technique. Minimum redundancy maximum relevance (mRMR) analysis and Akaike Information Criterion (AICc) were applied to reduce the effect of over-fitting. The sensitivity, specificity and optimality index of chronic seizure detection were compared for various HMM topologies. The ability of distinguishing early and late tonic firing patterns prior to chronic seizures were also evaluated. Results Significant improvement in state detection performance was achieved when additional wavelet coefficient rates of change information were used as features. The final HMM topology obtained using mRMR and AICc was able to detect non-ictal (interictal), early and late tonic firing, chronic seizures and postictal activities. A mean sensitivity of 95.7%, mean specificity of 98.9% and optimality index of 0.995 in the detection of chronic seizures was achieved. The detection of early and late tonic firing was validated with experimental intracellular electrical recordings of seizures. Conclusions The HMM implementation of a seizure dynamics detector is an improvement over existing approaches using visual detection and complexity measures. The subjectivity involved in partitioning the observed data prior to training can be eliminated. It can also decipher the probabilities of seizure state transitions using the magnitude and rate of change wavelet information of the LFPs. PMID:21504608

An outbreak of hepatitis A in Roma populations living in three prefectures in Greece.

PubMed

Vantarakis, A; Nearxou, A; Pagonidis, D; Melegos, F; Seretidis, J; Kokkinos, P; Zarkadis, I; Parasidis, T; Alamanos, Y

2010-07-01

An outbreak of hepatitis A virus (HAV) infection affected Roma populations living in three prefectures of northeastern Greece. Between July and November 2007, 124 cases were reported. We carried out investigations to characterize the pathogen, to identify the source of infection and the route of transmission. Using the RT-PCR technique, HAV strains of the same genotype were detected in all sera from a subset of patients with acute disease. These showed more than 99.8% identity, suggesting a common source. A questionnaire was also completed to collect clinical and epidemiological information. The outbreak affected mainly Roma children aged <10 years. An inspection of Roma settlements showed that poor sanitary conditions were associated with the HAV outbreak.

Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography

PubMed Central

Wang, Zhuo; Camino, Acner; Zhang, Miao; Wang, Jie; Hwang, Thomas S.; Wilson, David J.; Huang, David; Li, Dengwang; Jia, Yali

2017-01-01

Diabetic retinopathy is a pathology where microvascular circulation abnormalities ultimately result in photoreceptor disruption and, consequently, permanent loss of vision. Here, we developed a method that automatically detects photoreceptor disruption in mild diabetic retinopathy by mapping ellipsoid zone reflectance abnormalities from en face optical coherence tomography images. The algorithm uses a fuzzy c-means scheme with a redefined membership function to assign a defect severity level on each pixel and generate a probability map of defect category affiliation. A novel scheme of unsupervised clustering optimization allows accurate detection of the affected area. The achieved accuracy, sensitivity and specificity were about 90% on a population of thirteen diseased subjects. This method shows potential for accurate and fast detection of early biomarkers in diabetic retinopathy evolution. PMID:29296475

Spatial-time-state fusion algorithm for defect detection through eddy current pulsed thermography

NASA Astrophysics Data System (ADS)

Xiao, Xiang; Gao, Bin; Woo, Wai Lok; Tian, Gui Yun; Xiao, Xiao Ting

2018-05-01

Eddy Current Pulsed Thermography (ECPT) has received extensive attention due to its high sensitive of detectability on surface and subsurface cracks. However, it remains as a difficult challenge in unsupervised detection as to identify defects without knowing any prior knowledge. This paper presents a spatial-time-state features fusion algorithm to obtain fully profile of the defects by directional scanning. The proposed method is intended to conduct features extraction by using independent component analysis (ICA) and automatic features selection embedding genetic algorithm. Finally, the optimal feature of each step is fused to obtain defects reconstruction by applying common orthogonal basis extraction (COBE) method. Experiments have been conducted to validate the study and verify the efficacy of the proposed method on blind defect detection.

Comparison between genetic algorithm and self organizing map to detect botnet network traffic

NASA Astrophysics Data System (ADS)

Yugandhara Prabhakar, Shinde; Parganiha, Pratishtha; Madhu Viswanatham, V.; Nirmala, M.

2017-11-01

In Cyber Security world the botnet attacks are increasing. To detect botnet is a challenging task. Botnet is a group of computers connected in a coordinated fashion to do malicious activities. Many techniques have been developed and used to detect and prevent botnet traffic and the attacks. In this paper, a comparative study is done on Genetic Algorithm (GA) and Self Organizing Map (SOM) to detect the botnet network traffic. Both are soft computing techniques and used in this paper as data analytics system. GA is based on natural evolution process and SOM is an Artificial Neural Network type, uses unsupervised learning techniques. SOM uses neurons and classifies the data according to the neurons. Sample of KDD99 dataset is used as input to GA and SOM.

Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography.

PubMed

Wang, Zhuo; Camino, Acner; Zhang, Miao; Wang, Jie; Hwang, Thomas S; Wilson, David J; Huang, David; Li, Dengwang; Jia, Yali

2017-12-01

Diabetic retinopathy is a pathology where microvascular circulation abnormalities ultimately result in photoreceptor disruption and, consequently, permanent loss of vision. Here, we developed a method that automatically detects photoreceptor disruption in mild diabetic retinopathy by mapping ellipsoid zone reflectance abnormalities from en face optical coherence tomography images. The algorithm uses a fuzzy c-means scheme with a redefined membership function to assign a defect severity level on each pixel and generate a probability map of defect category affiliation. A novel scheme of unsupervised clustering optimization allows accurate detection of the affected area. The achieved accuracy, sensitivity and specificity were about 90% on a population of thirteen diseased subjects. This method shows potential for accurate and fast detection of early biomarkers in diabetic retinopathy evolution.

Detection, Isolation, and Molecular Subtyping of Escherichia coli O157:H7 and Campylobacter jejuni Associated with a Large Waterborne Outbreak

PubMed Central

Bopp, Dianna J.; Sauders, Brian D.; Waring, Alfred L.; Ackelsberg, Joel; Dumas, Nellie; Braun-Howland, Ellen; Dziewulski, David; Wallace, Barbara J.; Kelly, Molly; Halse, Tanya; Musser, Kimberlee Aruda; Smith, Perry F.; Morse, Dale L.; Limberger, Ronald J.

2003-01-01

The largest reported outbreak of waterborne Escherichia coli O157:H7 in the United States occurred in upstate New York following a county fair in August 1999. Culture methods were used to isolate E. coli O157:H7 from specimens from 128 of 775 patients with suspected infections. Campylobacter jejuni was also isolated from stools of 44 persons who developed diarrheal illness after attending this fair. There was one case of a confirmed coinfection with E. coli O157:H7 and C. jejuni. Molecular detection of stx1 and stx2 Shiga toxin genes, immunomagnetic separation (IMS), and selective culture enrichment were utilized to detect and isolate E. coli O157:H7 from an unchlorinated well and its distribution points, a dry well, and a nearby septic tank. PCR for stx1 and stx2 was shown to provide a useful screen for toxin-producing E. coli O157:H7, and IMS subculture improved recovery. Pulsed-field gel electrophoresis (PFGE) was used to compare patient and environmental E. coli O157:H7 isolates. Among patient isolates, 117 of 128 (91.5%) were type 1 or 1a (three or fewer bands different). Among the water distribution system isolates, 13 of 19 (68%) were type 1 or 1a. Additionally, PFGE of C. jejuni isolates revealed that 29 of 35 (83%) had indistinguishable PFGE patterns. The PFGE results implicated the water distribution system as the main source of the E. coli O157:H7 outbreak. This investigation demonstrates the potential for outbreaks involving more than one pathogen and the importance of analyzing isolates from multiple patients and environmental samples to develop a better understanding of bacterial transmission during an outbreak. PMID:12517844

Social Network Sensors for Early Detection of Contagious Outbreaks

PubMed Central

Christakis, Nicholas A.; Fowler, James H.

2010-01-01

Current methods for the detection of contagious outbreaks give contemporaneous information about the course of an epidemic at best. It is known that individuals near the center of a social network are likely to be infected sooner during the course of an outbreak, on average, than those at the periphery. Unfortunately, mapping a whole network to identify central individuals who might be monitored for infection is typically very difficult. We propose an alternative strategy that does not require ascertainment of global network structure, namely, simply monitoring the friends of randomly selected individuals. Such individuals are known to be more central. To evaluate whether such a friend group could indeed provide early detection, we studied a flu outbreak at Harvard College in late 2009. We followed 744 students who were either members of a group of randomly chosen individuals or a group of their friends. Based on clinical diagnoses, the progression of the epidemic in the friend group occurred 13.9 days (95% C.I. 9.9–16.6) in advance of the randomly chosen group (i.e., the population as a whole). The friend group also showed a significant lead time (p<0.05) on day 16 of the epidemic, a full 46 days before the peak in daily incidence in the population as a whole. This sensor method could provide significant additional time to react to epidemics in small or large populations under surveillance. The amount of lead time will depend on features of the outbreak and the network at hand. The method could in principle be generalized to other biological, psychological, informational, or behavioral contagions that spread in networks. PMID:20856792

Early warning signal for dengue outbreaks and identification of high risk areas for dengue fever in Colombia using climate and non-climate datasets.

PubMed

Lee, Jung-Seok; Carabali, Mabel; Lim, Jacqueline K; Herrera, Victor M; Park, Il-Yeon; Villar, Luis; Farlow, Andrew

2017-07-10

Dengue has been prevalent in Colombia with high risk of outbreaks in various locations. While the prediction of dengue epidemics will bring significant benefits to the society, accurate forecasts have been a challenge. Given competing health demands in Colombia, it is critical to consider the effective use of the limited healthcare resources by identifying high risk areas for dengue fever. The Climate Risk Factor (CRF) index was constructed based upon temperature, precipitation, and humidity. Considering the conditions necessary for vector survival and transmission behavior, elevation and population density were taken into account. An Early Warning Signal (EWS) model was developed by estimating the elasticity of the climate risk factor function to detect dengue epidemics. The climate risk factor index was further estimated at the smaller geographical unit (5 km by 5 km resolution) to identify populations at high risk. From January 2007 to December 2015, the Early Warning Signal model successfully detected 75% of the total number of outbreaks 1 ~ 5 months ahead of time, 12.5% in the same month, and missed 12.5% of all outbreaks. The climate risk factors showed that populations at high risk are concentrated in the Western part of Colombia where more suitable climate conditions for vector mosquitoes and the high population level were observed compared to the East. This study concludes that it is possible to detect dengue outbreaks ahead of time and identify populations at high risk for various disease prevention activities based upon observed climate and non-climate information. The study outcomes can be used to minimize potential societal losses by prioritizing limited healthcare services and resources, as well as by conducting vector control activities prior to experiencing epidemics.

An unsupervised classification approach for analysis of Landsat data to monitor land reclamation in Belmont county, Ohio

NASA Technical Reports Server (NTRS)

Brumfield, J. O.; Bloemer, H. H. L.; Campbell, W. J.

1981-01-01

Two unsupervised classification procedures for analyzing Landsat data used to monitor land reclamation in a surface mining area in east central Ohio are compared for agreement with data collected from the corresponding locations on the ground. One procedure is based on a traditional unsupervised-clustering/maximum-likelihood algorithm sequence that assumes spectral groupings in the Landsat data in n-dimensional space; the other is based on a nontraditional unsupervised-clustering/canonical-transformation/clustering algorithm sequence that not only assumes spectral groupings in n-dimensional space but also includes an additional feature-extraction technique. It is found that the nontraditional procedure provides an appreciable improvement in spectral groupings and apparently increases the level of accuracy in the classification of land cover categories.

Rapid MALDI-TOF Mass Spectrometry Strain Typing during a Large Outbreak of Shiga-Toxigenic Escherichia coli

PubMed Central

Christner, Martin; Trusch, Maria; Rohde, Holger; Kwiatkowski, Marcel; Schlüter, Hartmut; Wolters, Manuel; Aepfelbacher, Martin; Hentschke, Moritz

2014-01-01

Background In 2011 northern Germany experienced a large outbreak of Shiga-Toxigenic Escherichia coli O104:H4. The large amount of samples sent to microbiology laboratories for epidemiological assessment highlighted the importance of fast and inexpensive typing procedures. We have therefore evaluated the applicability of a MALDI-TOF mass spectrometry based strategy for outbreak strain identification. Methods Specific peaks in the outbreak strain’s spectrum were identified by comparative analysis of archived pre-outbreak spectra that had been acquired for routine species-level identification. Proteins underlying these discriminatory peaks were identified by liquid chromatography tandem mass spectrometry and validated against publicly available databases. The resulting typing scheme was evaluated against PCR genotyping with 294 E. coli isolates from clinical samples collected during the outbreak. Results Comparative spectrum analysis revealed two characteristic peaks at m/z 6711 and m/z 10883. The underlying proteins were found to be of low prevalence among genome sequenced E. coli strains. Marker peak detection correctly classified 292 of 293 study isolates, including all 104 outbreak isolates. Conclusions MALDI-TOF mass spectrometry allowed for reliable outbreak strain identification during a large outbreak of Shiga-Toxigenic E. coli. The applied typing strategy could probably be adapted to other typing tasks and might facilitate epidemiological surveys as part of the routine pathogen identification workflow. PMID:25003758

Preventing Community-wide Transmission of Cryptosporidium: A Proactive Public Health Response to a Swimming Pool–Associated Outbreak — Auglaize County, Ohio, USA

PubMed Central

Cope, J.R.; Prosser, A.; Nowicki, S.; Roberts, M.W.; Scheer, D.; Anderson, C.; Longsworth, A.; Parsons, C.; Goldschmidt, D.; Johnston, S.; Bishop, H.; Xiao, L.; Hill, V.; Beach, M.; Hlavsa, M.C.

2015-01-01

Summary The incidence of recreational water–associated outbreaks in the United States has significantly increased, driven, at least in part, by outbreaks both caused by Cryptosporidium and associated with treated recreational water venues. Because of the parasite's extreme chlorine tolerance, transmission can occur even in well-maintained treated recreational water venues, (e.g., pools) and a focal cryptosporidiosis outbreak can evolve into a community-wide outbreak associated with multiple recreational water venues and settings (e.g., child care facilities). In August 2004 in Auglaize County, Ohio, multiple cryptosporidiosis cases were identified and anecdotally linked to Pool A. Within 5 days of the first case being reported, Pool A was hyperchlorinated to achieve 99.9% Cryptosporidium inactivition. A case-control study was launched to epidemiologically ascertain the outbreak source 11 days later. A total of 150 confirmed and probable cases were identified; the temporal distribution of illness onset was peaked, indicating a point-source exposure. Cryptosporidiosis was significantly associated with swimming in Pool A (matched odds ratio 121.7, 95% confidence interval 27.4–∞) but not with another venue or setting. The findings of this investigation suggest that proactive implementation of control measures, when increased Cryptosporidium transmission is detected but before an outbreak source is epidemiologically ascertained, might prevent a focal cryptosporidiosis outbreak from evolving into a community-wide outbreak. PMID:25907106

Tularaemia outbreaks in Sakarya, Turkey: case-control and environmental studies.

PubMed

Meric, M; Sayan, M; Dundar, D; Willke, A

2010-08-01

Tularaemia is an important zoonotic disease that leads to outbreaks. This study aimed to compare the epidemiological characteristics of two tularaemia outbreaks that occurred in the Sakarya region of Turkey, analyse the risk factors for the development of outbreaks and identify Francisella (F.) tularensis in the water samples. Two tularaemia outbreaks occurred in the Kocadongel village in 2005 and 2006. A field investigation and a case-control study with 47 cases and 47 healthy households were performed during the second outbreak. Clinical samples from the patients and filtrated water samples were analysed for F. tularensis via real-time polymerase chain reaction. From the two outbreaks, a total of 58 patients were diagnosed with oropharyngeal tularaemia based on their clinical and serological results. Both outbreaks occurred between the months of January and April, and the number of patients peaked in February. Logistic regression analysis revealed that the consumption of natural spring water was the only significant risk factor for tularaemia infection (odds ratio 3.5, confidence interval 1.23-10.07). F. tularensis was detected in eight clinical samples and in the filtrated natural spring water. This study is the first report of tularaemia from this region. The results show that both tularaemia outbreaks were related to the consumption of untreated natural spring water. To prevent waterborne tularaemia, community water supplies should be treated and checked periodically.

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.

[Norovirus outbreaks in hospitals and nursing homes in Catalonia, Spain].

PubMed

Godoy, Pere; Domínguez, Angela; Alvarez, Josep; Camps, Neus; Barrabeig, Irene; Bartolomé, Rosa; Sala, María Rosa; Ferre, Dolors; Pañella, Helena; Torres, Joan; Minguell, Sofía; Alsedà, Miquel; Pumares, Analía

2009-01-01

The low infectious dose and multiple transmission routes favour the appearance of norovirus outbreaks. The objective of this study was to compare the incidence of norovirus outbreaks in hospitals and nursing homes in Catalonia. A descriptive study of norovirus outbreaks between 15/10/2004 and 30/10/2005 was carried out. An epidemiological survey was completed for each outbreak. Norovirus in clinical samples was determined by PCR techniques. The incidence in each centre and the annual incidence of outbreaks by centre were calculated. Differences were calculated using the chi-square test and the Student's t test, taking a p value of > 0.05 as significant. Seventeen outbreaks (6 in hospitals and 11 in nursing homes) were detected. The global attack rate was 33.4% (652/1951) and was slightly higher in nursing homes (35.2%) than in hospitals (31.4%). A total of 94.1% (16/17) of outbreaks were caused by person-to-person transmission and only 5.9% (1/17) by foods. The mean number of days between the first and last case was 11.4 (SD = 6.9). The mean duration of symptoms was 2.39 days (SD=1.6), and was higher hospitals, 2.63 (SD=1.7), than in nursing homes, 1.97 (SD=1.7) (p < 0.0001). Norovirus is responsible for a large number of outbreaks due to person-to-person transmission. Control should be standardized to reduce the number and duration of 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.

Whole genome sequencing of Salmonella Typhimurium illuminates distinct outbreaks caused by an endemic multi-locus variable number tandem repeat analysis type in Australia, 2014.

PubMed

Phillips, Anastasia; Sotomayor, Cristina; Wang, Qinning; Holmes, Nadine; Furlong, Catriona; Ward, Kate; Howard, Peter; Octavia, Sophie; Lan, Ruiting; Sintchenko, Vitali

2016-09-15

Salmonella Typhimurium (STM) is an important cause of foodborne outbreaks worldwide. Subtyping of STM remains critical to outbreak investigation, yet current techniques (e.g. multilocus variable number tandem repeat analysis, MLVA) may provide insufficient discrimination. Whole genome sequencing (WGS) offers potentially greater discriminatory power to support infectious disease surveillance. We performed WGS on 62 STM isolates of a single, endemic MLVA type associated with two epidemiologically independent, food-borne outbreaks along with sporadic cases in New South Wales, Australia, during 2014. Genomes of case and environmental isolates were sequenced using HiSeq (Illumina) and the genetic distance between them was assessed by single nucleotide polymorphism (SNP) analysis. SNP analysis was compared to the epidemiological context. The WGS analysis supported epidemiological evidence and genomes of within-outbreak isolates were nearly identical. Sporadic cases differed from outbreak cases by a small number of SNPs, although their close relationship to outbreak cases may represent an unidentified common food source that may warrant further public health follow up. Previously unrecognised mini-clusters were detected. WGS of STM can discriminate foodborne community outbreaks within a single endemic MLVA clone. Our findings support the translation of WGS into public health laboratory surveillance of salmonellosis.

Emergence of norovirus GI.2 outbreaks in military camps in Singapore.

PubMed

Ho, Zheng Jie Marc; Vithia, Gunalan; Ng, Ching Ging; Maurer-Stroh, Sebastian; Tan, Clive M; Loh, Jimmy; Lin, Tzer Pin Raymond; Lee, Jian Ming Vernon

2015-02-01

Simultaneous acute gastroenteritis (AGE) outbreaks occurred at two military camps. This study details the epidemiological findings, explores possible origins, and discusses preventive measures. Investigations included attack rate surveys, symptom surveys, hygiene inspections, and the testing of water, food, and stool samples. DNA/RNA was extracted from stool samples and amplified via real-time reverse transcription PCR (RT-PCR). Partial and full-length capsid nucleotide sequences were obtained, phylogenetic relationships inferred, and homology modelling of antigenic sites performed. The military outbreaks involved 775 persons and were preceded by two AGE outbreaks at restaurants in the local community. The outbreak was longer and larger in the bigger camp (21 days, attack rate 15.0%) than the smaller camp (6 days, attack rate 8.3%). Of 198 stool samples, norovirus GI.2 was detected in 32.5% (larger camp) and 28.6% (smaller camp). These were essentially identical to preceding community outbreaks. Antigenic site homology modelling also showed differences between identified and more common AGE outbreak strains (norovirus GII.4). Differences observed highlight difficulties in controlling person-to-person outbreaks among large groups in close proximity (e.g., military trainees). Distinct differences in antigenic sites may have contributed to increased immunological susceptibility of the soldiers to infection. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Prevalence and Level of Listeria monocytogenes in Ice Cream Linked to a Listeriosis Outbreak in the United States.

PubMed

Chen, Y I; Burall, Laurel S; Macarisin, Dumitru; Pouillot, Régis; Strain, Errol; DE Jesus, Antonio J; Laasri, Anna; Wang, Hua; Ali, Laila; Tatavarthy, Aparna; Zhang, Guodong; Hu, Lijun; Day, James; Kang, Jihun; Sahu, Surasri; Srinivasan, Devayani; Klontz, Karl; Parish, Mickey; Evans, Peter S; Brown, Eric W; Hammack, Thomas S; Zink, Donald L; Datta, Atin R

2016-11-01

A most-probable-number (MPN) method was used to enumerate Listeria monocytogenes in 2,320 commercial ice cream scoops manufactured on a production line that was implicated in a 2015 listeriosis outbreak in the United States. The analyzed samples were collected from seven lots produced in November 2014, December 2014, January 2015, and March 2015. L. monocytogenes was detected in 99% (2,307 of 2,320) of the tested samples (lower limit of detection, 0.03 MPN/g), 92% of which were contaminated at <20 MPN/g. The levels of L. monocytogenes in these samples had a geometric mean per lot of 0.15 to 7.1 MPN/g. The prevalence and enumeration data from an unprecedented large number of naturally contaminated ice cream products linked to a listeriosis outbreak provided a unique data set for further understanding the risk associated with L. monocytogenes contamination for highly susceptible populations.

The scenario of norovirus contamination in food and food handlers.

PubMed

Tuan Zainazor, C; Hidayah, M S Noor; Chai, L C; Tunung, R; Ghazali, F Mohamad; Son, R

2010-02-01

Recently, many cases related to viral gastroenteritis outbreaks have been reported all over the world. Noroviruses are found to be leading as the major cause of outbreaks of acute gastroenteritis. Patients with the acute gastroenteritis normally found to be positive with norovirus when stools and vomit were analyzed. This paper reviews various activities and previous reports that describe norovirus contaminated in various food matrixes and relationship between food handlers. Lately, a numbers of norovirus outbreaks have been reported which are involved fresh produce (such as vegetables, fruits), shellfish and prepared food. Food produces by infected food handlers may therefore easily contaminated. In addition, food that required much handling and have been eaten without heat treatment gave the high risk for getting foodborne illnesses. The standard method for detection of norovirus has already been available for stool samples. However, only few methods for detection of norovirus in food samples have been developed until now.

Molecular detection and point-of-care testing in Ebola virus disease and other threats: a new global public health framework to stop outbreaks.

PubMed

Kost, Gerald J; Ferguson, William; Truong, Anh-Thu; Hoe, Jackie; Prom, Daisy; Banpavichit, Arirat; Kongpila, Surin

2015-01-01

Ultrahigh sensitivity and specificity assays that detect Ebola virus disease or other highly contagious and deadly diseases quickly and successfully upstream in Spatial Care Paths™ can stop outbreaks from escalating into devastating epidemics ravaging communities locally and countries globally. Even had the WHO and CDC responded more quickly and not misjudged the dissemination of Ebola in West Africa and other world regions, mobile rapid diagnostics were, and still are, not readily available for immediate and definitive diagnosis, a stunning strategic flaw that needs correcting worldwide. This article strategizes point-of-care testing for diagnosis, triage, monitoring, recovery and stopping outbreaks in the USA and other countries; reviews Ebola molecular diagnostics, summarizes USA FDA emergency use authorizations and documents why they should not be stop-gaps; and reduces community risk from internal and external infectious disease threats by enabling public health at points of need.

Western blot (immunoblot) assay of small, round-structured virus associated with an acute gastroenteritis outbreak in Tokyo.

PubMed

Hayashi, Y; Ando, T; Utagawa, E; Sekine, S; Okada, S; Yabuuchi, K; Miki, T; Ohashi, M

1989-08-01

Small, round-structured virus (SRSV) was detected in a stool specimen of a patient during an acute gastroenteritis outbreak in Tokyo and was tentatively named SRSV-9. SRSV-9 was purified by sucrose velocity gradient centrifugation after CsCl density gradient centrifugation. The buoyant density of SRSV-9 appeared to be 1.36 g/ml in CsCl. A Western blot (immunoblot) assay using the biotin-avidin system revealed that SRSV-9 was antigenically related to the Hawaii agent but distinct from the Norwalk agent and contained a single major structural protein with a molecular size of 63.0 +/- 0.6 kilodaltons. The prevalence of SRSV-9 infection in Tokyo was surveyed by the Western blot antibody assay by using a crude virus preparation as the antigen. Seroconversion was observed in 56.5% of the patients involved in the outbreaks from which SRSV was detected by electron microscopy.

Real-Time PCR Typing of Escherichia coli Based on Multiple Single Nucleotide Polymorphisms--a Convenient and Rapid Method.

PubMed

Lager, Malin; Mernelius, Sara; Löfgren, Sture; Söderman, Jan

2016-01-01

Healthcare-associated infections caused by Escherichia coli and antibiotic resistance due to extended-spectrum beta-lactamase (ESBL) production constitute a threat against patient safety. To identify, track, and control outbreaks and to detect emerging virulent clones, typing tools of sufficient discriminatory power that generate reproducible and unambiguous data are needed. A probe based real-time PCR method targeting multiple single nucleotide polymorphisms (SNP) was developed. The method was based on the multi locus sequence typing scheme of Institute Pasteur and by adaptation of previously described typing assays. An 8 SNP-panel that reached a Simpson's diversity index of 0.95 was established, based on analysis of sporadic E. coli cases (ESBL n = 27 and non-ESBL n = 53). This multi-SNP assay was used to identify the sequence type 131 (ST131) complex according to the Achtman's multi locus sequence typing scheme. However, it did not fully discriminate within the complex but provided a diagnostic signature that outperformed a previously described detection assay. Pulsed-field gel electrophoresis typing of isolates from a presumed outbreak (n = 22) identified two outbreaks (ST127 and ST131) and three different non-outbreak-related isolates. Multi-SNP typing generated congruent data except for one non-outbreak-related ST131 isolate. We consider multi-SNP real-time PCR typing an accessible primary generic E. coli typing tool for rapid and uniform type identification.

Update on oral Chagas disease outbreaks in Venezuela: epidemiological, clinical and diagnostic approaches

PubMed Central

de Noya, Belkisyolé Alarcón; Díaz-Bello, Zoraida; Colmenares, Cecilia; Ruiz-Guevara, Raiza; Mauriello, Luciano; Muñoz-Calderón, Arturo; Noya, Oscar

2015-01-01

Orally transmitted Chagas disease has become a matter of concern due to outbreaks reported in four Latin American countries. Although several mechanisms for orally transmitted Chagas disease transmission have been proposed, food and beverages contaminated with whole infected triatomines or their faeces, which contain metacyclic trypomastigotes of Trypanosoma cruzi, seems to be the primary vehicle. In 2007, the first recognised outbreak of orally transmitted Chagas disease occurred in Venezuela and largest recorded outbreak at that time. Since then, 10 outbreaks (four in Caracas) with 249 cases (73.5% children) and 4% mortality have occurred. The absence of contact with the vector and of traditional cutaneous and Romana’s signs, together with a florid spectrum of clinical manifestations during the acute phase, confuse the diagnosis of orally transmitted Chagas disease with other infectious diseases. The simultaneous detection of IgG and IgM by ELISA and the search for parasites in all individuals at risk have been valuable diagnostic tools for detecting acute cases. Follow-up studies regarding the microepidemics primarily affecting children has resulted in 70% infection persistence six years after anti-parasitic treatment. Panstrongylus geniculatus has been the incriminating vector in most cases. As a food-borne disease, this entity requires epidemiological, clinical, diagnostic and therapeutic approaches that differ from those approaches used for traditional direct or cutaneous vector transmission. PMID:25946155

Public Health Investigation of Two Outbreaks of Shiga Toxin-Producing Escherichia coli O157 Associated with Consumption of Watercress.

PubMed

Jenkins, Claire; Dallman, Timothy J; Launders, Naomi; Willis, Caroline; Byrne, Lisa; Jorgensen, Frieda; Eppinger, Mark; Adak, Goutam K; Aird, Heather; Elviss, Nicola; Grant, Kathie A; Morgan, Dilys; McLauchlin, Jim

2015-06-15

An increase in the number of cases of Shiga toxin-producing Escherichia coli (STEC) O157 phage type 2 (PT2) in England in September 2013 was epidemiologically linked to watercress consumption. Whole-genome sequencing (WGS) identified a phylogenetically related cluster of 22 cases (outbreak 1). The isolates comprising this cluster were not closely related to any other United Kingdom strain in the Public Health England WGS database, suggesting a possible imported source. A second outbreak of STEC O157 PT2 (outbreak 2) was identified epidemiologically following the detection of outbreak 1. Isolates associated with outbreak 2 were phylogenetically distinct from those in outbreak 1. Epidemiologically unrelated isolates on the same branch as the outbreak 2 cluster included those from human cases in England with domestically acquired infection and United Kingdom domestic cattle. Environmental sampling using PCR resulted in the isolation of STEC O157 PT2 from irrigation water at one implicated watercress farm, and WGS showed this isolate belonged to the same phylogenetic cluster as outbreak 2 isolates. Cattle were in close proximity to the watercress bed and were potentially the source of the second outbreak. Transfer of STEC from the field to the watercress bed may have occurred through wildlife entering the watercress farm or via runoff water. During this complex outbreak investigation, epidemiological studies, comprehensive testing of environmental samples, and the use of novel molecular methods proved invaluable in demonstrating that two simultaneous outbreaks of STEC O157 PT2 were both linked to the consumption of watercress but were associated with different sources of contamination. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Public Health Investigation of Two Outbreaks of Shiga Toxin-Producing Escherichia coli O157 Associated with Consumption of Watercress

PubMed Central

Dallman, Timothy J.; Launders, Naomi; Willis, Caroline; Byrne, Lisa; Jorgensen, Frieda; Eppinger, Mark; Adak, Goutam K.; Aird, Heather; Elviss, Nicola; Grant, Kathie A.; Morgan, Dilys; McLauchlin, Jim

2015-01-01

An increase in the number of cases of Shiga toxin-producing Escherichia coli (STEC) O157 phage type 2 (PT2) in England in September 2013 was epidemiologically linked to watercress consumption. Whole-genome sequencing (WGS) identified a phylogenetically related cluster of 22 cases (outbreak 1). The isolates comprising this cluster were not closely related to any other United Kingdom strain in the Public Health England WGS database, suggesting a possible imported source. A second outbreak of STEC O157 PT2 (outbreak 2) was identified epidemiologically following the detection of outbreak 1. Isolates associated with outbreak 2 were phylogenetically distinct from those in outbreak 1. Epidemiologically unrelated isolates on the same branch as the outbreak 2 cluster included those from human cases in England with domestically acquired infection and United Kingdom domestic cattle. Environmental sampling using PCR resulted in the isolation of STEC O157 PT2 from irrigation water at one implicated watercress farm, and WGS showed this isolate belonged to the same phylogenetic cluster as outbreak 2 isolates. Cattle were in close proximity to the watercress bed and were potentially the source of the second outbreak. Transfer of STEC from the field to the watercress bed may have occurred through wildlife entering the watercress farm or via runoff water. During this complex outbreak investigation, epidemiological studies, comprehensive testing of environmental samples, and the use of novel molecular methods proved invaluable in demonstrating that two simultaneous outbreaks of STEC O157 PT2 were both linked to the consumption of watercress but were associated with different sources of contamination. PMID:25841005

Best practices to prevent transmission and control outbreaks of hand, foot, and mouth disease in childcare facilities: a systematic review.

PubMed

Chan, J Hy; Law, C K; Hamblion, E; Fung, H; Rudge, J

2017-04-01

Hand, foot, and mouth disease continues to cause seasonal epidemics in the Asia-Pacific Region. Since the current Enterovirus 71 vaccines do not provide cross-protection for all Enterovirus species that cause hand, foot, and mouth disease, there is an urgent need to identify appropriate detection tools and best practice to prevent its transmission and to effectively control its outbreaks. This systematic review aimed to identify characteristics of outbreak and assess the impact and effectiveness of detection tools and public health preventive measures to interrupt transmission. The findings will be used to recommend policy on the most effective responses and interventions in Hong Kong to effectively minimise and contain the spread of the disease within childcare facilities. We searched the following databases for primary studies written in Chinese or English: MEDLINE, EMBASE, Global Health, WHO Western Pacific Region Index Medicus database, China National Knowledge Infrastructure Databases, and Chinese Scientific Journals Database. Studies conducted during or retrospective to outbreaks of hand, foot, and mouth disease caused by Enterovirus 71 from 1980 to 2012 within childcare facilities and with a study population of 0 to 6 years old were included. Sixteen studies conducted on outbreaks in China showed that hand, foot, and mouth disease spread rapidly within the facility, with an outbreak length of 4 to 46 days, especially in those with delayed notification (after 24 hours) of clustered outbreak (with five or more cases discovered within the facility) to the local Center for Disease Control and Prevention and delayed implementation of a control response. The number of classes affected ranged from 1 to 13, and the attack rate for children ranged from 0.97% to 28.18%. Communication between key stakeholders about outbreak confirmation, risk assessment, and surveillance should be improved. Effective communication facilitates timely notification (within 24 hours) of clustered outbreaks to a local Center for Disease Control and Prevention. Timely implementation of a control response is effective in minimising incidence and length of an outbreak in childcare facilities. The government should provide incentives for childcare facilities to train infection control specialists who can serve as the first contact, knowledge, and communication points, as well as facilitate exchange of information and provision of support across stakeholders during a communicable disease epidemic.

Hard exudates segmentation based on learned initial seeds and iterative graph cut.

PubMed

Kusakunniran, Worapan; Wu, Qiang; Ritthipravat, Panrasee; Zhang, Jian

2018-05-01

(Background and Objective): The occurrence of hard exudates is one of the early signs of diabetic retinopathy which is one of the leading causes of the blindness. Many patients with diabetic retinopathy lose their vision because of the late detection of the disease. Thus, this paper is to propose a novel method of hard exudates segmentation in retinal images in an automatic way. (Methods): The existing methods are based on either supervised or unsupervised learning techniques. In addition, the learned segmentation models may often cause miss-detection and/or fault-detection of hard exudates, due to the lack of rich characteristics, the intra-variations, and the similarity with other components in the retinal image. Thus, in this paper, the supervised learning based on the multilayer perceptron (MLP) is only used to identify initial seeds with high confidences to be hard exudates. Then, the segmentation is finalized by unsupervised learning based on the iterative graph cut (GC) using clusters of initial seeds. Also, in order to reduce color intra-variations of hard exudates in different retinal images, the color transfer (CT) is applied to normalize their color information, in the pre-processing step. (Results): The experiments and comparisons with the other existing methods are based on the two well-known datasets, e_ophtha EX and DIARETDB1. It can be seen that the proposed method outperforms the other existing methods in the literature, with the sensitivity in the pixel-level of 0.891 for the DIARETDB1 dataset and 0.564 for the e_ophtha EX dataset. The cross datasets validation where the training process is performed on one dataset and the testing process is performed on another dataset is also evaluated in this paper, in order to illustrate the robustness of the proposed method. (Conclusions): This newly proposed method integrates the supervised learning and unsupervised learning based techniques. It achieves the improved performance, when compared with the existing methods in the literature. The robustness of the proposed method for the scenario of cross datasets could enhance its practical usage. That is, the trained model could be more practical for unseen data in the real-world situation, especially when the capturing environments of training and testing images are not the same. Copyright © 2018 Elsevier B.V. All rights reserved.

Dynamics and ecological consequences of the 2013−2014 koa moth outbreak at Hakalau Forest National Wildlife Refuge.

USGS Publications Warehouse

Banko, Paul C.; Peck, Robert W.; Yelenik, Stephanie G.; Paxton, Eben H.; Bonaccorso, Frank J.; Montoya-Aiona, Kristina; Foote, David

2014-01-01

A massive outbreak of the koa moth (Geometridea: Scotorythra paludicola) defoliated more than a third of the koa (Acacia koa) forest on Hawai‘i Island during 2013−2014. This was the largest koa moth outbreak ever recorded and the first on the island since 1953. The outbreak spread to sites distributed widely around the island between 800−2,000 m elevation and in wet rainforest to dry woodland habitats. We monitored the outbreak at two windward forest sites (Laupāhoehoe and Saddle Road Kīpuka) and one leeward forest site (Kona), and we studied the dynamics of the outbreak and its impacts on the forest ecosystem at Hakalau Forest National Wildlife Refuge, our higher elevation windward site. Study sites at Hakalau included two stands of koa that were planted (reforestation stands) in former cattle pastureland about 20 years earlier and two stands of koa that were dominated by ‘ōhi‘a (Metrosideros polymorpha) and that were naturally recovering from cattle grazing (forest stands). We observed one outbreak at Hakalau, multiple outbreaks at the two other windward sites, but no outbreak at the leeward site. Caterpillars at Hakalau reached peak estimated abundances of more than 250,000 per tree and 18,000,000 per hectare, and they removed between 64−93% of the koa canopy in managed forest stands. Defoliation was more extensive in naturally recovering forest, where ‘ōhi‘a dominated and koa was less abundant, compared to the planted stands, where koa density was high. Koa trees were still growing new foliage six months after being defoliated, and leaves were produced in greater proportion to phyllodes, especially by small koa (≤ 8 cm dbh) and by larger trees in forest stands, where light levels may have remained relatively low after defoliation due to the high cover of ‘ōhi‘a. Small branches of many trees apparently died, and canopy regrowth was absent or low in 9% of koa trees and seedlings, which indicates the likely level of mortality. Between 2,000−5,000 kg/ha of frass fell during the defoliation event, resulting in the deposition of up to 200 kg/ha of highly labile nitrogen on the forest floor in less than two months. The deposition of nitrogen was detected as pulses in resin-available nitrogen in the top 5−10 cm of soil at two of three sites. These sites showed elevated soil nitrogen for about seven months. Nitrogen content of understory plant foliage, which is indicative of nitrogen uptake, suggested weak and variable effects of nitrogen deposition in the soil. Foliar nitrogen increased slightly in alien pasture grasses four months after the deposition of frass, although distinctive increases were not detected in native woody species. Birds responded to the abundance of caterpillars by increasing their activity in koa during the buildup of caterpillars and decreasing their use of koa after defoliation. During the outbreak, caterpillars increased in the diets of the two generalist insectivores we examined, and nearly all species gained weight. Bats responded to the abundance of moths by compression of active foraging into the first three hours of darkness each night after presumably having reached a digestive bottleneck. Reduced foraging activity by bats also resulted in lower indices of detectability based upon acoustic monitoring when compared to non-outbreak years. Parasitoid wasps tracked caterpillar abundance, but the low rate at which they attacked caterpillars suggests that they had little influence on the population. The predatory yellowjacket (Vespula pensylvanica) did not respond to the outbreak. Although a single, protracted outbreak occurred at Hakalau, multiple outbreaks and defoliations occurred at lower elevations. Our results provide a broad foundation for evaluating the dynamics and impacts of future Scotorythra outbreaks.

Impact of virus strain characteristics on early detection of highly pathogenic avian influenza infection in commercial table-egg layer flocks and implications for outbreak control.

PubMed

Weaver, J Todd; Malladi, Sasidhar; Goldsmith, Timothy J; Hueston, Will; Hennessey, Morgan; Lee, Brendan; Voss, Shauna; Funk, Janel; Der, Christina; Bjork, Kathe E; Clouse, Timothy L; Halvorson, David A

2012-12-01

Early detection of highly pathogenic avian influenza (HPAI) infection in commercial poultry flocks is a critical component of outbreak control. Reducing the time to detect HPAI infection can reduce the risk of disease transmission to other flocks. The timeliness of different types of detection triggers could be dependent on clinical signs that are first observed in a flock, signs that might vary due to HPAI virus strain characteristics. We developed a stochastic disease transmission model to evaluate how transmission characteristics of various HPAI strains might effect the relative importance of increased mortality, drop in egg production, or daily real-time reverse transcriptase (RRT)-PCR testing, toward detecting HPAI infection in a commercial table-egg layer flock. On average, daily RRT-PCR testing resulted in the shortest time to detection (from 3.5 to 6.1 days) depending on the HPAI virus strain and was less variable over a range of transmission parameters compared with other triggers evaluated. Our results indicate that a trigger to detect a drop in egg production would be useful for HPAI virus strains with long infectious periods (6-8 days) and including an egg-drop detection trigger in emergency response plans would lead to earlier and consistent reporting in some cases. We discuss implications for outbreak control and risk of HPAI spread attributed to different HPAI strain characteristics where an increase in mortality or a drop in egg production or both would be among the first clinical signs observed in an infected flock.

Data quality and timeliness of outbreak reporting system among countries in Greater Mekong subregion: Challenges for international data sharing

PubMed Central

Kaewkungwal, Jaranit; Khamsiriwatchara, Amnat; Sovann, Ly; Sreng, Bun; Phommasack, Bounlay; Kitthiphong, Viengsavanh; Lwin Nyein, Soe; Win Myint, Nyan; Dang Vung, Nguyen; Hung, Pham; S. Smolinski, Mark; W. Crawley, Adam; Ko Oo, Moe

2018-01-01

Cross-border disease transmission is a key challenge for prevention and control of outbreaks. Variation in surveillance structure and national guidelines used in different countries can affect their data quality and the timeliness of outbreak reports. This study aimed to evaluate timeliness and data quality of national outbreak reporting for four countries in the Mekong Basin Disease Surveillance network (MBDS). Data on disease outbreaks occurring from 2010 to 2015 were obtained from the national disease surveillance reports of Cambodia, Lao PDR, Myanmar, and Vietnam. Data included total cases, geographical information, and dates at different timeline milestones in the outbreak detection process. Nine diseases or syndromes with public health importance were selected for the analysis including: dengue, food poisoning & diarrhea, severe diarrhea, diphtheria, measles, H5N1 influenza, H1N1 influenza, rabies, and pertussis. Overall, 2,087 outbreaks were reported from the four countries. The number of outbreaks and number of cases per outbreak varied across countries and diseases, depending in part on the outbreak definition used in each country. Dates on index onset, report, and response were >95% complete in all countries, while laboratory confirmation dates were 10%-100% incomplete in most countries. Inconsistent and out of range date data were observed in 1%-5% of records. The overall timeliness of outbreak report, response, and public communication was within 1–15 days, depending on countries and diseases. Diarrhea and severe diarrhea outbreaks showed the most rapid time to report and response, whereas diseases such as rabies, pertussis and diphtheria required a longer time to report and respond. The hierarchical structure of the reporting system, data collection method, and country’s resources could affect the data quality and timeliness of the national outbreak reporting system. Differences in data quality and timeliness of outbreak reporting system among member countries should be considered when planning data sharing strategies within a regional network. PMID:29694372

Detection of Tree Crowns Based on Reclassification Using Aerial Images and LIDAR Data

NASA Astrophysics Data System (ADS)

Talebi, S.; Zarea, A.; Sadeghian, S.; Arefi, H.

2013-09-01

Tree detection using aerial sensors in early decades was focused by many researchers in different fields including Remote Sensing and Photogrammetry. This paper is intended to detect trees in complex city areas using aerial imagery and laser scanning data. Our methodology is a hierarchal unsupervised method consists of some primitive operations. This method could be divided into three sections, in which, first section uses aerial imagery and both second and third sections use laser scanners data. In the first section a vegetation cover mask is created in both sunny and shadowed areas. In the second section Rate of Slope Change (RSC) is used to eliminate grasses. In the third section a Digital Terrain Model (DTM) is obtained from LiDAR data. By using DTM and Digital Surface Model (DSM) we would get to Normalized Digital Surface Model (nDSM). Then objects which are lower than a specific height are eliminated. Now there are three result layers from three sections. At the end multiplication operation is used to get final result layer. This layer will be smoothed by morphological operations. The result layer is sent to WG III/4 to evaluate. The evaluation result shows that our method has a good rank in comparing to other participants' methods in ISPRS WG III/4, when assessed in terms of 5 indices including area base completeness, area base correctness, object base completeness, object base correctness and boundary RMS. With regarding of being unsupervised and automatic, this method is improvable and could be integrate with other methods to get best results.

Detection of notifiable diseases through surveillance for imported plague--New York, September-October 1994.

PubMed

1994-11-11

Recent reports of bubonic and pneumonic plague outbreaks in India (1,2) prompted the New York City Department of Health (NYCDOH) and the New York State Department of Health (NYSDOH), in conjunction with CDC, to develop an emergency response plan to detect and manage suspected cases imported by international air travel. This report describes the surveillance system implemented by CDC on September 27 and supplemental efforts by NYC/NYSDOH to guide and inform physicians about the outbreak, and summarizes clinical findings for 11 travelers who had symptoms suggestive of plague.

Separated Component-Based Restoration of Speckled SAR Images

DTIC Science & Technology

2013-01-01

unsupervised change detection from SAR amplitude imagery,” IEEE Trans. Geosci. Remote Sens., vol. 44, no. 10, pp. 2972–2982, Oct. 2006. [5] F. Argenti , T...Sens., vol. 40, no. 10, pp. 2196–2212, Oct. 2002. [13] F. Argenti and L. Alparone, “Speckle removal from SAR images in the undecimated wavelet domain...iterative thresh- olding algorithm for linear inverse problems with a sparsity con- straint,” Commun . Pure Appl. Math., vol. 57, no. 11, pp. 1413

Detection of Erroneous Payments Utilizing Supervised And Unsupervised Data Mining Techniques

DTIC Science & Technology

2004-09-01

will look at which statistical analysis technique will work best in developing and enhancing existing erroneous payment models . Chapter I and II... payment models that are used for selection of records to be audited. The models are set up such that if two or more records have the same payment...Identification Number, Invoice Number and Delivery Order Number are not compared. The DM0102 Duplicate Payment Model will be analyzed in this thesis

Effect of UV-A and UV-B irradiation on the metabolic profile of aqueous humor in rabbits analyzed by 1H NMR spectroscopy.

PubMed

Tessem, May-Britt; Bathen, Tone F; Cejková, Jitka; Midelfart, Anna

2005-03-01

This study was conducted to investigate metabolic changes in aqueous humor from rabbit eyes exposed to either UV-A or -B radiation, by using (1)H nuclear magnetic resonance (NMR) spectroscopy and unsupervised pattern recognition methods. Both eyes of adult albino rabbits were irradiated with UV-A (366 nm, 0.589 J/cm(2)) or UV-B (312 nm, 1.667 J/cm(2)) radiation for 8 minutes, once a day for 5 days. Three days after the last irradiation, samples of aqueous humor were aspirated, and the metabolic profiles analyzed with (1)H NMR spectroscopy. The metabolic concentrations in the exposed and control materials were statistically analyzed and compared, with multivariate methods and one-way ANOVA. UV-B radiation caused statistically significant alterations of betaine, glucose, ascorbate, valine, isoleucine, and formate in the rabbit aqueous humor. By using principal component analysis, the UV-B-irradiated samples were clearly separated from the UV-A-irradiated samples and the control group. No significant metabolic changes were detected in UV-A-irradiated samples. This study demonstrates the potential of using unsupervised pattern recognition methods to extract valuable metabolic information from complex (1)H NMR spectra. UV-B irradiation of rabbit eyes led to significant metabolic changes in the aqueous humor detected 3 days after the last exposure.

Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high-grade glioma.

PubMed

Kebir, Sied; Khurshid, Zain; Gaertner, Florian C; Essler, Markus; Hattingen, Elke; Fimmers, Rolf; Scheffler, Björn; Herrlinger, Ulrich; Bundschuh, Ralph A; Glas, Martin

2017-01-31

Timely detection of pseudoprogression (PSP) is crucial for the management of patients with high-grade glioma (HGG) but remains difficult. Textural features of O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography (FET-PET) mirror tumor uptake heterogeneity; some of them may be associated with tumor progression. Fourteen patients with HGG and suspected of PSP underwent FET-PET imaging. A set of 19 conventional and textural FET-PET features were evaluated and subjected to unsupervised consensus clustering. The final diagnosis of true progression vs. PSP was based on follow-up MRI using RANO criteria. Three robust clusters have been identified based on 10 predominantly textural FET-PET features. None of the patients with PSP fell into cluster 2, which was associated with high values for textural FET-PET markers of uptake heterogeneity. Three out of 4 patients with PSP were assigned to cluster 3 that was largely associated with low values of textural FET-PET features. By comparison, tumor-to-normal brain ratio (TNRmax) at the optimal cutoff 2.1 was less predictive of PSP (negative predictive value 57% for detecting true progression, p=0.07 vs. 75% with cluster 3, p=0.04). Clustering based on textural O-(2-[18F]fluoroethyl)-L-tyrosine PET features may provide valuable information in assessing the elusive phenomenon of pseudoprogression.

Pseudo-outbreak of Penicillium in an outpatient obstetrics and gynecology clinic.

PubMed

Sood, Geetika; Huber, Kerri; Dam, Lisa; Riedel, Stefan; Grubb, Lisa; Zenilman, Jonathan; Perl, Trish M; Argani, Cynthia

2017-05-01

We report an unusual pseudo-outbreak of Penicillium that occurred in patients seen in an outpatient obstetrics and gynecology clinic. The pseudo-outbreak was detected in late 2012, when the microbiology department reported a series of vaginal cultures positive for Penicillium spp. Our investigation found Penicillium spp in both patient and environmental samples and was potentially associated with the practice of wetting gloves with tap water by a health care worker prior to patient examination. Copyright © 2017 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Optimizing surveillance for livestock disease spreading through animal movements

PubMed Central

Bajardi, Paolo; Barrat, Alain; Savini, Lara; Colizza, Vittoria

2012-01-01

The spatial propagation of many livestock infectious diseases critically depends on the animal movements among premises; so the knowledge of movement data may help us to detect, manage and control an outbreak. The identification of robust spreading features of the system is however hampered by the temporal dimension characterizing population interactions through movements. Traditional centrality measures do not provide relevant information as results strongly fluctuate in time and outbreak properties heavily depend on geotemporal initial conditions. By focusing on the case study of cattle displacements in Italy, we aim at characterizing livestock epidemics in terms of robust features useful for planning and control, to deal with temporal fluctuations, sensitivity to initial conditions and missing information during an outbreak. Through spatial disease simulations, we detect spreading paths that are stable across different initial conditions, allowing the clustering of the seeds and reducing the epidemic variability. Paths also allow us to identify premises, called sentinels, having a large probability of being infected and providing critical information on the outbreak origin, as encoded in the clusters. This novel procedure provides a general framework that can be applied to specific diseases, for aiding risk assessment analysis and informing the design of optimal surveillance systems. PMID:22728387

Supervised versus unsupervised categorization: two sides of the same coin?

PubMed

Pothos, Emmanuel M; Edwards, Darren J; Perlman, Amotz

2011-09-01

Supervised and unsupervised categorization have been studied in separate research traditions. A handful of studies have attempted to explore a possible convergence between the two. The present research builds on these studies, by comparing the unsupervised categorization results of Pothos et al. ( 2011 ; Pothos et al., 2008 ) with the results from two procedures of supervised categorization. In two experiments, we tested 375 participants with nine different stimulus sets and examined the relation between ease of learning of a classification, memory for a classification, and spontaneous preference for a classification. After taking into account the role of the number of category labels (clusters) in supervised learning, we found the three variables to be closely associated with each other. Our results provide encouragement for researchers seeking unified theoretical explanations for supervised and unsupervised categorization, but raise a range of challenging theoretical questions.

Surveillance for Waterborne Disease Outbreaks Associated with Drinking Water - United States, 2013-2014.

PubMed

Benedict, Katharine M; Reses, Hannah; Vigar, Marissa; Roth, David M; Roberts, Virginia A; Mattioli, Mia; Cooley, Laura A; Hilborn, Elizabeth D; Wade, Timothy J; Fullerton, Kathleen E; Yoder, Jonathan S; Hill, Vincent R

2017-11-10

Provision of safe water in the United States is vital to protecting public health (1). Public health agencies in the U.S. states and territories* report information on waterborne disease outbreaks to CDC through the National Outbreak Reporting System (NORS) (https://www.cdc.gov/healthywater/surveillance/index.html). During 2013-2014, 42 drinking water-associated † outbreaks were reported, accounting for at least 1,006 cases of illness, 124 hospitalizations, and 13 deaths. Legionella was associated with 57% of these outbreaks and all of the deaths. Sixty-nine percent of the reported illnesses occurred in four outbreaks in which the etiology was determined to be either a chemical or toxin or the parasite Cryptosporidium. Drinking water contamination events can cause disruptions in water service, large impacts on public health, and persistent community concern about drinking water quality. Effective water treatment and regulations can protect public drinking water supplies in the United States, and rapid detection, identification of the cause, and response to illness reports can reduce the transmission of infectious pathogens and harmful chemicals and toxins.

Outbreak of Salmonella Oslo Infections Linked to Persian Cucumbers - United States, 2016.

PubMed

Bottichio, Lyndsay; Medus, Carlota; Sorenson, Alida; Donovan, Danielle; Sharma, Reeti; Dowell, Natasha; Williams, Ian; Wellman, Allison; Jackson, Alikeh; Tolar, Beth; Griswold, Taylor; Basler, Colin

2016-12-30

In April 2016, PulseNet, the national molecular subtyping network for foodborne disease surveillance, detected a multistate cluster of Salmonella enterica serotype Oslo infections with an indistinguishable pulsed-field gel electrophoresis (PFGE) pattern (XbaI PFGE pattern OSLX01.0090).* This PFGE pattern was new in the database; no previous infections or outbreaks have been identified. CDC, state and local health and agriculture departments and laboratories, and the Food and Drug Administration (FDA) conducted epidemiologic, traceback, and laboratory investigations to identify the source of this outbreak. A total of 14 patients in eight states were identified, with illness onsets occurring during March 21-April 9, 2016. Whole genome sequencing, a highly discriminating subtyping method, was used to further characterize PFGE pattern OSLX01.0090 isolates. Epidemiologic evidence indicates Persian cucumbers as the source of Salmonella Oslo infections in this outbreak. This is the fourth identified multistate outbreak of salmonellosis associated with cucumbers since 2013. Further research is needed to understand the mechanism and factors that contribute to contamination of cucumbers during growth, harvesting, and processing to prevent future outbreaks.

A susceptible-infected model of early detection of respiratory infection outbreaks on a background of influenza

PubMed Central

Mohtashemi, Mojdeh; Szolovits, Peter; Dunyak, James; Mandl, Kenneth D.

2013-01-01

The threat of biological warfare and the emergence of new infectious agents spreading at a global scale have highlighted the need for major enhancements to the public health infrastructure. Early detection of epidemics of infectious diseases requires both real-time data and real-time interpretation of data. Despite moderate advancements in data acquisition, the state of the practice for real-time analysis of data remains inadequate. We present a nonlinear mathematical framework for modeling the transient dynamics of influenza, applied to historical data sets of patients with influenza-like illness. We estimate the vital time-varying epidemiological parameters of infections from historical data, representing normal epidemiological trends. We then introduce simulated outbreaks of different shapes and magnitudes into the historical data, and estimate the parameters representing the infection rates of anomalous deviations from normal trends. Finally, a dynamic threshold-based detection algorithm is devised to assess the timeliness and sensitivity of detecting the irregularities in the data, under a fixed low false-positive rate. We find that the detection algorithm can identify such designated abnormalities in the data with high sensitivity with specificity held at 97%, but more importantly, early during an outbreak. The proposed methodology can be applied to a broad range of influenza-like infectious diseases, whether naturally occurring or a result of bioterrorism, and thus can be an integral component of a real-time surveillance system. PMID:16556450

Label-free detection of salmonella typhimurium with ssDNA aptamers

USDA-ARS?s Scientific Manuscript database

Foodborne pathogen Salmonella enterica is one of the major causes of gastrointestinal infections in human and animals. Conventional detection methods are time consuming and not effective enough under emergency circumstances to control outbreaks immediately. Therefore, biosensors that can detect Salm...

Detection of Staphylococcal Enterotoxin in Food

PubMed Central

Casman, Ezra P.; Bennett, Reginald W.

1965-01-01

Methods are described for the extraction and serological detection of trace amounts of enterotoxins A and B in foods incriminated in outbreaks of staphylococcal food poisoning. Evidence is presented for the probable applicability of the methods for the detection of unidentified enterotoxins. PMID:14325876