Bayesian networks improve causal environmental ...

EPA Pesticide Factsheets

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value

Bayesian Networks Improve Causal Environmental Assessments for Evidence-Based Policy.

PubMed

Carriger, John F; Barron, Mace G; Newman, Michael C

2016-12-20

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on valued ecological resources. These aspects are demonstrated through hypothetical problem scenarios that explore some major benefits of using Bayesian networks for reasoning and making inferences in evidence-based policy.

Origin of microbial biomineralization and magnetotaxis during the Archean.

PubMed

Lin, Wei; Paterson, Greig A; Zhu, Qiyun; Wang, Yinzhao; Kopylova, Evguenia; Li, Ying; Knight, Rob; Bazylinski, Dennis A; Zhu, Rixiang; Kirschvink, Joseph L; Pan, Yongxin

2017-02-28

Microbes that synthesize minerals, a process known as microbial biomineralization, contributed substantially to the evolution of current planetary environments through numerous important geochemical processes. Despite its geological significance, the origin and evolution of microbial biomineralization remain poorly understood. Through combined metagenomic and phylogenetic analyses of deep-branching magnetotactic bacteria from the Nitrospirae phylum, and using a Bayesian molecular clock-dating method, we show here that the gene cluster responsible for biomineralization of magnetosomes, and the arrangement of magnetosome chain(s) within cells, both originated before or near the Archean divergence between the Nitrospirae and Proteobacteria This phylogenetic divergence occurred well before the Great Oxygenation Event. Magnetotaxis likely evolved due to environmental pressures conferring an evolutionary advantage to navigation via the geomagnetic field. Earth's dynamo must therefore have been sufficiently strong to sustain microbial magnetotaxis in the Archean, suggesting that magnetotaxis coevolved with the geodynamo over geological time.

Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula

NASA Astrophysics Data System (ADS)

Sarhadi, Ali; Burn, Donald H.; Concepción Ausín, María.; Wiper, Michael P.

2016-03-01

A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments. The results demonstrate that the nature and the risk of extreme-climate multidimensional processes are changed over time under the impact of climate change, and accordingly the long-term decision making strategies should be updated based on the anomalies of the nonstationary environment.

Combining QMRA and Epidemiology to Estimate Campylobacteriosis Incidence.

PubMed

Evers, Eric G; Bouwknegt, Martijn

2016-10-01

The disease burden of pathogens as estimated by QMRA (quantitative microbial risk assessment) and EA (epidemiological analysis) often differs considerably. This is an unsatisfactory situation for policymakers and scientists. We explored methods to obtain a unified estimate using campylobacteriosis in the Netherlands as an example, where previous work resulted in estimates of 4.9 million (QMRA) and 90,600 (EA) cases per year. Using the maximum likelihood approach and considering EA the gold standard, the QMRA model could produce the original EA estimate by adjusting mainly the dose-infection relationship. Considering QMRA the gold standard, the EA model could produce the original QMRA estimate by adjusting mainly the probability that a gastroenteritis case is caused by Campylobacter. A joint analysis of QMRA and EA data and models assuming identical outcomes, using a frequentist or Bayesian approach (using vague priors), resulted in estimates of 102,000 or 123,000 campylobacteriosis cases per year, respectively. These were close to the original EA estimate, and this will be related to the dissimilarity in data availability. The Bayesian approach further showed that attenuating the condition of equal outcomes immediately resulted in very different estimates of the number of campylobacteriosis cases per year and that using more informative priors had little effect on the results. In conclusion, EA was dominant in estimating the burden of campylobacteriosis in the Netherlands. However, it must be noted that only statistical uncertainties were taken into account here. Taking all, usually difficult to quantify, uncertainties into account might lead to a different conclusion. © 2016 Society for Risk Analysis.

BiomeNet: A Bayesian Model for Inference of Metabolic Divergence among Microbial Communities

PubMed Central

Chipman, Hugh; Gu, Hong; Bielawski, Joseph P.

2014-01-01

Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic subnetworks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems). The metabosystems are composed of mixtures of tightly connected metabolic subnetworks. BiomeNet differs from other unsupervised methods by allowing researchers to discriminate groups of samples through the metabolic patterns it discovers in the data, and by providing a framework for interpreting them. We describe a collapsed Gibbs sampler for inference of the mixture weights under BiomeNet, and we use simulation to validate the inference algorithm. Application of BiomeNet to human gut metagenomes revealed a metabosystem with greater prevalence among inflammatory bowel disease (IBD) patients. Based on the discriminatory subnetworks for this metabosystem, we inferred that the community is likely to be closely associated with the human gut epithelium, resistant to dietary interventions, and interfere with human uptake of an antioxidant connected to IBD. Because this metabosystem has a greater capacity to exploit host-associated glycans, we speculate that IBD-associated communities might arise from opportunist growth of bacteria that can circumvent the host's nutrient-based mechanism for bacterial partner selection. PMID:25412107

Methods for fitting a parametric probability distribution to most probable number data.

PubMed

Williams, Michael S; Ebel, Eric D

2012-07-02

Every year hundreds of thousands, if not millions, of samples are collected and analyzed to assess microbial contamination in food and water. The concentration of pathogenic organisms at the end of the production process is low for most commodities, so a highly sensitive screening test is used to determine whether the organism of interest is present in a sample. In some applications, samples that test positive are subjected to quantitation. The most probable number (MPN) technique is a common method to quantify the level of contamination in a sample because it is able to provide estimates at low concentrations. This technique uses a series of dilution count experiments to derive estimates of the concentration of the microorganism of interest. An application for these data is food-safety risk assessment, where the MPN concentration estimates can be fitted to a parametric distribution to summarize the range of potential exposures to the contaminant. Many different methods (e.g., substitution methods, maximum likelihood and regression on order statistics) have been proposed to fit microbial contamination data to a distribution, but the development of these methods rarely considers how the MPN technique influences the choice of distribution function and fitting method. An often overlooked aspect when applying these methods is whether the data represent actual measurements of the average concentration of microorganism per milliliter or the data are real-valued estimates of the average concentration, as is the case with MPN data. In this study, we propose two methods for fitting MPN data to a probability distribution. The first method uses a maximum likelihood estimator that takes average concentration values as the data inputs. The second is a Bayesian latent variable method that uses the counts of the number of positive tubes at each dilution to estimate the parameters of the contamination distribution. The performance of the two fitting methods is compared for two data sets that represent Salmonella and Campylobacter concentrations on chicken carcasses. The results demonstrate a bias in the maximum likelihood estimator that increases with reductions in average concentration. The Bayesian method provided unbiased estimates of the concentration distribution parameters for all data sets. We provide computer code for the Bayesian fitting method. Published by Elsevier B.V.

Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model.

PubMed

Sampid, Marius Galabe; Hasim, Haslifah M; Dai, Hongsheng

2018-01-01

In this paper, we propose a model for forecasting Value-at-Risk (VaR) using a Bayesian Markov-switching GJR-GARCH(1,1) model with skewed Student's-t innovation, copula functions and extreme value theory. A Bayesian Markov-switching GJR-GARCH(1,1) model that identifies non-constant volatility over time and allows the GARCH parameters to vary over time following a Markov process, is combined with copula functions and EVT to formulate the Bayesian Markov-switching GJR-GARCH(1,1) copula-EVT VaR model, which is then used to forecast the level of risk on financial asset returns. We further propose a new method for threshold selection in EVT analysis, which we term the hybrid method. Empirical and back-testing results show that the proposed VaR models capture VaR reasonably well in periods of calm and in periods of crisis.

Comparing models for quantitative risk assessment: an application to the European Registry of foreign body injuries in children.

PubMed

Berchialla, Paola; Scarinzi, Cecilia; Snidero, Silvia; Gregori, Dario

2016-08-01

Risk Assessment is the systematic study of decisions subject to uncertain consequences. An increasing interest has been focused on modeling techniques like Bayesian Networks since their capability of (1) combining in the probabilistic framework different type of evidence including both expert judgments and objective data; (2) overturning previous beliefs in the light of the new information being received and (3) making predictions even with incomplete data. In this work, we proposed a comparison among Bayesian Networks and other classical Quantitative Risk Assessment techniques such as Neural Networks, Classification Trees, Random Forests and Logistic Regression models. Hybrid approaches, combining both Classification Trees and Bayesian Networks, were also considered. Among Bayesian Networks, a clear distinction between purely data-driven approach and combination of expert knowledge with objective data is made. The aim of this paper consists in evaluating among this models which best can be applied, in the framework of Quantitative Risk Assessment, to assess the safety of children who are exposed to the risk of inhalation/insertion/aspiration of consumer products. The issue of preventing injuries in children is of paramount importance, in particular where product design is involved: quantifying the risk associated to product characteristics can be of great usefulness in addressing the product safety design regulation. Data of the European Registry of Foreign Bodies Injuries formed the starting evidence for risk assessment. Results showed that Bayesian Networks appeared to have both the ease of interpretability and accuracy in making prediction, even if simpler models like logistic regression still performed well. © The Author(s) 2013.

Bayesian Monte Carlo and Maximum Likelihood Approach for Uncertainty Estimation and Risk Management: Application to Lake Oxygen Recovery Model

EPA Science Inventory

Model uncertainty estimation and risk assessment is essential to environmental management and informed decision making on pollution mitigation strategies. In this study, we apply a probabilistic methodology, which combines Bayesian Monte Carlo simulation and Maximum Likelihood e...

A Generalized QMRA Beta-Poisson Dose-Response Model.

PubMed

Xie, Gang; Roiko, Anne; Stratton, Helen; Lemckert, Charles; Dunn, Peter K; Mengersen, Kerrie

2016-10-01

Quantitative microbial risk assessment (QMRA) is widely accepted for characterizing the microbial risks associated with food, water, and wastewater. Single-hit dose-response models are the most commonly used dose-response models in QMRA. Denoting PI(d) as the probability of infection at a given mean dose d, a three-parameter generalized QMRA beta-Poisson dose-response model, PI(d|α,β,r*), is proposed in which the minimum number of organisms required for causing infection, K min , is not fixed, but a random variable following a geometric distribution with parameter 0

Research on factors allowing a risk assessment of spore-forming pathogenic bacteria in cooked chilled foods containing vegetables: a FAIR collaborative project.

PubMed

Carlin, F; Girardin, H; Peck, M W; Stringer, S C; Barker, G C; Martinez, A; Fernandez, A; Fernandez, P; Waites, W M; Movahedi, S; van Leusden, F; Nauta, M; Moezelaar, R; Torre, M D; Litman, S

2000-09-25

Vegetables are frequent ingredients of cooked chilled foods and are frequently contaminated with spore-forming bacteria (SFB). Therefore, risk assessment studies have been carried out, including the following: hazard identification and characterisation--from an extensive literature review and expertise of the participants, B. cereus and C. botulinum were identified as the main hazards; exposure assessment--consisting of determination of the prevalence of hazardous SFB in cooked chilled foods containing vegetables and in unprocessed vegetables, and identification of SFB representative of the bacterial community in cooked chilled foods containing vegetables, determination of heat-resistance parameters and factors affecting heat resistance of SFB, determination of the growth kinetics of SFB in vegetable substrate and of the influence of controlling factors, validation of previous work in complex food systems and by challenge testing and information about process and storage conditions of cooked chilled foods containing vegetables. The paper illustrates some original results obtained in the course of the project. The results and information collected from scientific literature or from the expertise of the participants are integrated into the microbial risk assessment, using both a Bayesian belief network approach and a process risk model approach, previously applied to other foodborne hazards.

Inflammatory bowel diseases phenotype, C. difficile and NOD2 genotype are associated with shifts in human ileum associated microbial composition.

PubMed

Li, Ellen; Hamm, Christina M; Gulati, Ajay S; Sartor, R Balfour; Chen, Hongyan; Wu, Xiao; Zhang, Tianyi; Rohlf, F James; Zhu, Wei; Gu, Chi; Robertson, Charles E; Pace, Norman R; Boedeker, Edgar C; Harpaz, Noam; Yuan, Jeffrey; Weinstock, George M; Sodergren, Erica; Frank, Daniel N

2012-01-01

We tested the hypothesis that Crohn's disease (CD)-related genetic polymorphisms involved in host innate immunity are associated with shifts in human ileum-associated microbial composition in a cross-sectional analysis of human ileal samples. Sanger sequencing of the bacterial 16S ribosomal RNA (rRNA) gene and 454 sequencing of 16S rRNA gene hypervariable regions (V1-V3 and V3-V5), were conducted on macroscopically disease-unaffected ileal biopsies collected from 52 ileal CD, 58 ulcerative colitis and 60 control patients without inflammatory bowel diseases (IBD) undergoing initial surgical resection. These subjects also were genotyped for the three major NOD2 risk alleles (Leu1007fs, R708W, G908R) and the ATG16L1 risk allele (T300A). The samples were linked to clinical metadata, including body mass index, smoking status and Clostridia difficile infection. The sequences were classified into seven phyla/subphyla categories using the Naïve Bayesian Classifier of the Ribosome Database Project. Centered log ratio transformation of six predominant categories was included as the dependent variable in the permutation based MANCOVA for the overall composition with stepwise variable selection. Polymerase chain reaction (PCR) assays were conducted to measure the relative frequencies of the Clostridium coccoides - Eubacterium rectales group and the Faecalibacterium prausnitzii spp. Empiric logit transformations of the relative frequencies of these two microbial groups were included in permutation-based ANCOVA. Regardless of sequencing method, IBD phenotype, Clostridia difficile and NOD2 genotype were selected as associated (FDR ≤ 0.05) with shifts in overall microbial composition. IBD phenotype and NOD2 genotype were also selected as associated with shifts in the relative frequency of the C. coccoides--E. rectales group. IBD phenotype, smoking and IBD medications were selected as associated with shifts in the relative frequency of F. prausnitzii spp. These results indicate that the effects of genetic and environmental factors on IBD are mediated at least in part by the enteric microbiota.

Inflammatory Bowel Diseases Phenotype, C. difficile and NOD2 Genotype Are Associated with Shifts in Human Ileum Associated Microbial Composition

PubMed Central

Li, Ellen; Hamm, Christina M.; Gulati, Ajay S.; Sartor, R. Balfour; Chen, Hongyan; Wu, Xiao; Zhang, Tianyi; Rohlf, F. James; Zhu, Wei; Gu, Chi; Robertson, Charles E.; Pace, Norman R.; Boedeker, Edgar C.; Harpaz, Noam; Yuan, Jeffrey; Weinstock, George M.; Sodergren, Erica; Frank, Daniel N.

2012-01-01

We tested the hypothesis that Crohn’s disease (CD)-related genetic polymorphisms involved in host innate immunity are associated with shifts in human ileum–associated microbial composition in a cross-sectional analysis of human ileal samples. Sanger sequencing of the bacterial 16S ribosomal RNA (rRNA) gene and 454 sequencing of 16S rRNA gene hypervariable regions (V1–V3 and V3–V5), were conducted on macroscopically disease-unaffected ileal biopsies collected from 52 ileal CD, 58 ulcerative colitis and 60 control patients without inflammatory bowel diseases (IBD) undergoing initial surgical resection. These subjects also were genotyped for the three major NOD2 risk alleles (Leu1007fs, R708W, G908R) and the ATG16L1 risk allele (T300A). The samples were linked to clinical metadata, including body mass index, smoking status and Clostridia difficile infection. The sequences were classified into seven phyla/subphyla categories using the Naïve Bayesian Classifier of the Ribosome Database Project. Centered log ratio transformation of six predominant categories was included as the dependent variable in the permutation based MANCOVA for the overall composition with stepwise variable selection. Polymerase chain reaction (PCR) assays were conducted to measure the relative frequencies of the Clostridium coccoides – Eubacterium rectales group and the Faecalibacterium prausnitzii spp. Empiric logit transformations of the relative frequencies of these two microbial groups were included in permutation-based ANCOVA. Regardless of sequencing method, IBD phenotype, Clostridia difficile and NOD2 genotype were selected as associated (FDR ≤0.05) with shifts in overall microbial composition. IBD phenotype and NOD2 genotype were also selected as associated with shifts in the relative frequency of the C. coccoides – E. rectales group. IBD phenotype, smoking and IBD medications were selected as associated with shifts in the relative frequency of F. prausnitzii spp. These results indicate that the effects of genetic and environmental factors on IBD are mediated at least in part by the enteric microbiota. PMID:22719818

Application of a predictive Bayesian model to environmental accounting.

PubMed

Anex, R P; Englehardt, J D

2001-03-30

Environmental accounting techniques are intended to capture important environmental costs and benefits that are often overlooked in standard accounting practices. Environmental accounting methods themselves often ignore or inadequately represent large but highly uncertain environmental costs and costs conditioned by specific prior events. Use of a predictive Bayesian model is demonstrated for the assessment of such highly uncertain environmental and contingent costs. The predictive Bayesian approach presented generates probability distributions for the quantity of interest (rather than parameters thereof). A spreadsheet implementation of a previously proposed predictive Bayesian model, extended to represent contingent costs, is described and used to evaluate whether a firm should undertake an accelerated phase-out of its PCB containing transformers. Variability and uncertainty (due to lack of information) in transformer accident frequency and severity are assessed simultaneously using a combination of historical accident data, engineering model-based cost estimates, and subjective judgement. Model results are compared using several different risk measures. Use of the model for incorporation of environmental risk management into a company's overall risk management strategy is discussed.

Diversification dynamics of rhynchostomatian ciliates: the impact of seven intrinsic traits on speciation and extinction in a microbial group.

PubMed

Vďačný, Peter; Rajter, Ľubomír; Shazib, Shahed Uddin Ahmed; Jang, Seok Won; Shin, Mann Kyoon

2017-08-30

Ciliates are a suitable microbial model to investigate trait-dependent diversification because of their comparatively complex morphology and high diversity. We examined the impact of seven intrinsic traits on speciation, extinction, and net-diversification of rhynchostomatians, a group of comparatively large, predatory ciliates with proboscis carrying a dorsal brush (sensoric structure) and toxicysts (organelles used to kill the prey). Bayesian estimates under the binary-state speciation and extinction model indicate that two types of extrusomes and two-rowed dorsal brush raise diversification through decreasing extinction. On the other hand, the higher number of contractile vacuoles and their dorsal location likely increase diversification via elevating speciation rate. Particular nuclear characteristics, however, do not significantly differ in their diversification rates and hence lineages with various macronuclear patterns and number of micronuclei have similar probabilities to generate new species. Likelihood-based quantitative state diversification analyses suggest that rhynchostomatians conform to Cope's rule in that their diversity linearly grows with increasing body length and relative length of the proboscis. Comparison with other litostomatean ciliates indicates that rhynchostomatians are not among the cladogenically most successful lineages and their survival over several hundred million years could be associated with their comparatively large and complex bodies that reduce the risk of extinction.

A Bayesian network model to assess the public health risk associated with wet weather sewer overflows discharging into waterways.

PubMed

Goulding, R; Jayasuriya, N; Horan, E

2012-10-15

Overflows from sanitary sewers during wet weather, which occur when the hydraulic capacity of the sewer system is exceeded, are considered a potential threat to the ecological and public health of the waterways which receive these overflows. As a result, water retailers in Australia and internationally commit significant resources to manage and abate sewer overflows. However, whilst some studies have contributed to an increased understanding of the impacts and risks associated with these events, they are relatively few in number and there still is a general lack of knowledge in this area. A Bayesian network model to assess the public health risk associated with wet weather sewer overflows is presented in this paper. The Bayesian network approach is shown to provide significant benefits in the assessment of public health risks associated with wet weather sewer overflows. In particular, the ability for the model to account for the uncertainty inherent in sewer overflow events and subsequent impacts through the use of probabilities is a valuable function. In addition, the paper highlights the benefits of the probabilistic inference function of the Bayesian network in prioritising management options to minimise public health risks associated with sewer overflows. Copyright © 2012. Published by Elsevier Ltd.

Capturing changes in flood risk with Bayesian approaches for flood damage assessment

NASA Astrophysics Data System (ADS)

Vogel, Kristin; Schröter, Kai; Kreibich, Heidi; Thieken, Annegret; Müller, Meike; Sieg, Tobias; Laudan, Jonas; Kienzler, Sarah; Weise, Laura; Merz, Bruno; Scherbaum, Frank

2016-04-01

Flood risk is a function of hazard as well as of exposure and vulnerability. All three components are under change over space and time and have to be considered for reliable damage estimations and risk analyses, since this is the basis for an efficient, adaptable risk management. Hitherto, models for estimating flood damage are comparatively simple and cannot sufficiently account for changing conditions. The Bayesian network approach allows for a multivariate modeling of complex systems without relying on expert knowledge about physical constraints. In a Bayesian network each model component is considered to be a random variable. The way of interactions between those variables can be learned from observations or be defined by expert knowledge. Even a combination of both is possible. Moreover, the probabilistic framework captures uncertainties related to the prediction and provides a probability distribution for the damage instead of a point estimate. The graphical representation of Bayesian networks helps to study the change of probabilities for changing circumstances and may thus simplify the communication between scientists and public authorities. In the framework of the DFG-Research Training Group "NatRiskChange" we aim to develop Bayesian networks for flood damage and vulnerability assessments of residential buildings and companies under changing conditions. A Bayesian network learned from data, collected over the last 15 years in flooded regions in the Elbe and Danube catchments (Germany), reveals the impact of many variables like building characteristics, precaution and warning situation on flood damage to residential buildings. While the handling of incomplete and hybrid (discrete mixed with continuous) data are the most challenging issues in the study on residential buildings, a similar study, that focuses on the vulnerability of small to medium sized companies, bears new challenges. Relying on a much smaller data set for the determination of the model parameters, overly complex models should be avoided. A so called Markov Blanket approach aims at the identification of the most relevant factors and constructs a Bayesian network based on those findings. With our approach we want to exploit a major advantage of Bayesian networks which is their ability to consider dependencies not only pairwise, but to capture the joint effects and interactions of driving forces. Hence, the flood damage network does not only show the impact of precaution on the building damage separately, but also reveals the mutual effects of precaution and the quality of warning for a variety of flood settings. Thus, it allows for a consideration of changing conditions and different courses of action and forms a novel and valuable tool for decision support. This study is funded by the Deutsche Forschungsgemeinschaft (DFG) within the research training program GRK 2043/1 "NatRiskChange - Natural hazards and risks in a changing world" at the University of Potsdam.

Assessing the differences in public health impact of salmonella subtypes using a bayesian microbial subtyping approach for source attribution.

PubMed

Pires, Sara M; Hald, Tine

2010-02-01

Salmonella is a major cause of human gastroenteritis worldwide. To prioritize interventions and assess the effectiveness of efforts to reduce illness, it is important to attribute salmonellosis to the responsible sources. Studies have suggested that some Salmonella subtypes have a higher health impact than others. Likewise, some food sources appear to have a higher impact than others. Knowledge of variability in the impact of subtypes and sources may provide valuable added information for research, risk management, and public health strategies. We developed a Bayesian model that attributes illness to specific sources and allows for a better estimation of the differences in the ability of Salmonella subtypes and food types to result in reported salmonellosis. The model accommodates data for multiple years and is based on the Danish Salmonella surveillance. The number of sporadic cases caused by different Salmonella subtypes is estimated as a function of the prevalence of these subtypes in the animal-food sources, the amount of food consumed, subtype-related factors, and source-related factors. Our results showed relative differences between Salmonella subtypes in their ability to cause disease. These differences presumably represent multiple factors, such as differences in survivability through the food chain and/or pathogenicity. The relative importance of the source-dependent factors varied considerably over the years, reflecting, among others, variability in the surveillance programs for the different animal sources. The presented model requires estimation of fewer parameters than a previously developed model, and thus allows for a better estimation of these factors to result in reported human disease. In addition, a comparison of the results of the same model using different sets of typing data revealed that the model can be applied to data with less discriminatory power, which is the only data available in many countries. In conclusion, the model allows for the estimation of relative differences between Salmonella subtypes and sources, providing results that will benefit future risk assessment or risk ranking purposes.

Bayesian structured additive regression modeling of epidemic data: application to cholera

PubMed Central

2012-01-01

Background A significant interest in spatial epidemiology lies in identifying associated risk factors which enhances the risk of infection. Most studies, however, make no, or limited use of the spatial structure of the data, as well as possible nonlinear effects of the risk factors. Methods We develop a Bayesian Structured Additive Regression model for cholera epidemic data. Model estimation and inference is based on fully Bayesian approach via Markov Chain Monte Carlo (MCMC) simulations. The model is applied to cholera epidemic data in the Kumasi Metropolis, Ghana. Proximity to refuse dumps, density of refuse dumps, and proximity to potential cholera reservoirs were modeled as continuous functions; presence of slum settlers and population density were modeled as fixed effects, whereas spatial references to the communities were modeled as structured and unstructured spatial effects. Results We observe that the risk of cholera is associated with slum settlements and high population density. The risk of cholera is equal and lower for communities with fewer refuse dumps, but variable and higher for communities with more refuse dumps. The risk is also lower for communities distant from refuse dumps and potential cholera reservoirs. The results also indicate distinct spatial variation in the risk of cholera infection. Conclusion The study highlights the usefulness of Bayesian semi-parametric regression model analyzing public health data. These findings could serve as novel information to help health planners and policy makers in making effective decisions to control or prevent cholera epidemics. PMID:22866662

Research on Risk Manage of Power Construction Project Based on Bayesian Network

NASA Astrophysics Data System (ADS)

Jia, Zhengyuan; Fan, Zhou; Li, Yong

With China's changing economic structure and increasingly fierce competition in the market, the uncertainty and risk factors in the projects of electric power construction are increasingly complex, the projects will face huge risks or even fail if we don't consider or ignore these risk factors. Therefore, risk management in the projects of electric power construction plays an important role. The paper emphatically elaborated the influence of cost risk in electric power projects through study overall risk management and the behavior of individual in risk management, and introduced the Bayesian network to the project risk management. The paper obtained the order of key factors according to both scene analysis and causal analysis for effective risk management.

Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks.

PubMed

Zhang, Jinfen; Teixeira, Ângelo P; Guedes Soares, C; Yan, Xinping; Liu, Kezhong

2016-06-01

This article develops a Bayesian belief network model for the prediction of accident consequences in the Tianjin port. The study starts with a statistical analysis of historical accident data of six years from 2008 to 2013. Then a Bayesian belief network is constructed to express the dependencies between the indicator variables and accident consequences. The statistics and expert knowledge are synthesized in the Bayesian belief network model to obtain the probability distribution of the consequences. By a sensitivity analysis, several indicator variables that have influence on the consequences are identified, including navigational area, ship type and time of the day. The results indicate that the consequences are most sensitive to the position where the accidents occurred, followed by time of day and ship length. The results also reflect that the navigational risk of the Tianjin port is at the acceptable level, despite that there is more room of improvement. These results can be used by the Maritime Safety Administration to take effective measures to enhance maritime safety in the Tianjin port. © 2016 Society for Risk Analysis.

Assessment of sources of human pathogens and fecal contamination in a Florida freshwater lake.

PubMed

Staley, Christopher; Reckhow, Kenneth H; Lukasik, Jerzy; Harwood, Valerie J

2012-11-01

We investigated the potential for a variety of environmental reservoirs to harbor or contribute fecal indicator bacteria (FIB), DNA markers of human fecal contamination, and human pathogens to a freshwater lake. We hypothesized that submerged aquatic vegetation (SAV), sediments, and stormwater act as reservoirs and/or provide inputs of FIB and human pathogens to this inland water. Analysis included microbial source tracking (MST) markers of sewage contamination (Enterococcus faecium esp gene, human-associated Bacteroides HF183, and human polyomaviruses), pathogens (Salmonella, Cryptosporidium, Giardia, and enteric viruses), and FIB (fecal coliforms, Escherichia coli, and enterococci). Bayesian analysis was used to assess relationships among microbial and physicochemical variables. FIB in the water were correlated with concentrations in SAV and sediment. Furthermore, the correlation of antecedent rainfall and major rain events with FIB concentrations and detection of human markers and pathogens points toward multiple reservoirs for microbial contaminants in this system. Although pathogens and human-source markers were detected in 55% and 21% of samples, respectively, markers rarely coincided with pathogen detection. Bayesian analysis revealed that low concentrations (<45 CFU × 100 ml(-1)) of fecal coliforms were associated with 93% probability that pathogens would not be detected; furthermore the Bayes net model showed associations between elevated temperature and rainfall with fecal coliform and enterococci concentrations, but not E. coli. These data indicate that many under-studied matrices (e.g. SAV, sediment, stormwater) are important reservoirs for FIB and potentially human pathogens and demonstrate the usefulness of Bayes net analysis for water quality assessment. Copyright © 2012 Elsevier Ltd. All rights reserved.

Time-varying Concurrent Risk of Extreme Droughts and Heatwaves in California

NASA Astrophysics Data System (ADS)

Sarhadi, A.; Diffenbaugh, N. S.; Ausin, M. C.

2016-12-01

Anthropogenic global warming has changed the nature and the risk of extreme climate phenomena such as droughts and heatwaves. The concurrent of these nature-changing climatic extremes may result in intensifying undesirable consequences in terms of human health and destructive effects in water resources. The present study assesses the risk of concurrent extreme droughts and heatwaves under dynamic nonstationary conditions arising from climate change in California. For doing so, a generalized fully Bayesian time-varying multivariate risk framework is proposed evolving through time under dynamic human-induced environment. In this methodology, an extreme, Bayesian, dynamic copula (Gumbel) is developed to model the time-varying dependence structure between the two different climate extremes. The time-varying extreme marginals are previously modeled using a Generalized Extreme Value (GEV) distribution. Bayesian Markov Chain Monte Carlo (MCMC) inference is integrated to estimate parameters of the nonstationary marginals and copula using a Gibbs sampling method. Modelled marginals and copula are then used to develop a fully Bayesian, time-varying joint return period concept for the estimation of concurrent risk. Here we argue that climate change has increased the chance of concurrent droughts and heatwaves over decades in California. It is also demonstrated that a time-varying multivariate perspective should be incorporated to assess realistic concurrent risk of the extremes for water resources planning and management in a changing climate in this area. The proposed generalized methodology can be applied for other stochastic nature-changing compound climate extremes that are under the influence of climate change.

[Reliability theory based on quality risk network analysis for Chinese medicine injection].

PubMed

Li, Zheng; Kang, Li-Yuan; Fan, Xiao-Hui

2014-08-01

A new risk analysis method based upon reliability theory was introduced in this paper for the quality risk management of Chinese medicine injection manufacturing plants. The risk events including both cause and effect ones were derived in the framework as nodes with a Bayesian network analysis approach. It thus transforms the risk analysis results from failure mode and effect analysis (FMEA) into a Bayesian network platform. With its structure and parameters determined, the network can be used to evaluate the system reliability quantitatively with probabilistic analytical appraoches. Using network analysis tools such as GeNie and AgenaRisk, we are able to find the nodes that are most critical to influence the system reliability. The importance of each node to the system can be quantitatively evaluated by calculating the effect of the node on the overall risk, and minimization plan can be determined accordingly to reduce their influences and improve the system reliability. Using the Shengmai injection manufacturing plant of SZYY Ltd as a user case, we analyzed the quality risk with both static FMEA analysis and dynamic Bayesian Network analysis. The potential risk factors for the quality of Shengmai injection manufacturing were identified with the network analysis platform. Quality assurance actions were further defined to reduce the risk and improve the product quality.

Modeling the survival kinetics of Salmonella in tree nuts for use in risk assessment.

PubMed

Santillana Farakos, Sofia M; Pouillot, Régis; Anderson, Nathan; Johnson, Rhoma; Son, Insook; Van Doren, Jane

2016-06-16

Salmonella has been shown to survive in tree nuts over long periods of time. This survival capacity and its variability are key elements for risk assessment of Salmonella in tree nuts. The aim of this study was to develop a mathematical model to predict survival of Salmonella in tree nuts at ambient storage temperatures that considers variability and uncertainty separately and can easily be incorporated into a risk assessment model. Data on Salmonella survival on raw almonds, pecans, pistachios and walnuts were collected from the peer reviewed literature. The Weibull model was chosen as the baseline model and various fixed effect and mixed effect models were fit to the data. The best model identified through statistical analysis testing was then used to develop a hierarchical Bayesian model. Salmonella in tree nuts showed slow declines at temperatures ranging from 21°C to 24°C. A high degree of variability in survival was observed across tree nut studies reported in the literature. Statistical analysis results indicated that the best applicable model was a mixed effect model that included a fixed and random variation of δ per tree nut (which is the time it takes for the first log10 reduction) and a fixed variation of ρ per tree nut (parameter which defines the shape of the curve). Higher estimated survival rates (δ) were obtained for Salmonella on pistachios, followed in decreasing order by pecans, almonds and walnuts. The posterior distributions obtained from Bayesian inference were used to estimate the variability in the log10 decrease levels in survival for each tree nut, and the uncertainty of these estimates. These modeled uncertainty and variability distributions of the estimates can be used to obtain a complete exposure assessment of Salmonella in tree nuts when including time-temperature parameters for storage and consumption data. The statistical approach presented in this study may be applied to any studies that aim to develop predictive models to be implemented in a probabilistic exposure assessment or a quantitative microbial risk assessment. Published by Elsevier B.V.

A Bayesian hierarchical approach to comparative audit for carotid surgery.

PubMed

Kuhan, G; Marshall, E C; Abidia, A F; Chetter, I C; McCollum, P T

2002-12-01

the aim of this study was to illustrate how a Bayesian hierarchical modelling approach can aid the reliable comparison of outcome rates between surgeons. retrospective analysis of prospective and retrospective data. binary outcome data (death/stroke within 30 days), together with information on 15 possible risk factors specific for CEA were available on 836 CEAs performed by four vascular surgeons from 1992-99. The median patient age was 68 (range 38-86) years and 60% were men. the model was developed using the WinBUGS software. After adjusting for patient-level risk factors, a cross-validatory approach was adopted to identify "divergent" performance. A ranking exercise was also carried out. the overall observed 30-day stroke/death rate was 3.9% (33/836). The model found diabetes, stroke and heart disease to be significant risk factors. There was no significant difference between the predicted and observed outcome rates for any surgeon (Bayesian p -value>0.05). Each surgeon had a median rank of 3 with associated 95% CI 1.0-5.0, despite the variability of observed stroke/death rate from 2.9-4.4%. After risk adjustment, there was very little residual between-surgeon variability in outcome rate. Bayesian hierarchical models can help to accurately quantify the uncertainty associated with surgeons' performance and rank.

Risk analysis of new oral anticoagulants for gastrointestinal bleeding and intracranial hemorrhage in atrial fibrillation patients: a systematic review and network meta-analysis.

PubMed

Xu, Wei-Wei; Hu, Shen-Jiang; Wu, Tao

2017-07-01

Antithrombotic therapy using new oral anticoagulants (NOACs) in patients with atrial fibrillation (AF) has been generally shown to have a favorable risk-benefit profile. Since there has been dispute about the risks of gastrointestinal bleeding (GIB) and intracranial hemorrhage (ICH), we sought to conduct a systematic review and network meta-analysis using Bayesian inference to analyze the risks of GIB and ICH in AF patients taking NOACs. We analyzed data from 20 randomized controlled trials of 91 671 AF patients receiving anticoagulants, antiplatelet drugs, or placebo. Bayesian network meta-analysis of two different evidence networks was performed using a binomial likelihood model, based on a network in which different agents (and doses) were treated as separate nodes. Odds ratios (ORs) and 95% confidence intervals (CIs) were modeled using Markov chain Monte Carlo methods. Indirect comparisons with the Bayesian model confirmed that aspirin+clopidogrel significantly increased the risk of GIB in AF patients compared to the placebo (OR 0.33, 95% CI 0.01-0.92). Warfarin was identified as greatly increasing the risk of ICH compared to edoxaban 30 mg (OR 3.42, 95% CI 1.22-7.24) and dabigatran 110 mg (OR 3.56, 95% CI 1.10-8.45). We further ranked the NOACs for the lowest risk of GIB (apixaban 5 mg) and ICH (apixaban 5 mg, dabigatran 110 mg, and edoxaban 30 mg). Bayesian network meta-analysis of treatment of non-valvular AF patients with anticoagulants suggested that NOACs do not increase risks of GIB and/or ICH, compared to each other.

Epistemic-based investigation of the probability of hazard scenarios using Bayesian network for the lifting operation of floating objects

NASA Astrophysics Data System (ADS)

Toroody, Ahmad Bahoo; Abaiee, Mohammad Mahdi; Gholamnia, Reza; Ketabdari, Mohammad Javad

2016-09-01

Owing to the increase in unprecedented accidents with new root causes in almost all operational areas, the importance of risk management has dramatically risen. Risk assessment, one of the most significant aspects of risk management, has a substantial impact on the system-safety level of organizations, industries, and operations. If the causes of all kinds of failure and the interactions between them are considered, effective risk assessment can be highly accurate. A combination of traditional risk assessment approaches and modern scientific probability methods can help in realizing better quantitative risk assessment methods. Most researchers face the problem of minimal field data with respect to the probability and frequency of each failure. Because of this limitation in the availability of epistemic knowledge, it is important to conduct epistemic estimations by applying the Bayesian theory for identifying plausible outcomes. In this paper, we propose an algorithm and demonstrate its application in a case study for a light-weight lifting operation in the Persian Gulf of Iran. First, we identify potential accident scenarios and present them in an event tree format. Next, excluding human error, we use the event tree to roughly estimate the prior probability of other hazard-promoting factors using a minimal amount of field data. We then use the Success Likelihood Index Method (SLIM) to calculate the probability of human error. On the basis of the proposed event tree, we use the Bayesian network of the provided scenarios to compensate for the lack of data. Finally, we determine the resulting probability of each event based on its evidence in the epistemic estimation format by building on two Bayesian network types: the probability of hazard promotion factors and the Bayesian theory. The study results indicate that despite the lack of available information on the operation of floating objects, a satisfactory result can be achieved using epistemic data.

Estimating the extent and distribution of new-onset adult asthma in British Columbia using frequentist and Bayesian approaches.

PubMed

Beach, Jeremy; Burstyn, Igor; Cherry, Nicola

2012-07-01

We previously described a method to identify the incidence of new-onset adult asthma (NOAA) in Alberta by industry and occupation, utilizing Workers' Compensation Board (WCB) and physician billing data. The aim of this study was to extend this method to data from British Columbia (BC) so as to compare the two provinces and to incorporate Bayesian methodology into estimates of risk. WCB claims for any reason 1995-2004 were linked to physician billing data. NOAA was defined as a billing for asthma (ICD-9 493) in the 12 months before a WCB claim without asthma in the previous 3 years. Incidence was calculated by occupation and industry. In a matched case-referent analysis, associations with exposures were examined using an asthma-specific job exposure matrix (JEM). Posterior distributions from the Alberta analysis and estimated misclassification parameters were used as priors in the Bayesian analysis of the BC data. Among 1 118 239 eligible WCB claims the incidence of NOAA was 1.4%. Sixteen occupations and 44 industries had a significantly increased risk; six industries had a decreased risk. The JEM identified wood dust [odds ratio (OR) 1.55, 95% confidence interval (CI) 1.08-2.24] and animal antigens (OR 1.66, 95% CI 1.17-2.36) as related to an increased risk of NOAA. Exposure to isocyanates was associated with decreased risk (OR 0.57, 95% CI 0.39-0.85). Bayesian analyses taking account of exposure misclassification and informative priors resulted in posterior distributions of ORs with lower boundary of 95% credible intervals >1.00 for almost all exposures. The distribution of NOAA in BC appeared somewhat similar to that in Alberta, except for isocyanates. Bayesian analyses allowed incorporation of prior evidence into risk estimates, permitting reconsideration of the apparently protective effect of isocyanate exposure.

Bayesian networks improve causal environmental assessments for evidence-based policy

EPA Science Inventory

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the p...

Estimation of insurance premiums for coverage against natural disaster risk: an application of Bayesian Inference

NASA Astrophysics Data System (ADS)

Paudel, Y.; Botzen, W. J. W.; Aerts, J. C. J. H.

2013-03-01

This study applies Bayesian Inference to estimate flood risk for 53 dyke ring areas in the Netherlands, and focuses particularly on the data scarcity and extreme behaviour of catastrophe risk. The probability density curves of flood damage are estimated through Monte Carlo simulations. Based on these results, flood insurance premiums are estimated using two different practical methods that each account in different ways for an insurer's risk aversion and the dispersion rate of loss data. This study is of practical relevance because insurers have been considering the introduction of flood insurance in the Netherlands, which is currently not generally available.

Estimation model of life insurance claims risk for cancer patients by using Bayesian method

NASA Astrophysics Data System (ADS)

Sukono; Suyudi, M.; Islamiyati, F.; Supian, S.

2017-01-01

This paper discussed the estimation model of the risk of life insurance claims for cancer patients using Bayesian method. To estimate the risk of the claim, the insurance participant data is grouped into two: the number of policies issued and the number of claims incurred. Model estimation is done using a Bayesian approach method. Further, the estimator model was used to estimate the risk value of life insurance claims each age group for each sex. The estimation results indicate that a large risk premium for insured males aged less than 30 years is 0.85; for ages 30 to 40 years is 3:58; for ages 41 to 50 years is 1.71; for ages 51 to 60 years is 2.96; and for those aged over 60 years is 7.82. Meanwhile, for insured women aged less than 30 years was 0:56; for ages 30 to 40 years is 3:21; for ages 41 to 50 years is 0.65; for ages 51 to 60 years is 3:12; and for those aged over 60 years is 9.99. This study is useful in determining the risk premium in homogeneous groups based on gender and age.

[Evaluation of estimation of prevalence ratio using bayesian log-binomial regression model].

PubMed

Gao, W L; Lin, H; Liu, X N; Ren, X W; Li, J S; Shen, X P; Zhu, S L

2017-03-10

To evaluate the estimation of prevalence ratio ( PR ) by using bayesian log-binomial regression model and its application, we estimated the PR of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software. The results showed that caregivers' recognition of infant' s risk signs of diarrhea was associated significantly with a 13% increase of medical care-seeking. Meanwhile, we compared the differences in PR 's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1: not adjusting for the covariates; model 2: adjusting for duration of caregivers' education, model 3: adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model. The results showed that all three bayesian log-binomial regression models were convergence and the estimated PRs were 1.130(95 %CI : 1.005-1.265), 1.128(95 %CI : 1.001-1.264) and 1.132(95 %CI : 1.004-1.267), respectively. Conventional log-binomial regression model 1 and model 2 were convergence and their PRs were 1.130(95 % CI : 1.055-1.206) and 1.126(95 % CI : 1.051-1.203), respectively, but the model 3 was misconvergence, so COPY method was used to estimate PR , which was 1.125 (95 %CI : 1.051-1.200). In addition, the point estimation and interval estimation of PRs from three bayesian log-binomial regression models differed slightly from those of PRs from conventional log-binomial regression model, but they had a good consistency in estimating PR . Therefore, bayesian log-binomial regression model can effectively estimate PR with less misconvergence and have more advantages in application compared with conventional log-binomial regression model.

Social deprivation and population density are not associated with small area risk of amyotrophic lateral sclerosis.

PubMed

Rooney, James P K; Tobin, Katy; Crampsie, Arlene; Vajda, Alice; Heverin, Mark; McLaughlin, Russell; Staines, Anthony; Hardiman, Orla

2015-10-01

Evidence of an association between areal ALS risk and population density has been previously reported. We aim to examine ALS spatial incidence in Ireland using small areas, to compare this analysis with our previous analysis of larger areas and to examine the associations between population density, social deprivation and ALS incidence. Residential area social deprivation has not been previously investigated as a risk factor for ALS. Using the Irish ALS register, we included all cases of ALS diagnosed in Ireland from 1995-2013. 2006 census data was used to calculate age and sex standardised expected cases per small area. Social deprivation was assessed using the pobalHP deprivation index. Bayesian smoothing was used to calculate small area relative risk for ALS, whilst cluster analysis was performed using SaTScan. The effects of population density and social deprivation were tested in two ways: (1) as covariates in the Bayesian spatial model; (2) via post-Bayesian regression. 1701 cases were included. Bayesian smoothed maps of relative risk at small area resolution matched closely to our previous analysis at a larger area resolution. Cluster analysis identified two areas of significant low risk. These areas did not correlate with population density or social deprivation indices. Two areas showing low frequency of ALS have been identified in the Republic of Ireland. These areas do not correlate with population density or residential area social deprivation, indicating that other reasons, such as genetic admixture may account for the observed findings. Copyright © 2015 Elsevier Inc. All rights reserved.

Bayesian algorithm implementation in a real time exposure assessment model on benzene with calculation of associated cancer risks.

PubMed

Sarigiannis, Dimosthenis A; Karakitsios, Spyros P; Gotti, Alberto; Papaloukas, Costas L; Kassomenos, Pavlos A; Pilidis, Georgios A

2009-01-01

The objective of the current study was the development of a reliable modeling platform to calculate in real time the personal exposure and the associated health risk for filling station employees evaluating current environmental parameters (traffic, meteorological and amount of fuel traded) determined by the appropriate sensor network. A set of Artificial Neural Networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees. Furthermore, a Physiology Based Pharmaco-Kinetic (PBPK) risk assessment model was developed in order to calculate the lifetime probability distribution of leukemia to the employees, fed by data obtained by the ANN model. Bayesian algorithm was involved in crucial points of both model sub compartments. The application was evaluated in two filling stations (one urban and one rural). Among several algorithms available for the development of the ANN exposure model, Bayesian regularization provided the best results and seemed to be a promising technique for prediction of the exposure pattern of that occupational population group. On assessing the estimated leukemia risk under the scope of providing a distribution curve based on the exposure levels and the different susceptibility of the population, the Bayesian algorithm was a prerequisite of the Monte Carlo approach, which is integrated in the PBPK-based risk model. In conclusion, the modeling system described herein is capable of exploiting the information collected by the environmental sensors in order to estimate in real time the personal exposure and the resulting health risk for employees of gasoline filling stations.

Bayesian Algorithm Implementation in a Real Time Exposure Assessment Model on Benzene with Calculation of Associated Cancer Risks

PubMed Central

Sarigiannis, Dimosthenis A.; Karakitsios, Spyros P.; Gotti, Alberto; Papaloukas, Costas L.; Kassomenos, Pavlos A.; Pilidis, Georgios A.

2009-01-01

The objective of the current study was the development of a reliable modeling platform to calculate in real time the personal exposure and the associated health risk for filling station employees evaluating current environmental parameters (traffic, meteorological and amount of fuel traded) determined by the appropriate sensor network. A set of Artificial Neural Networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees. Furthermore, a Physiology Based Pharmaco-Kinetic (PBPK) risk assessment model was developed in order to calculate the lifetime probability distribution of leukemia to the employees, fed by data obtained by the ANN model. Bayesian algorithm was involved in crucial points of both model sub compartments. The application was evaluated in two filling stations (one urban and one rural). Among several algorithms available for the development of the ANN exposure model, Bayesian regularization provided the best results and seemed to be a promising technique for prediction of the exposure pattern of that occupational population group. On assessing the estimated leukemia risk under the scope of providing a distribution curve based on the exposure levels and the different susceptibility of the population, the Bayesian algorithm was a prerequisite of the Monte Carlo approach, which is integrated in the PBPK-based risk model. In conclusion, the modeling system described herein is capable of exploiting the information collected by the environmental sensors in order to estimate in real time the personal exposure and the resulting health risk for employees of gasoline filling stations. PMID:22399936

Cheese Microbial Risk Assessments — A Review

PubMed Central

Choi, Kyoung-Hee; Lee, Heeyoung; Lee, Soomin; Kim, Sejeong; Yoon, Yohan

2016-01-01

Cheese is generally considered a safe and nutritious food, but foodborne illnesses linked to cheese consumption have occurred in many countries. Several microbial risk assessments related to Listeria monocytogenes, Staphylococcus aureus, and Escherichia coli infections, causing cheese-related foodborne illnesses, have been conducted. Although the assessments of microbial risk in soft and low moisture cheeses such as semi-hard and hard cheeses have been accomplished, it has been more focused on the correlations between pathogenic bacteria and soft cheese, because cheese-associated foodborne illnesses have been attributed to the consumption of soft cheeses. As a part of this microbial risk assessment, predictive models have been developed to describe the relationship between several factors (pH, Aw, starter culture, and time) and the fates of foodborne pathogens in cheese. Predictions from these studies have been used for microbial risk assessment as a part of exposure assessment. These microbial risk assessments have identified that risk increased in cheese with high moisture content, especially for raw milk cheese, but the risk can be reduced by preharvest and postharvest preventions. For accurate quantitative microbial risk assessment, more data including interventions such as curd cooking conditions (temperature and time) and ripening period should be available for predictive models developed with cheese, cheese consumption amounts and cheese intake frequency data as well as more dose-response models. PMID:26950859

Informative priors on fetal fraction increase power of the noninvasive prenatal screen.

PubMed

Xu, Hanli; Wang, Shaowei; Ma, Lin-Lin; Huang, Shuai; Liang, Lin; Liu, Qian; Liu, Yang-Yang; Liu, Ke-Di; Tan, Ze-Min; Ban, Hao; Guan, Yongtao; Lu, Zuhong

2017-11-09

PurposeNoninvasive prenatal screening (NIPS) sequences a mixture of the maternal and fetal cell-free DNA. Fetal trisomy can be detected by examining chromosomal dosages estimated from sequencing reads. The traditional method uses the Z-test, which compares a subject against a set of euploid controls, where the information of fetal fraction is not fully utilized. Here we present a Bayesian method that leverages informative priors on the fetal fraction.MethodOur Bayesian method combines the Z-test likelihood and informative priors of the fetal fraction, which are learned from the sex chromosomes, to compute Bayes factors. Bayesian framework can account for nongenetic risk factors through the prior odds, and our method can report individual positive/negative predictive values.ResultsOur Bayesian method has more power than the Z-test method. We analyzed 3,405 NIPS samples and spotted at least 9 (of 51) possible Z-test false positives.ConclusionBayesian NIPS is more powerful than the Z-test method, is able to account for nongenetic risk factors through prior odds, and can report individual positive/negative predictive values.Genetics in Medicine advance online publication, 9 November 2017; doi:10.1038/gim.2017.186.

Confidence of compliance: a Bayesian approach for percentile standards.

PubMed

McBride, G B; Ellis, J C

2001-04-01

Rules for assessing compliance with percentile standards commonly limit the number of exceedances permitted in a batch of samples taken over a defined assessment period. Such rules are commonly developed using classical statistical methods. Results from alternative Bayesian methods are presented (using beta-distributed prior information and a binomial likelihood), resulting in "confidence of compliance" graphs. These allow simple reading of the consumer's risk and the supplier's risks for any proposed rule. The influence of the prior assumptions required by the Bayesian technique on the confidence results is demonstrated, using two reference priors (uniform and Jeffreys') and also using optimistic and pessimistic user-defined priors. All four give less pessimistic results than does the classical technique, because interpreting classical results as "confidence of compliance" actually invokes a Bayesian approach with an extreme prior distribution. Jeffreys' prior is shown to be the most generally appropriate choice of prior distribution. Cost savings can be expected using rules based on this approach.

Doctor, what does my positive test mean? From Bayesian textbook tasks to personalized risk communication

PubMed Central

Navarrete, Gorka; Correia, Rut; Sirota, Miroslav; Juanchich, Marie; Huepe, David

2015-01-01

Most of the research on Bayesian reasoning aims to answer theoretical questions about the extent to which people are able to update their beliefs according to Bayes' Theorem, about the evolutionary nature of Bayesian inference, or about the role of cognitive abilities in Bayesian inference. Few studies aim to answer practical, mainly health-related questions, such as, “What does it mean to have a positive test in a context of cancer screening?” or “What is the best way to communicate a medical test result so a patient will understand it?”. This type of research aims to translate empirical findings into effective ways of providing risk information. In addition, the applied research often adopts the paradigms and methods of the theoretically-motivated research. But sometimes it works the other way around, and the theoretical research borrows the importance of the practical question in the medical context. The study of Bayesian reasoning is relevant to risk communication in that, to be as useful as possible, applied research should employ specifically tailored methods and contexts specific to the recipients of the risk information. In this paper, we concentrate on the communication of the result of medical tests and outline the epidemiological and test parameters that affect the predictive power of a test—whether it is correct or not. Building on this, we draw up recommendations for better practice to convey the results of medical tests that could inform health policy makers (What are the drawbacks of mass screenings?), be used by health practitioners and, in turn, help patients to make better and more informed decisions. PMID:26441711

Microbial Impact on Success of Human Exploration Missions

NASA Technical Reports Server (NTRS)

Pierson, Duane L.; Ott, C. Mark; Groves, T. O.; Paloski, W. H. (Technical Monitor)

2000-01-01

The purpose of this study is to identify microbiological risks associated with space exploration and identify potential countermeasures available. Identification of microbial risks associated with space habitation requires knowledge of the sources and expected types of microbial agents. Crew data along with environmental data from water, surfaces, air, and free condensate are utilized in risk examination. Data from terrestrial models are also used. Microbial risks to crew health include bacteria, fungi, protozoa, and viruses. Adverse effects of microbes include: infections, allergic reactions, toxin production, release of volatiles, food spoilage, plant disease, material degradation, and environmental contamination. Risk is difficult to assess because of unknown potential changes in microbes (e.g., mutation) and the human host (e.g., immune changes). Prevention of adverse microbial impacts is preferred over remediation. Preventative measures include engineering measures (e.g., air filtration), crew microbial screening, acceptability standards, and active verification by onboard monitoring. Microbiological agents are important risks to human health and performance during space flight and risks increase with mission duration. Acceptable risk level must be defined. Prevention must be given high priority. Careful screening of crewmembers and payloads is an important element of any risk mitigation plan. Improved quantitation of microbiological risks is a high priority.

How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling.

PubMed

Onisko, Agnieszka; Druzdzel, Marek J; Austin, R Marshall

2016-01-01

Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.

Development of a Bayesian Classifier for Breast Cancer Risk Stratification: A Feasibility Study

DTIC Science & Technology

2010-03-29

IV 0 2 10 23 BIRADS V 0 1 2 1 No mammogram 116 94 55 45 Breast biopsy category .4076 Benign, no atypia 19 12 27 34 Premalignant 1 0 2 4 Infiltrating... breast EIS result∗ Estimated outcome, % Known evidence Biopsy category EIS Gail Benign, no Infiltrating cancer Case frequency, % result cutoff‘ atypia or...Development of a Bayesian Classifier for Breast Cancer Risk Stratification: A Feasibility Study Alexander Stojadinovic, MD,a,b Christina Eberhardt,a

Using auxiliary information to improve wildlife disease surveillance when infected animals are not detected: a Bayesian approach

USGS Publications Warehouse

Heisey, Dennis M.; Jennelle, Christopher S.; Russell, Robin E.; Walsh, Daniel P.

2014-01-01

There are numerous situations in which it is important to determine whether a particular disease of interest is present in a free-ranging wildlife population. However adequate disease surveillance can be labor-intensive and expensive and thus there is substantial motivation to conduct it as efficiently as possible. Surveillance is often based on the assumption of a simple random sample, but this can almost always be improved upon if there is auxiliary information available about disease risk factors. We present a Bayesian approach to disease surveillance when auxiliary risk information is available which will usually allow for substantial improvements over simple random sampling. Others have employed risk weights in surveillance, but this can result in overly optimistic statements regarding freedom from disease due to not accounting for the uncertainty in the auxiliary information; our approach remedies this. We compare our Bayesian approach to a published example of risk weights applied to chronic wasting disease in deer in Colorado, and we also present calculations to examine when uncertainty in the auxiliary information has a serious impact on the risk weights approach. Our approach allows “apples-to-apples” comparisons of surveillance efficiencies between units where heterogeneous samples were collected

Human Immune Function and Microbial Pathogenesis in Human Spaceflight

NASA Technical Reports Server (NTRS)

Pierson, Duane J.; Ott, M.

2006-01-01

This oral presentation was requested by Conference conveners. The requested subject is microbial risk assessment considering changes in the human immune system during flight and microbial diversity of environmental samples aboard the International Space Station (ISS). The presentation will begin with an introduction discussing the goals and limitations of microbial risk assessment during flight. The main portion of the presentation will include changes in the immune system that have been published, historical data from microbial analyses, and initial modeling of the environmental flora aboard ISS. The presentation will conclude with future goals and techniques to enhance our ability to perform microbial risk assessment on long duration missions.

A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

PubMed

Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

2013-06-01

Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric Society.

Bayesian Analysis for Risk Assessment of Selected Medical Events in Support of the Integrated Medical Model Effort

NASA Technical Reports Server (NTRS)

Gilkey, Kelly M.; Myers, Jerry G.; McRae, Michael P.; Griffin, Elise A.; Kallrui, Aditya S.

2012-01-01

The Exploration Medical Capability project is creating a catalog of risk assessments using the Integrated Medical Model (IMM). The IMM is a software-based system intended to assist mission planners in preparing for spaceflight missions by helping them to make informed decisions about medical preparations and supplies needed for combating and treating various medical events using Probabilistic Risk Assessment. The objective is to use statistical analyses to inform the IMM decision tool with estimated probabilities of medical events occurring during an exploration mission. Because data regarding astronaut health are limited, Bayesian statistical analysis is used. Bayesian inference combines prior knowledge, such as data from the general U.S. population, the U.S. Submarine Force, or the analog astronaut population located at the NASA Johnson Space Center, with observed data for the medical condition of interest. The posterior results reflect the best evidence for specific medical events occurring in flight. Bayes theorem provides a formal mechanism for combining available observed data with data from similar studies to support the quantification process. The IMM team performed Bayesian updates on the following medical events: angina, appendicitis, atrial fibrillation, atrial flutter, dental abscess, dental caries, dental periodontal disease, gallstone disease, herpes zoster, renal stones, seizure, and stroke.

Hierarchical Bayesian modeling of spatio-temporal patterns of lung cancer incidence risk in Georgia, USA: 2000-2007

NASA Astrophysics Data System (ADS)

Yin, Ping; Mu, Lan; Madden, Marguerite; Vena, John E.

2014-10-01

Lung cancer is the second most commonly diagnosed cancer in both men and women in Georgia, USA. However, the spatio-temporal patterns of lung cancer risk in Georgia have not been fully studied. Hierarchical Bayesian models are used here to explore the spatio-temporal patterns of lung cancer incidence risk by race and gender in Georgia for the period of 2000-2007. With the census tract level as the spatial scale and the 2-year period aggregation as the temporal scale, we compare a total of seven Bayesian spatio-temporal models including two under a separate modeling framework and five under a joint modeling framework. One joint model outperforms others based on the deviance information criterion. Results show that the northwest region of Georgia has consistently high lung cancer incidence risk for all population groups during the study period. In addition, there are inverse relationships between the socioeconomic status and the lung cancer incidence risk among all Georgian population groups, and the relationships in males are stronger than those in females. By mapping more reliable variations in lung cancer incidence risk at a relatively fine spatio-temporal scale for different Georgian population groups, our study aims to better support healthcare performance assessment, etiological hypothesis generation, and health policy making.

Spatial mapping and prediction of Plasmodium falciparum infection risk among school-aged children in Côte d'Ivoire.

PubMed

Houngbedji, Clarisse A; Chammartin, Frédérique; Yapi, Richard B; Hürlimann, Eveline; N'Dri, Prisca B; Silué, Kigbafori D; Soro, Gotianwa; Koudou, Benjamin G; Assi, Serge-Brice; N'Goran, Eliézer K; Fantodji, Agathe; Utzinger, Jürg; Vounatsou, Penelope; Raso, Giovanna

2016-09-07

In Côte d'Ivoire, malaria remains a major public health issue, and thus a priority to be tackled. The aim of this study was to identify spatially explicit indicators of Plasmodium falciparum infection among school-aged children and to undertake a model-based spatial prediction of P. falciparum infection risk using environmental predictors. A cross-sectional survey was conducted, including parasitological examinations and interviews with more than 5,000 children from 93 schools across Côte d'Ivoire. A finger-prick blood sample was obtained from each child to determine Plasmodium species-specific infection and parasitaemia using Giemsa-stained thick and thin blood films. Household socioeconomic status was assessed through asset ownership and household characteristics. Children were interviewed for preventive measures against malaria. Environmental data were gathered from satellite images and digitized maps. A Bayesian geostatistical stochastic search variable selection procedure was employed to identify factors related to P. falciparum infection risk. Bayesian geostatistical logistic regression models were used to map the spatial distribution of P. falciparum infection and to predict the infection prevalence at non-sampled locations via Bayesian kriging. Complete data sets were available from 5,322 children aged 5-16 years across Côte d'Ivoire. P. falciparum was the predominant species (94.5 %). The Bayesian geostatistical variable selection procedure identified land cover and socioeconomic status as important predictors for infection risk with P. falciparum. Model-based prediction identified high P. falciparum infection risk in the north, central-east, south-east, west and south-west of Côte d'Ivoire. Low-risk areas were found in the south-eastern area close to Abidjan and the south-central and west-central part of the country. The P. falciparum infection risk and related uncertainty estimates for school-aged children in Côte d'Ivoire represent the most up-to-date malaria risk maps. These tools can be used for spatial targeting of malaria control interventions.

Hierarchical Bayesian Modeling of Fluid-Induced Seismicity

NASA Astrophysics Data System (ADS)

Broccardo, M.; Mignan, A.; Wiemer, S.; Stojadinovic, B.; Giardini, D.

2017-11-01

In this study, we present a Bayesian hierarchical framework to model fluid-induced seismicity. The framework is based on a nonhomogeneous Poisson process with a fluid-induced seismicity rate proportional to the rate of injected fluid. The fluid-induced seismicity rate model depends upon a set of physically meaningful parameters and has been validated for six fluid-induced case studies. In line with the vision of hierarchical Bayesian modeling, the rate parameters are considered as random variables. We develop both the Bayesian inference and updating rules, which are used to develop a probabilistic forecasting model. We tested the Basel 2006 fluid-induced seismic case study to prove that the hierarchical Bayesian model offers a suitable framework to coherently encode both epistemic uncertainty and aleatory variability. Moreover, it provides a robust and consistent short-term seismic forecasting model suitable for online risk quantification and mitigation.

Calibrating Bayesian Network Representations of Social-Behavioral Models

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

Whitney, Paul D.; Walsh, Stephen J.

2010-04-08

While human behavior has long been studied, recent and ongoing advances in computational modeling present opportunities for recasting research outcomes in human behavior. In this paper we describe how Bayesian networks can represent outcomes of human behavior research. We demonstrate a Bayesian network that represents political radicalization research – and show a corresponding visual representation of aspects of this research outcome. Since Bayesian networks can be quantitatively compared with external observations, the representation can also be used for empirical assessments of the research which the network summarizes. For a political radicalization model based on published research, we show this empiricalmore » comparison with data taken from the Minorities at Risk Organizational Behaviors database.« less

Generalizability of Evidence-Based Assessment Recommendations for Pediatric Bipolar Disorder

PubMed Central

Jenkins, Melissa M.; Youngstrom, Eric A.; Youngstrom, Jennifer Kogos; Feeny, Norah C.; Findling, Robert L.

2013-01-01

Bipolar disorder is frequently clinically diagnosed in youths who do not actually satisfy DSM-IV criteria, yet cases that would satisfy full DSM-IV criteria are often undetected clinically. Evidence-based assessment methods that incorporate Bayesian reasoning have demonstrated improved diagnostic accuracy, and consistency; however, their clinical utility is largely unexplored. The present study examines the effectiveness of promising evidence-based decision-making compared to the clinical gold standard. Participants were 562 youth, ages 5-17 and predominantly African American, drawn from a community mental health clinic. Research diagnoses combined semi-structured interview with youths’ psychiatric, developmental, and family mental health histories. Independent Bayesian estimates relied on published risk estimates from other samples discriminated bipolar diagnoses, Area Under Curve=.75, p<.00005. The Bayes and confidence ratings correlated rs =.30. Agreement about an evidence-based assessment intervention “threshold model” (wait/assess/treat) had K=.24, p<.05. No potential moderators of agreement between the Bayesian estimates and confidence ratings, including type of bipolar illness, were significant. Bayesian risk estimates were highly correlated with logistic regression estimates using optimal sample weights, r=.81, p<.0005. Clinical and Bayesian approaches agree in terms of overall concordance and deciding next clinical action, even when Bayesian predictions are based on published estimates from clinically and demographically different samples. Evidence-based assessment methods may be useful in settings that cannot routinely employ gold standard assessments, and they may help decrease rates of overdiagnosis while promoting earlier identification of true cases. PMID:22004538

Bayesian sensitivity analysis methods to evaluate bias due to misclassification and missing data using informative priors and external validation data.

PubMed

Luta, George; Ford, Melissa B; Bondy, Melissa; Shields, Peter G; Stamey, James D

2013-04-01

Recent research suggests that the Bayesian paradigm may be useful for modeling biases in epidemiological studies, such as those due to misclassification and missing data. We used Bayesian methods to perform sensitivity analyses for assessing the robustness of study findings to the potential effect of these two important sources of bias. We used data from a study of the joint associations of radiotherapy and smoking with primary lung cancer among breast cancer survivors. We used Bayesian methods to provide an operational way to combine both validation data and expert opinion to account for misclassification of the two risk factors and missing data. For comparative purposes we considered a "full model" that allowed for both misclassification and missing data, along with alternative models that considered only misclassification or missing data, and the naïve model that ignored both sources of bias. We identified noticeable differences between the four models with respect to the posterior distributions of the odds ratios that described the joint associations of radiotherapy and smoking with primary lung cancer. Despite those differences we found that the general conclusions regarding the pattern of associations were the same regardless of the model used. Overall our results indicate a nonsignificantly decreased lung cancer risk due to radiotherapy among nonsmokers, and a mildly increased risk among smokers. We described easy to implement Bayesian methods to perform sensitivity analyses for assessing the robustness of study findings to misclassification and missing data. Copyright © 2012 Elsevier Ltd. All rights reserved.

Use of surrogate indicators for the evaluation of potential health risks due to poor urban water quality: A Bayesian Network approach.

PubMed

Wijesiri, Buddhi; Deilami, Kaveh; McGree, James; Goonetilleke, Ashantha

2018-02-01

Urban water pollution poses risks of waterborne infectious diseases. Therefore, in order to improve urban liveability, effective pollution mitigation strategies are required underpinned by predictions generated using water quality models. However, the lack of reliability in current modelling practices detrimentally impacts planning and management decision making. This research study adopted a novel approach in the form of Bayesian Networks to model urban water quality to better investigate the factors that influence risks to human health. The application of Bayesian Networks was found to enhance the integration of quantitative and qualitative spatially distributed data for analysing the influence of environmental and anthropogenic factors using three surrogate indicators of human health risk, namely, turbidity, total nitrogen and fats/oils. Expert knowledge was found to be of critical importance in assessing the interdependent relationships between health risk indicators and influential factors. The spatial variability maps of health risk indicators developed enabled the initial identification of high risk areas in which flooding was found to be the most significant influential factor in relation to human health risk. Surprisingly, population density was found to be less significant in influencing health risk indicators. These high risk areas in turn can be subjected to more in-depth investigations instead of the entire region, saving time and resources. It was evident that decision making in relation to the design of pollution mitigation strategies needs to account for the impact of landscape characteristics on water quality, which can be related to risk to human health. Copyright © 2017 Elsevier Ltd. All rights reserved.

Integrated Environmental Modeling: Quantitative Microbial Risk Assessment

EPA Science Inventory

The presentation discusses the need for microbial assessments and presents a road map associated with quantitative microbial risk assessments, through an integrated environmental modeling approach. A brief introduction and the strengths of the current knowledge are illustrated. W...

Developing a new Bayesian Risk Index for risk evaluation of soil contamination.

PubMed

Albuquerque, M T D; Gerassis, S; Sierra, C; Taboada, J; Martín, J E; Antunes, I M H R; Gallego, J R

2017-12-15

Industrial and agricultural activities heavily constrain soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment alike. In this regard, the identification of areas that require remediation is crucial. In the herein research a geochemical dataset (230 samples) comprising 14 elements (Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, Cd, V, Cr, Ti, Al and S) was gathered throughout eight different zones distinguished by their main activity, namely, recreational, agriculture/livestock and heavy industry in the Avilés Estuary (North of Spain). Then a stratified systematic sampling method was used at short, medium, and long distances from each zone to obtain a representative picture of the total variability of the selected attributes. The information was then combined in four risk classes (Low, Moderate, High, Remediation) following reference values from several sediment quality guidelines (SQGs). A Bayesian analysis, inferred for each zone, allowed the characterization of PTEs correlations, the unsupervised learning network technique proving to be the best fit. Based on the Bayesian network structure obtained, Pb, As and Mn were selected as key contamination parameters. For these 3 elements, the conditional probability obtained was allocated to each observed point, and a simple, direct index (Bayesian Risk Index-BRI) was constructed as a linear rating of the pre-defined risk classes weighted by the previously obtained probability. Finally, the BRI underwent geostatistical modeling. One hundred Sequential Gaussian Simulations (SGS) were computed. The Mean Image and the Standard Deviation maps were obtained, allowing the definition of High/Low risk clusters (Local G clustering) and the computation of spatial uncertainty. High-risk clusters are mainly distributed within the area with the highest altitude (agriculture/livestock) showing an associated low spatial uncertainty, clearly indicating the need for remediation. Atmospheric emissions, mainly derived from the metallurgical industry, contribute to soil contamination by PTEs. Copyright © 2017 Elsevier B.V. All rights reserved.

Exploring the Specifications of Spatial Adjacencies and Weights in Bayesian Spatial Modeling with Intrinsic Conditional Autoregressive Priors in a Small-area Study of Fall Injuries

PubMed Central

Law, Jane

2016-01-01

Intrinsic conditional autoregressive modeling in a Bayeisan hierarchical framework has been increasingly applied in small-area ecological studies. This study explores the specifications of spatial structure in this Bayesian framework in two aspects: adjacency, i.e., the set of neighbor(s) for each area; and (spatial) weight for each pair of neighbors. Our analysis was based on a small-area study of falling injuries among people age 65 and older in Ontario, Canada, that was aimed to estimate risks and identify risk factors of such falls. In the case study, we observed incorrect adjacencies information caused by deficiencies in the digital map itself. Further, when equal weights was replaced by weights based on a variable of expected count, the range of estimated risks increased, the number of areas with probability of estimated risk greater than one at different probability thresholds increased, and model fit improved. More importantly, significance of a risk factor diminished. Further research to thoroughly investigate different methods of variable weights; quantify the influence of specifications of spatial weights; and develop strategies for better defining spatial structure of a map in small-area analysis in Bayesian hierarchical spatial modeling is recommended. PMID:29546147

Bayesian methods in reliability

NASA Astrophysics Data System (ADS)

Sander, P.; Badoux, R.

1991-11-01

The present proceedings from a course on Bayesian methods in reliability encompasses Bayesian statistical methods and their computational implementation, models for analyzing censored data from nonrepairable systems, the traits of repairable systems and growth models, the use of expert judgment, and a review of the problem of forecasting software reliability. Specific issues addressed include the use of Bayesian methods to estimate the leak rate of a gas pipeline, approximate analyses under great prior uncertainty, reliability estimation techniques, and a nonhomogeneous Poisson process. Also addressed are the calibration sets and seed variables of expert judgment systems for risk assessment, experimental illustrations of the use of expert judgment for reliability testing, and analyses of the predictive quality of software-reliability growth models such as the Weibull order statistics.

Daniel Goodman’s empirical approach to Bayesian statistics

USGS Publications Warehouse

Gerrodette, Tim; Ward, Eric; Taylor, Rebecca L.; Schwarz, Lisa K.; Eguchi, Tomoharu; Wade, Paul; Himes Boor, Gina

2016-01-01

Bayesian statistics, in contrast to classical statistics, uses probability to represent uncertainty about the state of knowledge. Bayesian statistics has often been associated with the idea that knowledge is subjective and that a probability distribution represents a personal degree of belief. Dr. Daniel Goodman considered this viewpoint problematic for issues of public policy. He sought to ground his Bayesian approach in data, and advocated the construction of a prior as an empirical histogram of “similar” cases. In this way, the posterior distribution that results from a Bayesian analysis combined comparable previous data with case-specific current data, using Bayes’ formula. Goodman championed such a data-based approach, but he acknowledged that it was difficult in practice. If based on a true representation of our knowledge and uncertainty, Goodman argued that risk assessment and decision-making could be an exact science, despite the uncertainties. In his view, Bayesian statistics is a critical component of this science because a Bayesian analysis produces the probabilities of future outcomes. Indeed, Goodman maintained that the Bayesian machinery, following the rules of conditional probability, offered the best legitimate inference from available data. We give an example of an informative prior in a recent study of Steller sea lion spatial use patterns in Alaska.

Potential microbial risk factors related to soil amendments and irrigation water of potato crops.

PubMed

Selma, M V; Allende, A; López-Gálvez, F; Elizaquível, P; Aznar, R; Gil, M I

2007-12-01

This study assesses the potential microbial risk factors related to the use of soil amendments and irrigation water on potato crops, cultivated in one traditional and two intensive farms during two harvest seasons. The natural microbiota and potentially pathogenic micro-organisms were evaluated in the soil amendment, irrigation water, soil and produce. Uncomposted amendments and residual and creek water samples showed the highest microbial counts. The microbial load of potatoes harvested in spring was similar among the tested farms despite the diverse microbial levels of Listeria spp. and faecal coliforms in the potential risk sources. However, differences in total coliform load of potato were found between farms cultivated in the autumn. Immunochromatographic rapid tests and the BAM's reference method (Bacteriological Analytical Manual; AOAC International) were used to detect Escherichia coli O157:H7 from the potential risk sources and produce. Confirmation of the positive results by polymerase chain reaction procedures showed that the immunochromatographic assay was not reliable as it led to false-positive results. The potentially pathogenic micro-organisms of soil amendment, irrigation water and soil samples changed with the harvest seasons and the use of different agricultural practices. However, the microbial load of the produce was not always influenced by these risk sources. Improvements in environmental sample preparation are needed to avoid interferences in the use of immunochromatographic rapid tests. The potential microbial risk sources of fresh produce should be regularly controlled using reliable detection methods to guarantee their microbial safety.

Predicting Student Success: A Naïve Bayesian Application to Community College Data

ERIC Educational Resources Information Center

Ornelas, Fermin; Ordonez, Carlos

2017-01-01

This research focuses on developing and implementing a continuous Naïve Bayesian classifier for GEAR courses at Rio Salado Community College. Previous implementation efforts of a discrete version did not predict as well, 70%, and had deployment issues. This predictive model has higher prediction, over 90%, accuracy for both at-risk and successful…

Estimating effectiveness in HIV prevention trials with a Bayesian hierarchical compound Poisson frailty model

PubMed Central

Coley, Rebecca Yates; Browna, Elizabeth R.

2016-01-01

Inconsistent results in recent HIV prevention trials of pre-exposure prophylactic interventions may be due to heterogeneity in risk among study participants. Intervention effectiveness is most commonly estimated with the Cox model, which compares event times between populations. When heterogeneity is present, this population-level measure underestimates intervention effectiveness for individuals who are at risk. We propose a likelihood-based Bayesian hierarchical model that estimates the individual-level effectiveness of candidate interventions by accounting for heterogeneity in risk with a compound Poisson-distributed frailty term. This model reflects the mechanisms of HIV risk and allows that some participants are not exposed to HIV and, therefore, have no risk of seroconversion during the study. We assess model performance via simulation and apply the model to data from an HIV prevention trial. PMID:26869051

Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches.

PubMed

Valle, Denis; Lima, Joanna M Tucker; Millar, Justin; Amratia, Punam; Haque, Ubydul

2015-11-04

Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.

A QRM Discussion of Microbial Contamination of Non-sterile Drug Products, Using FDA and EMA Warning Letters Recorded between 2008 and 2016.

PubMed

Santos, Ana M C; Doria, Mara S; Meirinhos-Soares, Luís; Almeida, António J; Menezes, José C

2018-01-01

Microbial quality control of non-sterile drug products has been a concern to regulatory agencies and the pharmaceutical industry since the 1960s. Despite being an old challenge to companies, microbial contamination still affects a high number of manufacturers of non-sterile products. Consequences go well beyond the obvious direct costs related to batch rejections or product recalls, as human lives and a company's reputation are significantly impacted if such events occur. To better manage risk and establish effective mitigation strategies, it is necessary to understand the microbial hazards involved in non-sterile drug products manufacturing, be able to evaluate their potential impact on final product quality, and apply mitigation actions. Herein we discuss the most likely root causes involved in microbial contaminations referenced in warning letters issued by US health authorities and non-compliance reports issued by European health authorities over a period of several years. The quality risk management tools proposed were applied to the data gathered from those databases, and a generic risk ranking was provided based on a panel of non-sterile drug product manufacturers that was assembled and given the opportunity to perform the risk assessments. That panel identified gaps and defined potential mitigation actions, based on their own experience of potential risks expected for their processes. Major findings clearly indicate that the manufacturers affected by the warning letters should focus their attention on process improvements and microbial control strategies, especially those related to microbial analysis and raw material quality control. Additionally, the WLs considered frequently referred to failures in quality-related issues, which indicates that the quality commitment should be reinforced at most companies to avoid microbiological contaminations. LAY ABSTRACT: Microbial contamination of drug products affects the quality of non-sterile drug products produced by numerous manufacturers, representing a major risk to patients. It is necessary to understand the microbial hazards involved in the manufacturing process and evaluate their impact on final product quality so that effective prevention strategies can be implemented. A risk-based classification of most likely root causes for microbial contamination found in the warning letters issued by the US Food and Drug Administration and the European Medicines Agency is proposed. To validate the likely root causes extracted from the warning letters, a subject matter expert panel made of several manufacturers was formed and consulted. A quality risk management approach to assess microbiological contamination of non-sterile drug products is proposed for the identification of microbial hazards involved in the manufacturing process. To enable ranking of microbial contamination risks, quality risk management metrics related to criticality and overall risk were applied. The results showed that manufacturers of non-sterile drug products should improve their microbial control strategy, with special attention to quality controls of raw materials, primary containers, and closures. Besides that, they should invest in a more robust quality system and culture. As a start, manufacturers may consider investigating their specific microbiological risks, adressing their sites' own microbial ecology, type of manufacturing processes, and dosage form characteristics, as these may lead to increased contamination risks. Authorities should allow and enforce innovative, more comprehensive, and more effective approaches to in-process contamination monitoring and controls. © PDA, Inc. 2018.

Assessing Requirements Volatility and Risk Using Bayesian Networks

NASA Technical Reports Server (NTRS)

Russell, Michael S.

2010-01-01

There are many factors that affect the level of requirements volatility a system experiences over its lifecycle and the risk that volatility imparts. Improper requirements generation, undocumented user expectations, conflicting design decisions, and anticipated / unanticipated world states are representative of these volatility factors. Combined, these volatility factors can increase programmatic risk and adversely affect successful system development. This paper proposes that a Bayesian Network can be used to support reasonable judgments concerning the most likely sources and types of requirements volatility a developing system will experience prior to starting development and by doing so it is possible to predict the level of requirements volatility the system will experience over its lifecycle. This assessment offers valuable insight to the system's developers, particularly by providing a starting point for risk mitigation planning and execution.

Introduction of Bayesian network in risk analysis of maritime accidents in Bangladesh

NASA Astrophysics Data System (ADS)

Rahman, Sohanur

2017-12-01

Due to the unique geographic location, complex navigation environment and intense vessel traffic, a considerable number of maritime accidents occurred in Bangladesh which caused serious loss of life, property and environmental contamination. Based on the historical data of maritime accidents from 1981 to 2015, which has been collected from Department of Shipping (DOS) and Bangladesh Inland Water Transport Authority (BIWTA), this paper conducted a risk analysis of maritime accidents by applying Bayesian network. In order to conduct this study, a Bayesian network model has been developed to find out the relation among parameters and the probability of them which affect accidents based on the accident investigation report of Bangladesh. Furthermore, number of accidents in different categories has also been investigated in this paper. Finally, some viable recommendations have been proposed in order to ensure greater safety of inland vessels in Bangladesh.

A Bayesian network model for predicting type 2 diabetes risk based on electronic health records

NASA Astrophysics Data System (ADS)

Xie, Jiang; Liu, Yan; Zeng, Xu; Zhang, Wu; Mei, Zhen

2017-07-01

An extensive, in-depth study of diabetes risk factors (DBRF) is of crucial importance to prevent (or reduce) the chance of suffering from type 2 diabetes (T2D). Accumulation of electronic health records (EHRs) makes it possible to build nonlinear relationships between risk factors and diabetes. However, the current DBRF researches mainly focus on qualitative analyses, and the inconformity of physical examination items makes the risk factors likely to be lost, which drives us to study the novel machine learning approach for risk model development. In this paper, we use Bayesian networks (BNs) to analyze the relationship between physical examination information and T2D, and to quantify the link between risk factors and T2D. Furthermore, with the quantitative analyses of DBRF, we adopt EHR and propose a machine learning approach based on BNs to predict the risk of T2D. The experiments demonstrate that our approach can lead to better predictive performance than the classical risk model.

Probabilistic Approaches for Multi-Hazard Risk Assessment of Structures and Systems

NASA Astrophysics Data System (ADS)

Kwag, Shinyoung

Performance assessment of structures, systems, and components for multi-hazard scenarios has received significant attention in recent years. However, the concept of multi-hazard analysis is quite broad in nature and the focus of existing literature varies across a wide range of problems. In some cases, such studies focus on hazards that either occur simultaneously or are closely correlated with each other. For example, seismically induced flooding or seismically induced fires. In other cases, multi-hazard studies relate to hazards that are not dependent or correlated but have strong likelihood of occurrence at different times during the lifetime of a structure. The current approaches for risk assessment need enhancement to account for multi-hazard risks. It must be able to account for uncertainty propagation in a systems-level analysis, consider correlation among events or failure modes, and allow integration of newly available information from continually evolving simulation models, experimental observations, and field measurements. This dissertation presents a detailed study that proposes enhancements by incorporating Bayesian networks and Bayesian updating within a performance-based probabilistic framework. The performance-based framework allows propagation of risk as well as uncertainties in the risk estimates within a systems analysis. Unlike conventional risk assessment techniques such as a fault-tree analysis, a Bayesian network can account for statistical dependencies and correlations among events/hazards. The proposed approach is extended to develop a risk-informed framework for quantitative validation and verification of high fidelity system-level simulation tools. Validation of such simulations can be quite formidable within the context of a multi-hazard risk assessment in nuclear power plants. The efficiency of this approach lies in identification of critical events, components, and systems that contribute to the overall risk. Validation of any event or component on the critical path is relatively more important in a risk-informed environment. Significance of multi-hazard risk is also illustrated for uncorrelated hazards of earthquakes and high winds which may result in competing design objectives. It is also illustrated that the number of computationally intensive nonlinear simulations needed in performance-based risk assessment for external hazards can be significantly reduced by using the power of Bayesian updating in conjunction with the concept of equivalent limit-state.

A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods

DOE PAGES

Groth, Katrina M.; Smith, Curtis L.; Swiler, Laura P.

2014-04-05

In the past several years, several international agencies have begun to collect data on human performance in nuclear power plant simulators [1]. This data provides a valuable opportunity to improve human reliability analysis (HRA), but there improvements will not be realized without implementation of Bayesian methods. Bayesian methods are widely used in to incorporate sparse data into models in many parts of probabilistic risk assessment (PRA), but Bayesian methods have not been adopted by the HRA community. In this article, we provide a Bayesian methodology to formally use simulator data to refine the human error probabilities (HEPs) assigned by existingmore » HRA methods. We demonstrate the methodology with a case study, wherein we use simulator data from the Halden Reactor Project to update the probability assignments from the SPAR-H method. The case study demonstrates the ability to use performance data, even sparse data, to improve existing HRA methods. Furthermore, this paper also serves as a demonstration of the value of Bayesian methods to improve the technical basis of HRA.« less

RISK ASSESSMENT AND EPIDEMIOLOGICAL INFORMATION FOR PATHOGENIC MICROORGANISMS APPLIED TO SOIL

EPA Science Inventory

There is increasing interest in the development of a microbial risk assessment methodology for regulatory and operational decision making. Initial interests in microbial risk assessments focused on drinking, recreational, and reclaimed water issues. More recently risk assessmen...

Drivers of Microbial Risk for Direct Potable Reuse and de Facto Reuse Treatment Schemes: The Impacts of Source Water Quality and Blending.

PubMed

Chaudhry, Rabia M; Hamilton, Kerry A; Haas, Charles N; Nelson, Kara L

2017-06-13

Although reclaimed water for potable applications has many potential benefits, it poses concerns for chemical and microbial risks to consumers. We present a quantitative microbial risk assessment (QMRA) Monte Carlo framework to compare a de facto water reuse scenario (treated wastewater-impacted surface water) with four hypothetical Direct Potable Reuse (DPR) scenarios for Norovirus, Cryptosporidium , and Salmonella . Consumer microbial risks of surface source water quality (impacted by 0-100% treated wastewater effluent) were assessed. Additionally, we assessed risks for different blending ratios (0-100% surface water blended into advanced-treated DPR water) when source surface water consisted of 50% wastewater effluent. De facto reuse risks exceeded the yearly 10 -4 infections risk benchmark while all modeled DPR risks were significantly lower. Contamination with 1% or more wastewater effluent in the source water, and blending 1% or more wastewater-impacted surface water into the advanced-treated DPR water drove the risk closer to the 10 -4 benchmark. We demonstrate that de facto reuse by itself, or as an input into DPR, drives microbial risks more so than the advanced-treated DPR water. When applied using location-specific inputs, this framework can contribute to project design and public awareness campaigns to build legitimacy for DPR.

Drivers of Microbial Risk for Direct Potable Reuse and de Facto Reuse Treatment Schemes: The Impacts of Source Water Quality and Blending

PubMed Central

Chaudhry, Rabia M.; Hamilton, Kerry A.; Haas, Charles N.; Nelson, Kara L.

2017-01-01

Although reclaimed water for potable applications has many potential benefits, it poses concerns for chemical and microbial risks to consumers. We present a quantitative microbial risk assessment (QMRA) Monte Carlo framework to compare a de facto water reuse scenario (treated wastewater-impacted surface water) with four hypothetical Direct Potable Reuse (DPR) scenarios for Norovirus, Cryptosporidium, and Salmonella. Consumer microbial risks of surface source water quality (impacted by 0–100% treated wastewater effluent) were assessed. Additionally, we assessed risks for different blending ratios (0–100% surface water blended into advanced-treated DPR water) when source surface water consisted of 50% wastewater effluent. De facto reuse risks exceeded the yearly 10−4 infections risk benchmark while all modeled DPR risks were significantly lower. Contamination with 1% or more wastewater effluent in the source water, and blending 1% or more wastewater-impacted surface water into the advanced-treated DPR water drove the risk closer to the 10−4 benchmark. We demonstrate that de facto reuse by itself, or as an input into DPR, drives microbial risks more so than the advanced-treated DPR water. When applied using location-specific inputs, this framework can contribute to project design and public awareness campaigns to build legitimacy for DPR. PMID:28608808

Quantitative Microbial Risk Assessment Tutorial: Installation of Software for Watershed Modeling in Support of QMRA

EPA Science Inventory

This tutorial provides instructions for accessing, retrieving, and downloading the following software to install on a host computer in support of Quantitative Microbial Risk Assessment (QMRA) modeling:• SDMProjectBuilder (which includes the Microbial Source Module as part...

A Bayesian method for detecting pairwise associations in compositional data

PubMed Central

Ventz, Steffen; Huttenhower, Curtis

2017-01-01

Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance) to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix. We also use a first-order Taylor expansion to approximate the transformation from the unobserved counts to the composition in order to investigate what characteristics of the unobserved counts can make the correlations more or less difficult to infer. On simulated datasets, we show that BAnOCC infers the true network as well as previous methods while offering the advantage of posterior inference. Larger and more realistic simulated datasets further showed that BAnOCC performs well as measured by type I and type II error rates. Finally, we apply BAnOCC to a microbial ecology dataset from the Human Microbiome Project, which in addition to reproducing established ecological results revealed unique, competition-based roles for Proteobacteria in multiple distinct habitats. PMID:29140991

Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach.

PubMed

Fernandes, G S; Bhattacharya, A; McWilliams, D F; Ingham, S L; Doherty, M; Zhang, W

2017-03-20

Twenty-five percent of the British population over the age of 50 years experiences knee pain. Knee pain can limit physical ability and cause distress and bears significant socioeconomic costs. The objectives of this study were to develop and validate the first risk prediction model for incident knee pain in the Nottingham community and validate this internally within the Nottingham cohort and externally within the Osteoarthritis Initiative (OAI) cohort. A total of 1822 participants from the Nottingham community who were at risk for knee pain were followed for 12 years. Of this cohort, two-thirds (n = 1203) were used to develop the risk prediction model, and one-third (n = 619) were used to validate the model. Incident knee pain was defined as pain on most days for at least 1 month in the past 12 months. Predictors were age, sex, body mass index, pain elsewhere, prior knee injury and knee alignment. A Bayesian logistic regression model was used to determine the probability of an OR >1. The Hosmer-Lemeshow χ 2 statistic (HLS) was used for calibration, and ROC curve analysis was used for discrimination. The OAI cohort from the United States was also used to examine the performance of the model. A risk prediction model for knee pain incidence was developed using a Bayesian approach. The model had good calibration, with an HLS of 7.17 (p = 0.52) and moderate discriminative ability (ROC 0.70) in the community. Individual scenarios are given using the model. However, the model had poor calibration (HLS 5866.28, p < 0.01) and poor discriminative ability (ROC 0.54) in the OAI cohort. To our knowledge, this is the first risk prediction model for knee pain, regardless of underlying structural changes of knee osteoarthritis, in the community using a Bayesian modelling approach. The model appears to work well in a community-based population but not in individuals with a higher risk for knee osteoarthritis, and it may provide a convenient tool for use in primary care to predict the risk of knee pain in the general population.

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

USDA-ARS?s Scientific Manuscript database

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

Quantitative Microbial Risk Assessment Tutorial Installation of Software for Watershed Modeling in Support of QMRA - Updated 2017

EPA Science Inventory

This tutorial provides instructions for accessing, retrieving, and downloading the following software to install on a host computer in support of Quantitative Microbial Risk Assessment (QMRA) modeling: • QMRA Installation • SDMProjectBuilder (which includes the Microbial ...

Bayesian joint modelling of benefit and risk in drug development.

PubMed

Costa, Maria J; Drury, Thomas

2018-05-01

To gain regulatory approval, a new medicine must demonstrate that its benefits outweigh any potential risks, ie, that the benefit-risk balance is favourable towards the new medicine. For transparency and clarity of the decision, a structured and consistent approach to benefit-risk assessment that quantifies uncertainties and accounts for underlying dependencies is desirable. This paper proposes two approaches to benefit-risk evaluation, both based on the idea of joint modelling of mixed outcomes that are potentially dependent at the subject level. Using Bayesian inference, the two approaches offer interpretability and efficiency to enhance qualitative frameworks. Simulation studies show that accounting for correlation leads to a more accurate assessment of the strength of evidence to support benefit-risk profiles of interest. Several graphical approaches are proposed that can be used to communicate the benefit-risk balance to project teams. Finally, the two approaches are illustrated in a case study using real clinical trial data. Copyright © 2018 John Wiley & Sons, Ltd.

Competing risk models in reliability systems, a weibull distribution model with bayesian analysis approach

NASA Astrophysics Data System (ADS)

Iskandar, Ismed; Satria Gondokaryono, Yudi

2016-02-01

In reliability theory, the most important problem is to determine the reliability of a complex system from the reliability of its components. The weakness of most reliability theories is that the systems are described and explained as simply functioning or failed. In many real situations, the failures may be from many causes depending upon the age and the environment of the system and its components. Another problem in reliability theory is one of estimating the parameters of the assumed failure models. The estimation may be based on data collected over censored or uncensored life tests. In many reliability problems, the failure data are simply quantitatively inadequate, especially in engineering design and maintenance system. The Bayesian analyses are more beneficial than the classical one in such cases. The Bayesian estimation analyses allow us to combine past knowledge or experience in the form of an apriori distribution with life test data to make inferences of the parameter of interest. In this paper, we have investigated the application of the Bayesian estimation analyses to competing risk systems. The cases are limited to the models with independent causes of failure by using the Weibull distribution as our model. A simulation is conducted for this distribution with the objectives of verifying the models and the estimators and investigating the performance of the estimators for varying sample size. The simulation data are analyzed by using Bayesian and the maximum likelihood analyses. The simulation results show that the change of the true of parameter relatively to another will change the value of standard deviation in an opposite direction. For a perfect information on the prior distribution, the estimation methods of the Bayesian analyses are better than those of the maximum likelihood. The sensitivity analyses show some amount of sensitivity over the shifts of the prior locations. They also show the robustness of the Bayesian analysis within the range between the true value and the maximum likelihood estimated value lines.

Post-Fire Spatial Patterns of Soil Nitrogen Mineralization and Microbial Abundance

PubMed Central

Smithwick, Erica A. H.; Naithani, Kusum J.; Balser, Teri C.; Romme, William H.; Turner, Monica G.

2012-01-01

Stand-replacing fires influence soil nitrogen availability and microbial community composition, which may in turn mediate post-fire successional dynamics and nutrient cycling. However, fires create patchiness at both local and landscape scales and do not result in consistent patterns of ecological dynamics. The objectives of this study were to (1) quantify the spatial structure of microbial communities in forest stands recently affected by stand-replacing fire and (2) determine whether microbial variables aid predictions of in situ net nitrogen mineralization rates in recently burned stands. The study was conducted in lodgepole pine (Pinus contorta var. latifolia) and Engelmann spruce/subalpine fir (Picea engelmannii/Abies lasiocarpa) forest stands that burned during summer 2000 in Greater Yellowstone (Wyoming, USA). Using a fully probabilistic spatial process model and Bayesian kriging, the spatial structure of microbial lipid abundance and fungi-to-bacteria ratios were found to be spatially structured within plots two years following fire (for most plots, autocorrelation range varied from 1.5 to 10.5 m). Congruence of spatial patterns among microbial variables, in situ net N mineralization, and cover variables was evident. Stepwise regression resulted in significant models of in situ net N mineralization and included variables describing fungal and bacterial abundance, although explained variance was low (R2<0.29). Unraveling complex spatial patterns of nutrient cycling and the biotic factors that regulate it remains challenging but is critical for explaining post-fire ecosystem function, especially in Greater Yellowstone, which is projected to experience increased fire frequencies by mid 21st Century. PMID:23226324

Decision Making and Learning while Taking Sequential Risks

ERIC Educational Resources Information Center

Pleskac, Timothy J.

2008-01-01

A sequential risk-taking paradigm used to identify real-world risk takers invokes both learning and decision processes. This article expands the paradigm to a larger class of tasks with different stochastic environments and different learning requirements. Generalizing a Bayesian sequential risk-taking model to the larger set of tasks clarifies…

Risk analysis of emergent water pollution accidents based on a Bayesian Network.

PubMed

Tang, Caihong; Yi, Yujun; Yang, Zhifeng; Sun, Jie

2016-01-01

To guarantee the security of water quality in water transfer channels, especially in open channels, analysis of potential emergent pollution sources in the water transfer process is critical. It is also indispensable for forewarnings and protection from emergent pollution accidents. Bridges above open channels with large amounts of truck traffic are the main locations where emergent accidents could occur. A Bayesian Network model, which consists of six root nodes and three middle layer nodes, was developed in this paper, and was employed to identify the possibility of potential pollution risk. Dianbei Bridge is reviewed as a typical bridge on an open channel of the Middle Route of the South to North Water Transfer Project where emergent traffic accidents could occur. Risk of water pollutions caused by leakage of pollutants into water is focused in this study. The risk for potential traffic accidents at the Dianbei Bridge implies a risk for water pollution in the canal. Based on survey data, statistical analysis, and domain specialist knowledge, a Bayesian Network model was established. The human factor of emergent accidents has been considered in this model. Additionally, this model has been employed to describe the probability of accidents and the risk level. The sensitive reasons for pollution accidents have been deduced. The case has also been simulated that sensitive factors are in a state of most likely to lead to accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.

Automated Bayesian model development for frequency detection in biological time series.

PubMed

Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J

2011-06-24

A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure.

Characterizing species at risk. II: Using Bayesian belief networks as decision support tools to determine species conservation categories under the Northwest Forest Plan.

Treesearch

B.G. Marcot; P.A. Hohenlohe; S. Morey; R. Holmes; R. Molina; M.C. Turley; M.H. Huff; J.A. Laurence

2006-01-01

We developed decision-aiding models as Bayesian belief networks (BBNs) that represented evaluation guidelines used to determine the appropriate conservation of hundreds of potentially rare species on federally-administered lands in the Pacific Northwest United States. The models were used in a structured assessment and paneling procedure as part of an adaptive...

Automated Bayesian model development for frequency detection in biological time series

PubMed Central

2011-01-01

Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure. PMID:21702910

Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis

NASA Technical Reports Server (NTRS)

Dezfuli, Homayoon; Kelly, Dana; Smith, Curtis; Vedros, Kurt; Galyean, William

2009-01-01

This document, Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis, is intended to provide guidelines for the collection and evaluation of risk and reliability-related data. It is aimed at scientists and engineers familiar with risk and reliability methods and provides a hands-on approach to the investigation and application of a variety of risk and reliability data assessment methods, tools, and techniques. This document provides both: A broad perspective on data analysis collection and evaluation issues. A narrow focus on the methods to implement a comprehensive information repository. The topics addressed herein cover the fundamentals of how data and information are to be used in risk and reliability analysis models and their potential role in decision making. Understanding these topics is essential to attaining a risk informed decision making environment that is being sought by NASA requirements and procedures such as 8000.4 (Agency Risk Management Procedural Requirements), NPR 8705.05 (Probabilistic Risk Assessment Procedures for NASA Programs and Projects), and the System Safety requirements of NPR 8715.3 (NASA General Safety Program Requirements).

On The Value at Risk Using Bayesian Mixture Laplace Autoregressive Approach for Modelling the Islamic Stock Risk Investment

NASA Astrophysics Data System (ADS)

Miftahurrohmah, Brina; Iriawan, Nur; Fithriasari, Kartika

2017-06-01

Stocks are known as the financial instruments traded in the capital market which have a high level of risk. Their risks are indicated by their uncertainty of their return which have to be accepted by investors in the future. The higher the risk to be faced, the higher the return would be gained. Therefore, the measurements need to be made against the risk. Value at Risk (VaR) as the most popular risk measurement method, is frequently ignore when the pattern of return is not uni-modal Normal. The calculation of the risks using VaR method with the Normal Mixture Autoregressive (MNAR) approach has been considered. This paper proposes VaR method couple with the Mixture Laplace Autoregressive (MLAR) that would be implemented for analysing the first three biggest capitalization Islamic stock return in JII, namely PT. Astra International Tbk (ASII), PT. Telekomunikasi Indonesia Tbk (TLMK), and PT. Unilever Indonesia Tbk (UNVR). Parameter estimation is performed by employing Bayesian Markov Chain Monte Carlo (MCMC) approaches.

Quantitative Microbial Risk Assessment Tutorial: HSPF Setup, Application, and Calibration of Flows and Microbial Fate and Transport on an Example Watershed

EPA Science Inventory

A Quantitative Microbial Risk Assessment (QMRA) infrastructure that automates the manual process of characterizing transport of pathogens and microorganisms, from the source of release to a point of exposure, has been developed by loosely configuring a set of modules and process-...

Analysis of regional scale risk to whirling disease in populations of Colorado and Rio Grande cutthroat trout using Bayesian belief network model

USGS Publications Warehouse

Kolb Ayre, Kimberley; Caldwell, Colleen A.; Stinson, Jonah; Landis, Wayne G.

2014-01-01

Introduction and spread of the parasite Myxobolus cerebralis, the causative agent of whirling disease, has contributed to the collapse of wild trout populations throughout the intermountain west. Of concern is the risk the disease may have on conservation and recovery of native cutthroat trout. We employed a Bayesian belief network to assess probability of whirling disease in Colorado River and Rio Grande cutthroat trout (Oncorhynchus clarkii pleuriticus and Oncorhynchus clarkii virginalis, respectively) within their current ranges in the southwest United States. Available habitat (as defined by gradient and elevation) for intermediate oligochaete worm host, Tubifex tubifex, exerted the greatest influence on the likelihood of infection, yet prevalence of stream barriers also affected the risk outcome. Management areas that had the highest likelihood of infected Colorado River cutthroat trout were in the eastern portion of their range, although the probability of infection was highest for populations in the southern, San Juan subbasin. Rio Grande cutthroat trout had a relatively low likelihood of infection, with populations in the southernmost Pecos management area predicted to be at greatest risk. The Bayesian risk assessment model predicted the likelihood of whirling disease infection from its principal transmission vector, fish movement, and suggested that barriers may be effective in reducing risk of exposure to native trout populations. Data gaps, especially with regard to location of spawning, highlighted the importance in developing monitoring plans that support future risk assessments and adaptive management for subspecies of cutthroat trout.

Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis-Hastings Markov Chain Monte Carlo algorithm

NASA Astrophysics Data System (ADS)

Wang, Hongrui; Wang, Cheng; Wang, Ying; Gao, Xiong; Yu, Chen

2017-06-01

This paper presents a Bayesian approach using Metropolis-Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLE confidence interval and thus more precise estimation by using the related information from regional gage stations. The Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.

Forensic analysis of the microbiome of phones and shoes

DOE PAGES

Lax, Simon; Hampton-Marcell, Jarrad T.; Gibbons, Sean M.; ...

2015-05-12

Background: Microbial interaction between human-associated objects and the environments we inhabit may have forensic implications, and the extent to which microbes are shared between individuals inhabiting the same space may be relevant to human health and disease transmission. In this study, two participants sampled the front and back of their cell phones, four different locations on the soles of their shoes, and the floor beneath them every waking hour over a 2-day period. A further 89 participants took individual samples of their shoes and phones at three different scientific conferences. Results: Samples taken from different surface types maintained significantly differentmore » microbial community structures. The impact of the floor microbial community on that of the shoe environments was strong and immediate, as evidenced by Procrustes analysis of shoe replicates and significant correlation between shoe and floor samples taken at the same time point. Supervised learning was highly effective at determining which participant had taken a given shoe or phone sample, and a Bayesian method was able to determine which participant had taken each shoe sample based entirely on its similarity to the floor samples. Both shoe and phone samples taken by conference participants clustered into distinct groups based on location, though much more so when an unweighted distance metric was used, suggesting sharing of low-abundance microbial taxa between individuals inhabiting the same space. In conclusion, correlations between microbial community sources and sinks allow for inference of the interactions between humans and their environment.« less

Forensic analysis of the microbiome of phones and shoes

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

Lax, Simon; Hampton-Marcell, Jarrad T.; Gibbons, Sean M.

Background: Microbial interaction between human-associated objects and the environments we inhabit may have forensic implications, and the extent to which microbes are shared between individuals inhabiting the same space may be relevant to human health and disease transmission. In this study, two participants sampled the front and back of their cell phones, four different locations on the soles of their shoes, and the floor beneath them every waking hour over a 2-day period. A further 89 participants took individual samples of their shoes and phones at three different scientific conferences. Results: Samples taken from different surface types maintained significantly differentmore » microbial community structures. The impact of the floor microbial community on that of the shoe environments was strong and immediate, as evidenced by Procrustes analysis of shoe replicates and significant correlation between shoe and floor samples taken at the same time point. Supervised learning was highly effective at determining which participant had taken a given shoe or phone sample, and a Bayesian method was able to determine which participant had taken each shoe sample based entirely on its similarity to the floor samples. Both shoe and phone samples taken by conference participants clustered into distinct groups based on location, though much more so when an unweighted distance metric was used, suggesting sharing of low-abundance microbial taxa between individuals inhabiting the same space. In conclusion, correlations between microbial community sources and sinks allow for inference of the interactions between humans and their environment.« less

Quantitative analysis of factors that affect oil pipeline network accident based on Bayesian networks: A case study in China

NASA Astrophysics Data System (ADS)

Zhang, Chao; Qin, Ting Xin; Huang, Shuai; Wu, Jian Song; Meng, Xin Yan

2018-06-01

Some factors can affect the consequences of oil pipeline accident and their effects should be analyzed to improve emergency preparation and emergency response. Although there are some qualitative analysis models of risk factors' effects, the quantitative analysis model still should be researched. In this study, we introduce a Bayesian network (BN) model of risk factors' effects analysis in an oil pipeline accident case that happened in China. The incident evolution diagram is built to identify the risk factors. And the BN model is built based on the deployment rule for factor nodes in BN and the expert knowledge by Dempster-Shafer evidence theory. Then the probabilities of incident consequences and risk factors' effects can be calculated. The most likely consequences given by this model are consilient with the case. Meanwhile, the quantitative estimations of risk factors' effects may provide a theoretical basis to take optimal risk treatment measures for oil pipeline management, which can be used in emergency preparation and emergency response.

Source-to-Outcome Microbial Exposure and Risk Modeling Framework

EPA Science Inventory

A Quantitative Microbial Risk Assessment (QMRA) is a computer-based data-delivery and modeling approach that integrates interdisciplinary fate/transport, exposure, and impact models and databases to characterize potential health impacts/risks due to pathogens. As such, a QMRA ex...

A bayesian approach to classification criteria for spectacled eiders

USGS Publications Warehouse

Taylor, B.L.; Wade, P.R.; Stehn, R.A.; Cochrane, J.F.

1996-01-01

To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon-Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under- or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.

Joint distribution approaches to simultaneously quantifying benefit and risk.

PubMed

Shaffer, Michele L; Watterberg, Kristi L

2006-10-12

The benefit-risk ratio has been proposed to measure the tradeoff between benefits and risks of two therapies for a single binary measure of efficacy and a single adverse event. The ratio is calculated from the difference in risk and difference in benefit between therapies. Small sample sizes or expected differences in benefit or risk can lead to no solution or problematic solutions for confidence intervals. Alternatively, using the joint distribution of benefit and risk, confidence regions for the differences in risk and benefit can be constructed in the benefit-risk plane. The information in the joint distribution can be summarized by choosing regions of interest in this plane. Using Bayesian methodology provides a very flexible framework for summarizing information in the joint distribution. Data from a National Institute of Child Health & Human Development trial of hydrocortisone illustrate the construction of confidence regions and regions of interest in the benefit-risk plane, where benefit is survival without supplemental oxygen at 36 weeks postmenstrual age, and risk is gastrointestinal perforation. For the subgroup of infants exposed to chorioamnionitis the confidence interval based on the benefit-risk ratio is wide (Benefit-risk ratio: 1.52; 90% confidence interval: 0.23 to 5.25). Choosing regions of appreciable risk and acceptable risk in the benefit-risk plane confirms the uncertainty seen in the wide confidence interval for the benefit-risk ratio--there is a greater than 50% chance of falling into the region of acceptable risk--while visually allowing the uncertainty in risk and benefit to be shown separately. Applying Bayesian methodology, an incremental net health benefit analysis shows there is a 72% chance of having a positive incremental net benefit if hydrocortisone is used in place of placebo if one is willing to incur at most one gastrointestinal perforation for each additional infant that survives without supplemental oxygen. If the benefit-risk ratio is presented, the joint distribution of benefit and risk also should be shown. These regions avoid the ambiguity associated with collapsing benefit and risk to a single dimension. Bayesian methods allow even greater flexibility in simultaneously quantifying benefit and risk.

Microbial Source Tracking: Current and Future Molecular Tools in Microbial Water Quality Forensics

EPA Science Inventory

Current regulations in the United States stipulate that the microbial quality of waters used for consumption and recreational activities should be determined regularly by measuring microbial indicators of fecal pollution. Hence, the microbial risk associated with these waters is...

A cross-sectional study examining the prevalence and risk factors for anti-microbial-resistant generic Escherichia coli in domestic dogs that frequent dog parks in three cities in south-western Ontario, Canada.

PubMed

Procter, T D; Pearl, D L; Finley, R L; Leonard, E K; Janecko, N; Reid-Smith, R J; Weese, J S; Peregrine, A S; Sargeant, J M

2014-06-01

Anti-microbial resistance can threaten health by limiting treatment options and increasing the risk of hospitalization and severity of infection. Companion animals can shed anti-microbial-resistant bacteria that may result in the exposure of other dogs and humans to anti-microbial-resistant genes. The prevalence of anti-microbial-resistant generic Escherichia coli in the faeces of dogs that visited dog parks in south-western Ontario was examined and risk factors for shedding anti-microbial-resistant generic E. coli identified. From May to August 2009, canine faecal samples were collected at ten dog parks in three cities in south-western Ontario, Canada. Owners completed a questionnaire related to pet characteristics and management factors including recent treatment with antibiotics. Faecal samples were collected from 251 dogs, and 189 surveys were completed. Generic E. coli was isolated from 237 of the faecal samples, and up to three isolates per sample were tested for anti-microbial susceptibility. Eighty-nine percent of isolates were pan-susceptible; 82.3% of dogs shed isolates that were pan-susceptible. Multiclass resistance was detected in 7.2% of the isolates from 10.1% of the dogs. Based on multilevel multivariable logistic regression, a risk factor for the shedding of generic E. coli resistant to ampicillin was attending dog day care. Risk factors for the shedding of E. coli resistant to at least one anti-microbial included attending dog day care and being a large mixed breed dog, whereas consumption of commercial dry and home cooked diets was protective factor. In a multilevel multivariable model for the shedding of multiclass-resistant E. coli, exposure to compost and being a large mixed breed dog were risk factors, while consumption of a commercial dry diet was a sparing factor. Pet dogs are a potential reservoir of anti-microbial-resistant generic E. coli; some dog characteristics and management factors are associated with the prevalence of anti-microbial-resistant generic E. coli in dogs. © 2013 Blackwell Verlag GmbH.

Robust evaluation of performance monitoring options for ozone disinfection in water recycling using Bayesian analysis.

PubMed

Carvajal, Guido; Branch, Amos; Michel, Philipp; Sisson, Scott A; Roser, David J; Drewes, Jörg E; Khan, Stuart J

2017-11-01

Ozonation of wastewater has gained popularity because of its effectiveness in removing colour, UV absorbance, trace organic chemicals, and pathogens. Due to the rapid reaction of ozone with organic compounds, dissolved ozone is often not measurable and therefore, the common disinfection controlling parameter, concentration integrated over contact time (CT) cannot be obtained. In such cases, alternative parameters have been shown to be useful as surrogate measures for microbial removal including change in UV 254 absorbance (ΔUVA), change in total fluorescence (ΔTF), or O 3 :TOC (or O 3 :DOC). Although these measures have shown promise, a number of caveats remain. These include uncertainties in the associations between these measurements and microbial inactivation. Furthermore, previous use of seeded microorganisms with higher disinfection sensitivity compared to autochthonous microorganisms could lead to overestimation of appropriate log credits. In our study, secondary treated wastewater from a full-scale plant was ozonated in a bench-scale reactor using five increasing ozone doses. During the experiments, removal of four indigenous microbial indicators representing viruses, bacteria and protozoa were monitored concurrent with ΔUVA, ΔTF, O 3 :DOC and PARAFAC derived components. Bayesian methods were used to fit linear regression models, and the uncertainty in the posterior predictive distributions and slopes provided a comparison between previously reported results and those reported here. Combined results indicated that all surrogate parameters were useful in predicting the removal of microorganisms, with a better fit to the models using ΔUVA, ΔTF in most cases. Average adjusted determination coefficients for fitted models were high (R 2 adjusted >0.47). With ΔUVA, one unit decrease in LRV corresponded with a UVA mean reduction of 15-20% for coliforms, 59% for C. perfringens spores, and 11% for somatic coliphages. With ΔTF, a one unit decrease in LRV corresponded with a TF mean reduction of 18-23% for coliforms, 71% for C. perfringens spores, and 14% for somatic coliphages. Compared to previous studies also analysed, our results suggest that microbial reductions were more conservative for autochthonous than for seeded microorganisms. The findings of our study suggested that site-specific analyses should be conducted to generate models with lower uncertainty and that indigenous microorganisms are useful for the measurement of system performance even when censored observations are obtained. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Application of Bayesian Approach in Modeling Risk of Death in an Intensive Care Unit

PubMed Central

Wong, Rowena Syn Yin; Ismail, Noor Azina

2016-01-01

Background and Objectives There are not many studies that attempt to model intensive care unit (ICU) risk of death in developing countries, especially in South East Asia. The aim of this study was to propose and describe application of a Bayesian approach in modeling in-ICU deaths in a Malaysian ICU. Methods This was a prospective study in a mixed medical-surgery ICU in a multidisciplinary tertiary referral hospital in Malaysia. Data collection included variables that were defined in Acute Physiology and Chronic Health Evaluation IV (APACHE IV) model. Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied in the development of four multivariate logistic regression predictive models for the ICU, where the main outcome measure was in-ICU mortality risk. The performance of the models were assessed through overall model fit, discrimination and calibration measures. Results from the Bayesian models were also compared against results obtained using frequentist maximum likelihood method. Results The study involved 1,286 consecutive ICU admissions between January 1, 2009 and June 30, 2010, of which 1,111 met the inclusion criteria. Patients who were admitted to the ICU were generally younger, predominantly male, with low co-morbidity load and mostly under mechanical ventilation. The overall in-ICU mortality rate was 18.5% and the overall mean Acute Physiology Score (APS) was 68.5. All four models exhibited good discrimination, with area under receiver operating characteristic curve (AUC) values approximately 0.8. Calibration was acceptable (Hosmer-Lemeshow p-values > 0.05) for all models, except for model M3. Model M1 was identified as the model with the best overall performance in this study. Conclusion Four prediction models were proposed, where the best model was chosen based on its overall performance in this study. This study has also demonstrated the promising potential of the Bayesian MCMC approach as an alternative in the analysis and modeling of in-ICU mortality outcomes. PMID:27007413

An Application of Bayesian Approach in Modeling Risk of Death in an Intensive Care Unit.

PubMed

Wong, Rowena Syn Yin; Ismail, Noor Azina

2016-01-01

There are not many studies that attempt to model intensive care unit (ICU) risk of death in developing countries, especially in South East Asia. The aim of this study was to propose and describe application of a Bayesian approach in modeling in-ICU deaths in a Malaysian ICU. This was a prospective study in a mixed medical-surgery ICU in a multidisciplinary tertiary referral hospital in Malaysia. Data collection included variables that were defined in Acute Physiology and Chronic Health Evaluation IV (APACHE IV) model. Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied in the development of four multivariate logistic regression predictive models for the ICU, where the main outcome measure was in-ICU mortality risk. The performance of the models were assessed through overall model fit, discrimination and calibration measures. Results from the Bayesian models were also compared against results obtained using frequentist maximum likelihood method. The study involved 1,286 consecutive ICU admissions between January 1, 2009 and June 30, 2010, of which 1,111 met the inclusion criteria. Patients who were admitted to the ICU were generally younger, predominantly male, with low co-morbidity load and mostly under mechanical ventilation. The overall in-ICU mortality rate was 18.5% and the overall mean Acute Physiology Score (APS) was 68.5. All four models exhibited good discrimination, with area under receiver operating characteristic curve (AUC) values approximately 0.8. Calibration was acceptable (Hosmer-Lemeshow p-values > 0.05) for all models, except for model M3. Model M1 was identified as the model with the best overall performance in this study. Four prediction models were proposed, where the best model was chosen based on its overall performance in this study. This study has also demonstrated the promising potential of the Bayesian MCMC approach as an alternative in the analysis and modeling of in-ICU mortality outcomes.

A quantitative microbial risk assessment for center pivot irrigation of dairy wastewaters

USDA-ARS?s Scientific Manuscript database

In the western United States where livestock wastewaters are commonly land applied, there are concerns over individuals being exposed to airborne pathogens. In response, a quantitative microbial risk assessment (QMRA) was performed to estimate infectious risks from inhaling pathogens aerosolized dur...

An integrated environmental modeling framework for performing Quantitative Microbial Risk Assessments

EPA Science Inventory

Standardized methods are often used to assess the likelihood of a human-health effect from exposure to a specified hazard, and inform opinions and decisions about risk management and communication. A Quantitative Microbial Risk Assessment (QMRA) is specifically adapted to detail ...

A small-area ecologic study of myocardial infarction, neighborhood deprivation, and sex: a Bayesian modeling approach.

PubMed

Deguen, Séverine; Lalloue, Benoît; Bard, Denis; Havard, Sabrina; Arveiler, Dominique; Zmirou-Navier, Denis

2010-07-01

Socioeconomic inequalities in the risk of coronary heart disease (CHD) are well documented for men and women. CHD incidence is greater for men but its association with socioeconomic status is usually found to be stronger among women. We explored the sex-specific association between neighborhood deprivation level and the risk of myocardial infarction (MI) at a small-area scale. We studied 1193 myocardial infarction events in people aged 35-74 years in the Strasbourg metropolitan area, France (2000-2003). We used a deprivation index to assess the neighborhood deprivation level. To take into account spatial dependence and the variability of MI rates due to the small number of events, we used a hierarchical Bayesian modeling approach. We fitted hierarchical Bayesian models to estimate sex-specific relative and absolute MI risks across deprivation categories. We tested departure from additive joint effects of deprivation and sex. The risk of MI increased with the deprivation level for both sexes, but was higher for men for all deprivation classes. Relative rates increased along the deprivation scale more steadily for women and followed a different pattern: linear for men and nonlinear for women. Our data provide evidence of effect modification, with departure from an additive joint effect of deprivation and sex. We document sex differences in the socioeconomic gradient of MI risk in Strasbourg. Women appear more susceptible at levels of extreme deprivation; this result is not a chance finding, given the large difference in event rates between men and women.

Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis–Hastings Markov Chain Monte Carlo algorithm

DOE PAGES

Wang, Hongrui; Wang, Cheng; Wang, Ying; ...

2017-04-05

This paper presents a Bayesian approach using Metropolis-Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLEmore » confidence interval and thus more precise estimation by using the related information from regional gage stations. As a result, the Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.« less

Using Bayesian Networks for Candidate Generation in Consistency-based Diagnosis

NASA Technical Reports Server (NTRS)

Narasimhan, Sriram; Mengshoel, Ole

2008-01-01

Consistency-based diagnosis relies heavily on the assumption that discrepancies between model predictions and sensor observations can be detected accurately. When sources of uncertainty like sensor noise and model abstraction exist robust schemes have to be designed to make a binary decision on whether predictions are consistent with observations. This risks the occurrence of false alarms and missed alarms when an erroneous decision is made. Moreover when multiple sensors (with differing sensing properties) are available the degree of match between predictions and observations can be used to guide the search for fault candidates. In this paper we propose a novel approach to handle this problem using Bayesian networks. In the consistency- based diagnosis formulation, automatically generated Bayesian networks are used to encode a probabilistic measure of fit between predictions and observations. A Bayesian network inference algorithm is used to compute most probable fault candidates.

Learning Bayesian Networks from Correlated Data

NASA Astrophysics Data System (ADS)

Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H.; Perls, Thomas T.; Sebastiani, Paola

2016-05-01

Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.

Modeling epilepsy disparities among ethnic groups in Philadelphia, PA

PubMed Central

Wheeler, David C.; Waller, Lance A.; Elliott, John O.

2014-01-01

SUMMARY The Centers for Disease Control and Prevention defined epilepsy as an emerging public health issue in a recent report and emphasized the importance of epilepsy studies in minorities and people of low socioeconomic status. Previous research has suggested that the incidence rate for epilepsy is positively associated with various measures of social and economic disadvantage. In response, we utilize hierarchical Bayesian models to analyze health disparities in epilepsy and seizure risks among multiple ethnicities in the city of Philadelphia, Pennsylvania. The goals of the analysis are to highlight any overall significant disparities in epilepsy risks between the populations of Caucasians, African Americans, and Hispanics in the study area during the years 2002–2004 and to visualize the spatial pattern of epilepsy risks by ethnicity to indicate where certain ethnic populations were most adversely affected by epilepsy within the study area. Results of the Bayesian model indicate that Hispanics have the highest epilepsy risk overall, followed by African Americans, and then Caucasians. There are significant increases in relative risk for both African Americans and Hispanics when compared with Caucasians, as indicated by the posterior mean estimates of 2.09 with a 95 per cent credible interval of (1.67, 2.62) for African Americans and 2.97 with a 95 per cent credible interval of (2.37, 3.71) for Hispanics. Results also demonstrate that using a Bayesian analysis in combination with geographic information system (GIS) technology can reveal spatial patterns in patient data and highlight areas of disparity in epilepsy risk among subgroups of the population. PMID:18381676

Application of Poisson random effect models for highway network screening.

PubMed

Jiang, Ximiao; Abdel-Aty, Mohamed; Alamili, Samer

2014-02-01

In recent years, Bayesian random effect models that account for the temporal and spatial correlations of crash data became popular in traffic safety research. This study employs random effect Poisson Log-Normal models for crash risk hotspot identification. Both the temporal and spatial correlations of crash data were considered. Potential for Safety Improvement (PSI) were adopted as a measure of the crash risk. Using the fatal and injury crashes that occurred on urban 4-lane divided arterials from 2006 to 2009 in the Central Florida area, the random effect approaches were compared to the traditional Empirical Bayesian (EB) method and the conventional Bayesian Poisson Log-Normal model. A series of method examination tests were conducted to evaluate the performance of different approaches. These tests include the previously developed site consistence test, method consistence test, total rank difference test, and the modified total score test, as well as the newly proposed total safety performance measure difference test. Results show that the Bayesian Poisson model accounting for both temporal and spatial random effects (PTSRE) outperforms the model that with only temporal random effect, and both are superior to the conventional Poisson Log-Normal model (PLN) and the EB model in the fitting of crash data. Additionally, the method evaluation tests indicate that the PTSRE model is significantly superior to the PLN model and the EB model in consistently identifying hotspots during successive time periods. The results suggest that the PTSRE model is a superior alternative for road site crash risk hotspot identification. Copyright © 2013 Elsevier Ltd. All rights reserved.

Bayesian Networks for enterprise risk assessment

NASA Astrophysics Data System (ADS)

Bonafede, C. E.; Giudici, P.

2007-08-01

According to different typologies of activity and priority, risks can assume diverse meanings and it can be assessed in different ways. Risk, in general, is measured in terms of a probability combination of an event (frequency) and its consequence (impact). To estimate the frequency and the impact (severity) historical data or expert opinions (either qualitative or quantitative data) are used. Moreover, qualitative data must be converted in numerical values or bounds to be used in the model. In the case of enterprise risk assessment the considered risks are, for instance, strategic, operational, legal and of image, which many times are difficult to be quantified. So in most cases only expert data, gathered by scorecard approaches, are available for risk analysis. The Bayesian Networks (BNs) are a useful tool to integrate different information and in particular to study the risk's joint distribution by using data collected from experts. In this paper we want to show a possible approach for building a BN in the particular case in which only prior probabilities of node states and marginal correlations between nodes are available, and when the variables have only two states.

An integrated environmental modeling framework for performing quantitative microbial risk assessments

USDA-ARS?s Scientific Manuscript database

Standardized methods are often used to assess the likelihood of a human-health effect from exposure to a specified hazard, and inform opinions and decisions about risk management and communication. A Quantitative Microbial Risk Assessment (QMRA) is specifically adapted to detail potential human-heal...

Characterization of a Saccharomyces cerevisiae fermentation process for production of a therapeutic recombinant protein using a multivariate Bayesian approach.

PubMed

Fu, Zhibiao; Baker, Daniel; Cheng, Aili; Leighton, Julie; Appelbaum, Edward; Aon, Juan

2016-05-01

The principle of quality by design (QbD) has been widely applied to biopharmaceutical manufacturing processes. Process characterization is an essential step to implement the QbD concept to establish the design space and to define the proven acceptable ranges (PAR) for critical process parameters (CPPs). In this study, we present characterization of a Saccharomyces cerevisiae fermentation process using risk assessment analysis, statistical design of experiments (DoE), and the multivariate Bayesian predictive approach. The critical quality attributes (CQAs) and CPPs were identified with a risk assessment. The statistical model for each attribute was established using the results from the DoE study with consideration given to interactions between CPPs. Both the conventional overlapping contour plot and the multivariate Bayesian predictive approaches were used to establish the region of process operating conditions where all attributes met their specifications simultaneously. The quantitative Bayesian predictive approach was chosen to define the PARs for the CPPs, which apply to the manufacturing control strategy. Experience from the 10,000 L manufacturing scale process validation, including 64 continued process verification batches, indicates that the CPPs remain under a state of control and within the established PARs. The end product quality attributes were within their drug substance specifications. The probability generated with the Bayesian approach was also used as a tool to assess CPP deviations. This approach can be extended to develop other production process characterization and quantify a reliable operating region. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:799-812, 2016. © 2016 American Institute of Chemical Engineers.

Bayesian Inference on Malignant Breast Cancer in Nigeria: A Diagnosis of MCMC Convergence

PubMed Central

Ogunsakin, Ropo Ebenezer; Siaka, Lougue

2017-01-01

Background: There has been no previous study to classify malignant breast tumor in details based on Markov Chain Monte Carlo (MCMC) convergence in Western, Nigeria. This study therefore aims to profile patients living with benign and malignant breast tumor in two different hospitals among women of Western Nigeria, with a focus on prognostic factors and MCMC convergence. Materials and Methods: A hospital-based record was used to identify prognostic factors for malignant breast cancer among women of Western Nigeria. This paper describes Bayesian inference and demonstrates its usage to estimation of parameters of the logistic regression via Markov Chain Monte Carlo (MCMC) algorithm. The result of the Bayesian approach is compared with the classical statistics. Results: The mean age of the respondents was 42.2 ±16.6 years with 52% of the women aged between 35-49 years. The results of both techniques suggest that age and women with at least high school education have a significantly higher risk of being diagnosed with malignant breast tumors than benign breast tumors. The results also indicate a reduction of standard errors is associated with the coefficients obtained from the Bayesian approach. In addition, simulation result reveal that women with at least high school are 1.3 times more at risk of having malignant breast lesion in western Nigeria compared to benign breast lesion. Conclusion: We concluded that more efforts are required towards creating awareness and advocacy campaigns on how the prevalence of malignant breast lesions can be reduced, especially among women. The application of Bayesian produces precise estimates for modeling malignant breast cancer. PMID:29072396

Bayesian network learning for natural hazard assessments

NASA Astrophysics Data System (ADS)

Vogel, Kristin

2016-04-01

Even though quite different in occurrence and consequences, from a modelling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding. On top of the uncertainty about the modelling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Thus, for reliable natural hazard assessments it is crucial not only to capture and quantify involved uncertainties, but also to express and communicate uncertainties in an intuitive way. Decision-makers, who often find it difficult to deal with uncertainties, might otherwise return to familiar (mostly deterministic) proceedings. In the scope of the DFG research training group „NatRiskChange" we apply the probabilistic framework of Bayesian networks for diverse natural hazard and vulnerability studies. The great potential of Bayesian networks was already shown in previous natural hazard assessments. Treating each model component as random variable, Bayesian networks aim at capturing the joint distribution of all considered variables. Hence, each conditional distribution of interest (e.g. the effect of precautionary measures on damage reduction) can be inferred. The (in-)dependencies between the considered variables can be learned purely data driven or be given by experts. Even a combination of both is possible. By translating the (in-)dependences into a graph structure, Bayesian networks provide direct insights into the workings of the system and allow to learn about the underlying processes. Besides numerous studies on the topic, learning Bayesian networks from real-world data remains challenging. In previous studies, e.g. on earthquake induced ground motion and flood damage assessments, we tackled the problems arising with continuous variables and incomplete observations. Further studies rise the challenge of relying on very small data sets. Since parameter estimates for complex models based on few observations are unreliable, it is necessary to focus on simplified, yet still meaningful models. A so called Markov Blanket approach is developed to identify the most relevant model components and to construct a simple Bayesian network based on those findings. Since the proceeding is completely data driven, it can easily be transferred to various applications in natural hazard domains. This study is funded by the Deutsche Forschungsgemeinschaft (DFG) within the research training programme GRK 2043/1 "NatRiskChange - Natural hazards and risks in a changing world" at Potsdam University.

Outdoor fine particles and nonfatal strokes: systematic review and meta-analysis.

PubMed

Shin, Hwashin H; Fann, Neal; Burnett, Richard T; Cohen, Aaron; Hubbell, Bryan J

2014-11-01

Epidemiologic studies find that long- and short-term exposure to fine particles (PM2.5) is associated with adverse cardiovascular outcomes, including ischemic and hemorrhagic strokes. However, few systematic reviews or meta-analyses have synthesized these results. We reviewed epidemiologic studies that estimated the risks of nonfatal strokes attributable to ambient PM2.5. To pool risks among studies we used a random-effects model and 2 Bayesian approaches. The first Bayesian approach assumes a normal prior that allows risks to be zero, positive or negative. The second assumes a gamma prior, where risks can only be positive. This second approach is proposed when the number of studies pooled is small, and there is toxicological or clinical literature to support a causal relation. We identified 20 studies suitable for quantitative meta-analysis. Evidence for publication bias is limited. The frequentist meta-analysis produced pooled risk ratios of 1.06 (95% confidence interval = 1.00-1.13) and 1.007 (1.003-1.010) for long- and short-term effects, respectively. The Bayesian meta-analysis found a posterior mean risk ratio of 1.08 (95% posterior interval = 0.96-1.26) and 1.008 (1.003-1.013) from a normal prior, and of 1.05 (1.02-1.10) and 1.008 (1.004-1.013) from a gamma prior, for long- and short-term effects, respectively, per 10 μg/m PM2.5. Sufficient evidence exists to develop a concentration-response relation for short- and long-term exposures to PM2.5 and stroke incidence. Long-term exposures to PM2.5 result in a higher risk ratio than short-term exposures, regardless of the pooling method. The evidence for short-term PM2.5-related ischemic stroke is especially strong.

Quantitative Microbial Risk Assessment Tutorial - Primer

EPA Science Inventory

This document provides a Quantitative Microbial Risk Assessment (QMRA) primer that organizes QMRA tutorials. The tutorials describe functionality of a QMRA infrastructure, guide the user through software use and assessment options, provide step-by-step instructions for implementi...

Quantification of uncertainty in flood risk assessment for flood protection planning: a Bayesian approach

NASA Astrophysics Data System (ADS)

Dittes, Beatrice; Špačková, Olga; Ebrahimian, Negin; Kaiser, Maria; Rieger, Wolfgang; Disse, Markus; Straub, Daniel

2017-04-01

Flood risk estimates are subject to significant uncertainties, e.g. due to limited records of historic flood events, uncertainty in flood modeling, uncertain impact of climate change or uncertainty in the exposure and loss estimates. In traditional design of flood protection systems, these uncertainties are typically just accounted for implicitly, based on engineering judgment. In the AdaptRisk project, we develop a fully quantitative framework for planning of flood protection systems under current and future uncertainties using quantitative pre-posterior Bayesian decision analysis. In this contribution, we focus on the quantification of the uncertainties and study their relative influence on the flood risk estimate and on the planning of flood protection systems. The following uncertainty components are included using a Bayesian approach: 1) inherent and statistical (i.e. limited record length) uncertainty; 2) climate uncertainty that can be learned from an ensemble of GCM-RCM models; 3) estimates of climate uncertainty components not covered in 2), such as bias correction, incomplete ensemble, local specifics not captured by the GCM-RCM models; 4) uncertainty in the inundation modelling; 5) uncertainty in damage estimation. We also investigate how these uncertainties are possibly reduced in the future when new evidence - such as new climate models, observed extreme events, and socio-economic data - becomes available. Finally, we look into how this new evidence influences the risk assessment and effectivity of flood protection systems. We demonstrate our methodology for a pre-alpine catchment in southern Germany: the Mangfall catchment in Bavaria that includes the city of Rosenheim, which suffered significant losses during the 2013 flood event.

Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel-Induced Pipeline Damage.

PubMed

Zhang, Limao; Wu, Xianguo; Qin, Yawei; Skibniewski, Miroslaw J; Liu, Wenli

2016-02-01

Tunneling excavation is bound to produce significant disturbances to surrounding environments, and the tunnel-induced damage to adjacent underground buried pipelines is of considerable importance for geotechnical practice. A fuzzy Bayesian networks (FBNs) based approach for safety risk analysis is developed in this article with detailed step-by-step procedures, consisting of risk mechanism analysis, the FBN model establishment, fuzzification, FBN-based inference, defuzzification, and decision making. In accordance with the failure mechanism analysis, a tunnel-induced pipeline damage model is proposed to reveal the cause-effect relationships between the pipeline damage and its influential variables. In terms of the fuzzification process, an expert confidence indicator is proposed to reveal the reliability of the data when determining the fuzzy probability of occurrence of basic events, with both the judgment ability level and the subjectivity reliability level taken into account. By means of the fuzzy Bayesian inference, the approach proposed in this article is capable of calculating the probability distribution of potential safety risks and identifying the most likely potential causes of accidents under both prior knowledge and given evidence circumstances. A case concerning the safety analysis of underground buried pipelines adjacent to the construction of the Wuhan Yangtze River Tunnel is presented. The results demonstrate the feasibility of the proposed FBN approach and its application potential. The proposed approach can be used as a decision tool to provide support for safety assurance and management in tunnel construction, and thus increase the likelihood of a successful project in a complex project environment. © 2015 Society for Risk Analysis.

Social deprivation, inequality, and the neighborhood-level incidence of psychotic syndromes in East London.

PubMed

Kirkbride, James B; Jones, Peter B; Ullrich, Simone; Coid, Jeremy W

2014-01-01

Although urban birth, upbringing, and living are associated with increased risk of nonaffective psychotic disorders, few studies have used appropriate multilevel techniques accounting for spatial dependency in risk to investigate social, economic, or physical determinants of psychosis incidence. We adopted Bayesian hierarchical modeling to investigate the sociospatial distribution of psychosis risk in East London for DSM-IV nonaffective and affective psychotic disorders, ascertained over a 2-year period in the East London first-episode psychosis study. We included individual and environmental data on 427 subjects experiencing first-episode psychosis to estimate the incidence of disorder across 56 neighborhoods, having standardized for age, sex, ethnicity, and socioeconomic status. A Bayesian model that included spatially structured neighborhood-level random effects identified substantial unexplained variation in nonaffective psychosis risk after controlling for individual-level factors. This variation was independently associated with greater levels of neighborhood income inequality (SD increase in inequality: Bayesian relative risks [RR]: 1.25; 95% CI: 1.04-1.49), absolute deprivation (RR: 1.28; 95% CI: 1.08-1.51) and population density (RR: 1.18; 95% CI: 1.00-1.41). Neighborhood ethnic composition effects were associated with incidence of nonaffective psychosis for people of black Caribbean and black African origin. No variation in the spatial distribution of the affective psychoses was identified, consistent with the possibility of differing etiological origins of affective and nonaffective psychoses. Our data suggest that both absolute and relative measures of neighborhood social composition are associated with the incidence of nonaffective psychosis. We suggest these associations are consistent with a role for social stressors in psychosis risk, particularly when people live in more unequal communities.

Causal modelling applied to the risk assessment of a wastewater discharge.

PubMed

Paul, Warren L; Rokahr, Pat A; Webb, Jeff M; Rees, Gavin N; Clune, Tim S

2016-03-01

Bayesian networks (BNs), or causal Bayesian networks, have become quite popular in ecological risk assessment and natural resource management because of their utility as a communication and decision-support tool. Since their development in the field of artificial intelligence in the 1980s, however, Bayesian networks have evolved and merged with structural equation modelling (SEM). Unlike BNs, which are constrained to encode causal knowledge in conditional probability tables, SEMs encode this knowledge in structural equations, which is thought to be a more natural language for expressing causal information. This merger has clarified the causal content of SEMs and generalised the method such that it can now be performed using standard statistical techniques. As it was with BNs, the utility of this new generation of SEM in ecological risk assessment will need to be demonstrated with examples to foster an understanding and acceptance of the method. Here, we applied SEM to the risk assessment of a wastewater discharge to a stream, with a particular focus on the process of translating a causal diagram (conceptual model) into a statistical model which might then be used in the decision-making and evaluation stages of the risk assessment. The process of building and testing a spatial causal model is demonstrated using data from a spatial sampling design, and the implications of the resulting model are discussed in terms of the risk assessment. It is argued that a spatiotemporal causal model would have greater external validity than the spatial model, enabling broader generalisations to be made regarding the impact of a discharge, and greater value as a tool for evaluating the effects of potential treatment plant upgrades. Suggestions are made on how the causal model could be augmented to include temporal as well as spatial information, including suggestions for appropriate statistical models and analyses.

Spatial heterogeneity and risk factors for stunting among children under age five in Ethiopia: A Bayesian geo-statistical model.

PubMed

Hagos, Seifu; Hailemariam, Damen; WoldeHanna, Tasew; Lindtjørn, Bernt

2017-01-01

Understanding the spatial distribution of stunting and underlying factors operating at meso-scale is of paramount importance for intervention designing and implementations. Yet, little is known about the spatial distribution of stunting and some discrepancies are documented on the relative importance of reported risk factors. Therefore, the present study aims at exploring the spatial distribution of stunting at meso- (district) scale, and evaluates the effect of spatial dependency on the identification of risk factors and their relative contribution to the occurrence of stunting and severe stunting in a rural area of Ethiopia. A community based cross sectional study was conducted to measure the occurrence of stunting and severe stunting among children aged 0-59 months. Additionally, we collected relevant information on anthropometric measures, dietary habits, parent and child-related demographic and socio-economic status. Latitude and longitude of surveyed households were also recorded. Local Anselin Moran's I was calculated to investigate the spatial variation of stunting prevalence and identify potential local pockets (hotspots) of high prevalence. Finally, we employed a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data, to identify potential risk factors for stunting in the study area. Overall, the prevalence of stunting and severe stunting in the district was 43.7% [95%CI: 40.9, 46.4] and 21.3% [95%CI: 19.5, 23.3] respectively. We identified statistically significant clusters of high prevalence of stunting (hotspots) in the eastern part of the district and clusters of low prevalence (cold spots) in the western. We found out that the inclusion of spatial structure of the data into the Bayesian model has shown to improve the fit for stunting model. The Bayesian geo-statistical model indicated that the risk of stunting increased as the child's age increased (OR 4.74; 95% Bayesian credible interval [BCI]:3.35-6.58) and among boys (OR 1.28; 95%BCI; 1.12-1.45). However, maternal education and household food security were found to be protective against stunting and severe stunting. Stunting prevalence may vary across space at different scale. For this, it's important that nutrition studies and, more importantly, control interventions take into account this spatial heterogeneity in the distribution of nutritional deficits and their underlying associated factors. The findings of this study also indicated that interventions integrating household food insecurity in nutrition programs in the district might help to avert the burden of stunting.

Bayesian modeling of the mass and density of asteroids

NASA Astrophysics Data System (ADS)

Dotson, Jessie L.; Mathias, Donovan

2017-10-01

Mass and density are two of the fundamental properties of any object. In the case of near earth asteroids, knowledge about the mass of an asteroid is essential for estimating the risk due to (potential) impact and planning possible mitigation options. The density of an asteroid can illuminate the structure of the asteroid. A low density can be indicative of a rubble pile structure whereas a higher density can imply a monolith and/or higher metal content. The damage resulting from an impact of an asteroid with Earth depends on its interior structure in addition to its total mass, and as a result, density is a key parameter to understanding the risk of asteroid impact. Unfortunately, measuring the mass and density of asteroids is challenging and often results in measurements with large uncertainties. In the absence of mass / density measurements for a specific object, understanding the range and distribution of likely values can facilitate probabilistic assessments of structure and impact risk. Hierarchical Bayesian models have recently been developed to investigate the mass - radius relationship of exoplanets (Wolfgang, Rogers & Ford 2016) and to probabilistically forecast the mass of bodies large enough to establish hydrostatic equilibrium over a range of 9 orders of magnitude in mass (from planemos to main sequence stars; Chen & Kipping 2017). Here, we extend this approach to investigate the mass and densities of asteroids. Several candidate Bayesian models are presented, and their performance is assessed relative to a synthetic asteroid population. In addition, a preliminary Bayesian model for probablistically forecasting masses and densities of asteroids is presented. The forecasting model is conditioned on existing asteroid data and includes observational errors, hyper-parameter uncertainties and intrinsic scatter.

Privacy-Preserving Patient-Centric Clinical Decision Support System on Naïve Bayesian Classification.

PubMed

Liu, Ximeng; Lu, Rongxing; Ma, Jianfeng; Chen, Le; Qin, Baodong

2016-03-01

Clinical decision support system, which uses advanced data mining techniques to help clinician make proper decisions, has received considerable attention recently. The advantages of clinical decision support system include not only improving diagnosis accuracy but also reducing diagnosis time. Specifically, with large amounts of clinical data generated everyday, naïve Bayesian classification can be utilized to excavate valuable information to improve a clinical decision support system. Although the clinical decision support system is quite promising, the flourish of the system still faces many challenges including information security and privacy concerns. In this paper, we propose a new privacy-preserving patient-centric clinical decision support system, which helps clinician complementary to diagnose the risk of patients' disease in a privacy-preserving way. In the proposed system, the past patients' historical data are stored in cloud and can be used to train the naïve Bayesian classifier without leaking any individual patient medical data, and then the trained classifier can be applied to compute the disease risk for new coming patients and also allow these patients to retrieve the top- k disease names according to their own preferences. Specifically, to protect the privacy of past patients' historical data, a new cryptographic tool called additive homomorphic proxy aggregation scheme is designed. Moreover, to leverage the leakage of naïve Bayesian classifier, we introduce a privacy-preserving top- k disease names retrieval protocol in our system. Detailed privacy analysis ensures that patient's information is private and will not be leaked out during the disease diagnosis phase. In addition, performance evaluation via extensive simulations also demonstrates that our system can efficiently calculate patient's disease risk with high accuracy in a privacy-preserving way.

Integrating quantitative PCR and Bayesian statistics in quantifying human adenoviruses in small volumes of source water.

PubMed

Wu, Jianyong; Gronewold, Andrew D; Rodriguez, Roberto A; Stewart, Jill R; Sobsey, Mark D

2014-02-01

Rapid quantification of viral pathogens in drinking and recreational water can help reduce waterborne disease risks. For this purpose, samples in small volume (e.g. 1L) are favored because of the convenience of collection, transportation and processing. However, the results of viral analysis are often subject to uncertainty. To overcome this limitation, we propose an approach that integrates Bayesian statistics, efficient concentration methods, and quantitative PCR (qPCR) to quantify viral pathogens in water. Using this approach, we quantified human adenoviruses (HAdVs) in eighteen samples of source water collected from six drinking water treatment plants. HAdVs were found in seven samples. In the other eleven samples, HAdVs were not detected by qPCR, but might have existed based on Bayesian inference. Our integrated approach that quantifies uncertainty provides a better understanding than conventional assessments of potential risks to public health, particularly in cases when pathogens may present a threat but cannot be detected by traditional methods. © 2013 Elsevier B.V. All rights reserved.

40 CFR 158.2140 - Microbial pesticides toxicology data requirements table.

Code of Federal Regulations, 2011 CFR

2011-07-01

... inert ingredients is not likely to pose any significant human health risks. Where appropriate, the limit... 40 Protection of Environment 24 2011-07-01 2011-07-01 false Microbial pesticides toxicology data... (CONTINUED) PESTICIDE PROGRAMS DATA REQUIREMENTS FOR PESTICIDES Microbial Pesticides § 158.2140 Microbial...

40 CFR 158.2140 - Microbial pesticides toxicology data requirements table.

Code of Federal Regulations, 2013 CFR

2013-07-01

... inert ingredients is not likely to pose any significant human health risks. Where appropriate, the limit... 40 Protection of Environment 25 2013-07-01 2013-07-01 false Microbial pesticides toxicology data... (CONTINUED) PESTICIDE PROGRAMS DATA REQUIREMENTS FOR PESTICIDES Microbial Pesticides § 158.2140 Microbial...

40 CFR 158.2140 - Microbial pesticides toxicology data requirements table.

Code of Federal Regulations, 2012 CFR

2012-07-01

... inert ingredients is not likely to pose any significant human health risks. Where appropriate, the limit... 40 Protection of Environment 25 2012-07-01 2012-07-01 false Microbial pesticides toxicology data... (CONTINUED) PESTICIDE PROGRAMS DATA REQUIREMENTS FOR PESTICIDES Microbial Pesticides § 158.2140 Microbial...

40 CFR 158.2140 - Microbial pesticides toxicology data requirements table.

Code of Federal Regulations, 2014 CFR

2014-07-01

... inert ingredients is not likely to pose any significant human health risks. Where appropriate, the limit... 40 Protection of Environment 24 2014-07-01 2014-07-01 false Microbial pesticides toxicology data... (CONTINUED) PESTICIDE PROGRAMS DATA REQUIREMENTS FOR PESTICIDES Microbial Pesticides § 158.2140 Microbial...

40 CFR 158.2140 - Microbial pesticides toxicology data requirements table.

Code of Federal Regulations, 2010 CFR

2010-07-01

... inert ingredients is not likely to pose any significant human health risks. Where appropriate, the limit... 40 Protection of Environment 23 2010-07-01 2010-07-01 false Microbial pesticides toxicology data... (CONTINUED) PESTICIDE PROGRAMS DATA REQUIREMENTS FOR PESTICIDES Microbial Pesticides § 158.2140 Microbial...

The risk assessment of sudden water pollution for river network system under multi-source random emission

NASA Astrophysics Data System (ADS)

Li, D.

2016-12-01

Sudden water pollution accidents are unavoidable risk events that we must learn to co-exist with. In China's Taihu River Basin, the river flow conditions are complicated with frequently artificial interference. Sudden water pollution accident occurs mainly in the form of a large number of abnormal discharge of wastewater, and has the characteristics with the sudden occurrence, the uncontrollable scope, the uncertainty object and the concentrated distribution of many risk sources. Effective prevention of pollution accidents that may occur is of great significance for the water quality safety management. Bayesian networks can be applied to represent the relationship between pollution sources and river water quality intuitively. Using the time sequential Monte Carlo algorithm, the pollution sources state switching model, water quality model for river network and Bayesian reasoning is integrated together, and the sudden water pollution risk assessment model for river network is developed to quantify the water quality risk under the collective influence of multiple pollution sources. Based on the isotope water transport mechanism, a dynamic tracing model of multiple pollution sources is established, which can describe the relationship between the excessive risk of the system and the multiple risk sources. Finally, the diagnostic reasoning algorithm based on Bayesian network is coupled with the multi-source tracing model, which can identify the contribution of each risk source to the system risk under the complex flow conditions. Taking Taihu Lake water system as the research object, the model is applied to obtain the reasonable results under the three typical years. Studies have shown that the water quality risk at critical sections are influenced by the pollution risk source, the boundary water quality, the hydrological conditions and self -purification capacity, and the multiple pollution sources have obvious effect on water quality risk of the receiving water body. The water quality risk assessment approach developed in this study offers a effective tool for systematically quantifying the random uncertainty in plain river network system, and it also provides the technical support for the decision-making of controlling the sudden water pollution through identification of critical pollution sources.

A Bayesian Approach to Integrated Ecological and Human Health Risk Assessment for the South River, Virginia Mercury-Contaminated Site.

PubMed

Harris, Meagan J; Stinson, Jonah; Landis, Wayne G

2017-07-01

We conducted a regional-scale integrated ecological and human health risk assessment by applying the relative risk model with Bayesian networks (BN-RRM) to a case study of the South River, Virginia mercury-contaminated site. Risk to four ecological services of the South River (human health, water quality, recreation, and the recreational fishery) was evaluated using a multiple stressor-multiple endpoint approach. These four ecological services were selected as endpoints based on stakeholder feedback and prioritized management goals for the river. The BN-RRM approach allowed for the calculation of relative risk to 14 biotic, human health, recreation, and water quality endpoints from chemical and ecological stressors in five risk regions of the South River. Results indicated that water quality and the recreational fishery were the ecological services at highest risk in the South River. Human health risk for users of the South River was low relative to the risk to other endpoints. Risk to recreation in the South River was moderate with little spatial variability among the five risk regions. Sensitivity and uncertainty analysis identified stressors and other parameters that influence risk for each endpoint in each risk region. This research demonstrates a probabilistic approach to integrated ecological and human health risk assessment that considers the effects of chemical and ecological stressors across the landscape. © 2017 Society for Risk Analysis.

Quantifying uncertainty in Bayesian calibrated animal-to-human PBPK models with informative prior distributions

EPA Science Inventory

Understanding and quantifying the uncertainty of model parameters and predictions has gained more interest in recent years with the increased use of computational models in chemical risk assessment. Fully characterizing the uncertainty in risk metrics derived from linked quantita...

Competing risk models in reliability systems, an exponential distribution model with Bayesian analysis approach

NASA Astrophysics Data System (ADS)

Iskandar, I.

2018-03-01

The exponential distribution is the most widely used reliability analysis. This distribution is very suitable for representing the lengths of life of many cases and is available in a simple statistical form. The characteristic of this distribution is a constant hazard rate. The exponential distribution is the lower rank of the Weibull distributions. In this paper our effort is to introduce the basic notions that constitute an exponential competing risks model in reliability analysis using Bayesian analysis approach and presenting their analytic methods. The cases are limited to the models with independent causes of failure. A non-informative prior distribution is used in our analysis. This model describes the likelihood function and follows with the description of the posterior function and the estimations of the point, interval, hazard function, and reliability. The net probability of failure if only one specific risk is present, crude probability of failure due to a specific risk in the presence of other causes, and partial crude probabilities are also included.

Disease Mapping for Stomach Cancer in Libya Based on Besag– York– Mollié (BYM) Model

PubMed

Alhdiri, Maryam Ahmed Salem; Samat, Nor Azah; Mohamed, Zulkifley

2017-06-25

Globally, Cancer is the ever-increasing health problem and most common cause of medical deaths. In Libya, it is an important health concern, especially in the setting of an aging population and limited healthcare facilities. Therefore, the goal of this research is to map of the county’ cancer incidence rate using the Bayesian method and identify the high-risk regions (for the first time in a decade). In the field of disease mapping, very little has been done to address the issue of analyzing sparse cancer diseases in Libya. Standardized Morbidity Ratio or SMR is known as a traditional approach to measure the relative risk of the disease, which is the ratio of observed and expected number of accounts in a region that has the greatest uncertainty if the disease is rare or small geographical region. Therefore, to solve some of SMR’s problems, we used statistical smoothing or Bayesian models to estimate the relative risk for stomach cancer incidence in Libya in 2007 based on the BYM model. This research begins with a short offer of the SMR and Bayesian model with BYM model, which we applied to stomach cancer incidence in Libya. We compared all of the results using maps and tables. We found that BYM model is potentially beneficial, because it gives better relative risk estimates compared to SMR method. As well as, it has can overcome the classical method problem when there is no observed stomach cancer in a region. Creative Commons Attribution License

Using qPCR for Water Microbial Risk Assessments

EPA Science Inventory

Microbial risk assessment (MRA) has traditionally utilized microbiological data that was obtained by culture-based techniques that are expensive and time consuming. With the advent of PCR methods there is a realistic opportunity to conduct MRA studies economically, in less time,...

Using integrated environmental modeling to automate a process-based Quantitative Microbial Risk Assessment

EPA Science Inventory

Integrated Environmental Modeling (IEM) organizes multidisciplinary knowledge that explains and predicts environmental-system response to stressors. A Quantitative Microbial Risk Assessment (QMRA) is an approach integrating a range of disparate data (fate/transport, exposure, an...

Using Integrated Environmental Modeling to Automate a Process-Based Quantitative Microbial Risk Assessment (presentation)

EPA Science Inventory

Integrated Environmental Modeling (IEM) organizes multidisciplinary knowledge that explains and predicts environmental-system response to stressors. A Quantitative Microbial Risk Assessment (QMRA) is an approach integrating a range of disparate data (fate/transport, exposure, and...

QUANTITATIVE RISK ASSESSMENT FOR MICROBIAL AGENTS

EPA Science Inventory

Compared to chemical risk assessment, the process for microbial agents and infectious disease is more complex because of host factors and the variety of settings in which disease transmission can occur. While the National Academy of Science has established a paradigm for performi...

Dynamic safety assessment of natural gas stations using Bayesian network.

PubMed

Zarei, Esmaeil; Azadeh, Ali; Khakzad, Nima; Aliabadi, Mostafa Mirzaei; Mohammadfam, Iraj

2017-01-05

Pipelines are one of the most popular and effective ways of transporting hazardous materials, especially natural gas. However, the rapid development of gas pipelines and stations in urban areas has introduced a serious threat to public safety and assets. Although different methods have been developed for risk analysis of gas transportation systems, a comprehensive methodology for risk analysis is still lacking, especially in natural gas stations. The present work is aimed at developing a dynamic and comprehensive quantitative risk analysis (DCQRA) approach for accident scenario and risk modeling of natural gas stations. In this approach, a FMEA is used for hazard analysis while a Bow-tie diagram and Bayesian network are employed to model the worst-case accident scenario and to assess the risks. The results have indicated that the failure of the regulator system was the worst-case accident scenario with the human error as the most contributing factor. Thus, in risk management plan of natural gas stations, priority should be given to the most probable root events and main contribution factors, which have identified in the present study, in order to reduce the occurrence probability of the accident scenarios and thus alleviate the risks. Copyright © 2016 Elsevier B.V. All rights reserved.

Simultaneous fecal microbial and metabolite profiling enables accurate classification of pediatric irritable bowel syndrome.

PubMed

Shankar, Vijay; Reo, Nicholas V; Paliy, Oleg

2015-12-09

We previously showed that stool samples of pre-adolescent and adolescent US children diagnosed with diarrhea-predominant IBS (IBS-D) had different compositions of microbiota and metabolites compared to healthy age-matched controls. Here we explored whether observed fecal microbiota and metabolite differences between these two adolescent populations can be used to discriminate between IBS and health. We constructed individual microbiota- and metabolite-based sample classification models based on the partial least squares multivariate analysis and then applied a Bayesian approach to integrate individual models into a single classifier. The resulting combined classification achieved 84 % accuracy of correct sample group assignment and 86 % prediction for IBS-D in cross-validation tests. The performance of the cumulative classification model was further validated by the de novo analysis of stool samples from a small independent IBS-D cohort. High-throughput microbial and metabolite profiling of subject stool samples can be used to facilitate IBS diagnosis.

Bayesian statistical inference enhances the interpretation of contemporary randomized controlled trials.

PubMed

Wijeysundera, Duminda N; Austin, Peter C; Hux, Janet E; Beattie, W Scott; Laupacis, Andreas

2009-01-01

Randomized trials generally use "frequentist" statistics based on P-values and 95% confidence intervals. Frequentist methods have limitations that might be overcome, in part, by Bayesian inference. To illustrate these advantages, we re-analyzed randomized trials published in four general medical journals during 2004. We used Medline to identify randomized superiority trials with two parallel arms, individual-level randomization and dichotomous or time-to-event primary outcomes. Studies with P<0.05 in favor of the intervention were deemed "positive"; otherwise, they were "negative." We used several prior distributions and exact conjugate analyses to calculate Bayesian posterior probabilities for clinically relevant effects. Of 88 included studies, 39 were positive using a frequentist analysis. Although the Bayesian posterior probabilities of any benefit (relative risk or hazard ratio<1) were high in positive studies, these probabilities were lower and variable for larger benefits. The positive studies had only moderate probabilities for exceeding the effects that were assumed for calculating the sample size. By comparison, there were moderate probabilities of any benefit in negative studies. Bayesian and frequentist analyses complement each other when interpreting the results of randomized trials. Future reports of randomized trials should include both.

A Flexible Hierarchical Bayesian Modeling Technique for Risk Analysis of Major Accidents.

PubMed

Yu, Hongyang; Khan, Faisal; Veitch, Brian

2017-09-01

Safety analysis of rare events with potentially catastrophic consequences is challenged by data scarcity and uncertainty. Traditional causation-based approaches, such as fault tree and event tree (used to model rare event), suffer from a number of weaknesses. These include the static structure of the event causation, lack of event occurrence data, and need for reliable prior information. In this study, a new hierarchical Bayesian modeling based technique is proposed to overcome these drawbacks. The proposed technique can be used as a flexible technique for risk analysis of major accidents. It enables both forward and backward analysis in quantitative reasoning and the treatment of interdependence among the model parameters. Source-to-source variability in data sources is also taken into account through a robust probabilistic safety analysis. The applicability of the proposed technique has been demonstrated through a case study in marine and offshore industry. © 2017 Society for Risk Analysis.

A conceptual model for site-level ecology of the giant gartersnake (Thamnophis gigas) in the Sacramento Valley, California

USGS Publications Warehouse

Halstead, Brian J.; Wylie, Glenn D.; Casazza, Michael L.; Hansen, Eric C.; Scherer, Rick D.; Patterson, Laura C.

2015-08-14

Bayesian networks further provide a clear visual display of the model that facilitates understanding among various stakeholders (Marcot and others, 2001; Uusitalo , 2007). Empirical data and expert judgment can be combined, as continuous or categorical variables, to update knowledge about the system (Marcot and others, 2001; Uusitalo , 2007). Importantly, Bayesian network models allow inference from causes to consequences, but also from consequences to causes, so that data can inform the states of nodes (values of different random variables) in either direction (Marcot and others, 2001; Uusitalo , 2007). Because they can incorporate both decision nodes that represent management actions and utility nodes that quantify the costs and benefits of outcomes, Bayesian networks are ideally suited to risk analysis and adaptive management (Nyberg and others, 2006; Howes and others, 2010). Thus, Bayesian network models are useful in situations where empirical data are not available, such as questions concerning the responses of giant gartersnakes to management.

Microbial Risk Assessment

NASA Technical Reports Server (NTRS)

Ott, C. M.; Mena, K. D.; Nickerson, C.A.; Pierson, D. L.

2009-01-01

Historically, microbiological spaceflight requirements have been established in a subjective manner based upon expert opinion of both environmental and clinical monitoring results and the incidence of disease. The limited amount of data, especially from long-duration missions, has created very conservative requirements based primarily on the concentration of microorganisms. Periodic reevaluations of new data from later missions have allowed some relaxation of these stringent requirements. However, the requirements remain very conservative and subjective in nature, and the risk of crew illness due to infectious microorganisms is not well defined. The use of modeling techniques for microbial risk has been applied in the food and potable water industries and has exceptional potential for spaceflight applications. From a productivity standpoint, this type of modeling can (1) decrease unnecessary costs and resource usage and (2) prevent inadequate or inappropriate data for health assessment. In addition, a quantitative model has several advantages for risk management and communication. By identifying the variable components of the model and the knowledge associated with each component, this type of modeling can: (1) Systematically identify and close knowledge gaps, (2) Systematically identify acceptable and unacceptable risks, (3) Improve communication with stakeholders as to the reasons for resource use, and (4) Facilitate external scientific approval of the NASA requirements. The modeling of microbial risk involves the evaluation of several key factors including hazard identification, crew exposure assessment, dose-response assessment, and risk characterization. Many of these factors are similar to conditions found on Earth; however, the spaceflight environment is very specialized as the inhabitants live in a small, semi-closed environment that is often dependent on regenerative life support systems. To further complicate modeling efforts, microbial dose-response characteristics may be affected by a potentially dysfunctional crew immune system during a mission. In addition, microbial virulence has been shown to change under certain conditions during spaceflight, further complicating dose-response characterization. An initial study of the applicability of microbial risk assessment techniques was performed using Crew Health Care System (CHeCS) operational data from the International Space Station potable water systems. The risk of infection from potable water was selected as the flight systems and microbial ecology are well defined. This initial study confirmed the feasibility of using microbial risk assessment modeling for spaceflight systems. While no immediate threat was detected, the study identified several medically significant microorganisms that could pose a health risk if uncontrolled. The study also identified several specific knowledge gaps in making a risk assessment and noted that filling these knowledge gaps is essential as the risk estimates may change by orders of magnitude depending on the answers. The current phase of the microbial risk assessment studies focuses on the dose-response relationship of specific infectious agents, focusing on Salmonella enterica Typhimurium, Pseudomonas spp., and Escherichia coli, as their evaluation will provide a better baseline for determining the overall hazard characterization. The organisms were chosen as they either have been isolated on spacecraft or have an identified route of infection during a mission. The characterization will utilize dose-response models selected either from the peer-reviewed literature and/or by using statistical approaches. Development of these modeling and risk assessment techniques will help to optimize flight requirements and to protect the safety, health, and performance of the crew.

Risk assessment by dynamic representation of vulnerability, exploitation, and impact

NASA Astrophysics Data System (ADS)

Cam, Hasan

2015-05-01

Assessing and quantifying cyber risk accurately in real-time is essential to providing security and mission assurance in any system and network. This paper presents a modeling and dynamic analysis approach to assessing cyber risk of a network in real-time by representing dynamically its vulnerabilities, exploitations, and impact using integrated Bayesian network and Markov models. Given the set of vulnerabilities detected by a vulnerability scanner in a network, this paper addresses how its risk can be assessed by estimating in real-time the exploit likelihood and impact of vulnerability exploitation on the network, based on real-time observations and measurements over the network. The dynamic representation of the network in terms of its vulnerabilities, sensor measurements, and observations is constructed dynamically using the integrated Bayesian network and Markov models. The transition rates of outgoing and incoming links of states in hidden Markov models are used in determining exploit likelihood and impact of attacks, whereas emission rates help quantify the attack states of vulnerabilities. Simulation results show the quantification and evolving risk scores over time for individual and aggregated vulnerabilities of a network.

How can investment in the landscape or the interface reduce the risk of house loss from wildfires? A comparative study between Sydney, Australia and California, USA

NASA Astrophysics Data System (ADS)

Penman, Trent; Bradstock, Ross; Collins, Luke; Fotheringham, Cj; Keeley, Jon; Labiosa, Bill; Price, Owen; Syphard, Alex

2013-04-01

Wildfire can result in significant losses to people and property. Management agencies undertake a range of actions in the landscape and at the interface to reduce this risk. Data relating to the success of individual treatments varies, with some approaches well understood and others less so. Research has rarely attempted to consider the interactive effects of treatments in order to determine optimal management strategies that reduce the risk of loss. Bayesian Networks provide a statistical framework for undertaking such an analysis. Here we apply Bayesian Networks to examine the trade-offs in investment in preventative actions (e.g., fuel treatment, community education, development controls) and suppressive actions (e.g., initial attack, landscape suppression, property protection) in two fire prone regions -Sydney, Australia and California, USA. Investment in management actions at the interface resulted in the greatest reduction in the risk of house loss for both of the study regions. Landscape treatments had a limited ability to change the risk of house loss.

Using integrated environmental modeling to automate a process-based Quantitative Microbial Risk Assessment

USDA-ARS?s Scientific Manuscript database

Integrated Environmental Modeling (IEM) organizes multidisciplinary knowledge that explains and predicts environmental-system response to stressors. A Quantitative Microbial Risk Assessment (QMRA) is an approach integrating a range of disparate data (fate/transport, exposure, and human health effect...

Microbial cultures in open globe injuries in southern India.

PubMed

Gupta, Arvind; Srinivasan, Renuka; Kaliaperumal, Subashini; Setia, Sajita

2007-07-01

To determine the risk factors leading to positive intraocular culture in patients with open globe injury. A prospective interventional study involving 110 eyes of 110 patients of more than 15 years of age, presenting with open globe injury, was undertaken. Emergency repair of the injured globe was done. Prolapsed intraocular tissue or aqueous humour was sent for microbial work up before repair. In endophthalmitis cases intravitreal antibiotics were given according to the antimicrobial sensitivity. Chi-square and logistic regression analysis were used to determine the risk factors. Fifty-six patients showed microbial contamination. Bacteria were cultured in 42 patients and fungi in 14 patients. Nineteen patients developed endophthalmitis, of which 18 patients showed microbial growth initially. In univariate analysis, initial visual acuity (<6/360, P = 0.002), presence of uveal tissue prolapse (P < 0.001), vitreous prolapse (P < 0.001) and length of laceration (>8 mm, P < 0.001) were significantly associated with positive microbial culture, however, in the multivariate stepwise logistic regression delay in surgical intervention (>72 h, P < 0.001), uveal tissue prolapse (P = 0.004) and corneosclearal laceration (>8 mm, P = 0.013) were associated with increased risk of positive microbial culture. Six patients had intraocular foreign body but were culture negative. Age, gender, site of injury and presence of cataract did not significantly affect the culture positivity. Microbial contamination is a risk factor for the development for endophthalmitis. Despite the high frequency of microbial contamination, it develops only in few cases. Systemic antibiotics, virulence of the organism and host factors play a role in the manifestation of endophthalmitis. Prophylaxis with intraocular antibiotics should be strongly considered in cases with poor vision at presentation, larger corneoscleral laceration, delayed surgical intervention and uveal tissue or vitreous prolapse.

Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data

EPA Science Inventory

The benchmark dose (BMD) approach has gained acceptance as a valuable risk assessment tool, but risk assessors still face significant challenges associated with selecting an appropriate BMD/BMDL estimate from the results of a set of acceptable dose-response models. Current approa...

Bayesian network modeling applied to coastal geomorphology: lessons learned from a decade of experimentation and application

NASA Astrophysics Data System (ADS)

Plant, N. G.; Thieler, E. R.; Gutierrez, B.; Lentz, E. E.; Zeigler, S. L.; Van Dongeren, A.; Fienen, M. N.

2016-12-01

We evaluate the strengths and weaknesses of Bayesian networks that have been used to address scientific and decision-support questions related to coastal geomorphology. We will provide an overview of coastal geomorphology research that has used Bayesian networks and describe what this approach can do and when it works (or fails to work). Over the past decade, Bayesian networks have been formulated to analyze the multi-variate structure and evolution of coastal morphology and associated human and ecological impacts. The approach relates observable system variables to each other by estimating discrete correlations. The resulting Bayesian-networks make predictions that propagate errors, conduct inference via Bayes rule, or both. In scientific applications, the model results are useful for hypothesis testing, using confidence estimates to gage the strength of tests while applications to coastal resource management are aimed at decision-support, where the probabilities of desired ecosystems outcomes are evaluated. The range of Bayesian-network applications to coastal morphology includes emulation of high-resolution wave transformation models to make oceanographic predictions, morphologic response to storms and/or sea-level rise, groundwater response to sea-level rise and morphologic variability, habitat suitability for endangered species, and assessment of monetary or human-life risk associated with storms. All of these examples are based on vast observational data sets, numerical model output, or both. We will discuss the progression of our experiments, which has included testing whether the Bayesian-network approach can be implemented and is appropriate for addressing basic and applied scientific problems and evaluating the hindcast and forecast skill of these implementations. We will present and discuss calibration/validation tests that are used to assess the robustness of Bayesian-network models and we will compare these results to tests of other models. This will demonstrate how Bayesian networks are used to extract new insights about coastal morphologic behavior, assess impacts to societal and ecological systems, and communicate probabilistic predictions to decision makers.

Monitoring of Microbial Loads During Long Duration Missions as a Risk Reduction Tool

NASA Technical Reports Server (NTRS)

Roman, Monsi C.

2011-01-01

Humans have been exploring space for more than 40 years. For all those years microorganisms have accompanied, first un-manned spacecraft/cargo and later manned vessels. Microorganisms are everywhere on Earth, could easily adapt to new environments and/or can rapidly mutate to survive in very harsh conditions. Their presence in spacecraft and cargo have caused a few inconveniences over the years of humans spaceflight, ranging from crew health, life support systems challenges and material degradation. The sterilization of spacecraft that will host humans in long duration mission would be a costly operation that will not provide a long-term solution to the microbial colonization of the vessels. As soon as a human is exposed to the spacecraft, during the mission, microorganisms will start to populate the new environment. As the hum an presence in space increases in length, the risk from the microbial load, to hardware and crew will also increase. Mitigation of this risk includes several different strategies that will include minimizing the microbial load (in numbers and diversity) and monitoring. This presentation will provide a list of the risk mitigation strategies that should be implemented during ground processing, and during the mission. It will also discuss the areas that should be discussed before an effective in-flight microbial monitoring regimen is implemented. Microbial monitoring technologies will also be presented.

Bayesian methodology for the design and interpretation of clinical trials in critical care medicine: a primer for clinicians.

PubMed

Kalil, Andre C; Sun, Junfeng

2014-10-01

To review Bayesian methodology and its utility to clinical decision making and research in the critical care field. Clinical, epidemiological, and biostatistical studies on Bayesian methods in PubMed and Embase from their inception to December 2013. Bayesian methods have been extensively used by a wide range of scientific fields, including astronomy, engineering, chemistry, genetics, physics, geology, paleontology, climatology, cryptography, linguistics, ecology, and computational sciences. The application of medical knowledge in clinical research is analogous to the application of medical knowledge in clinical practice. Bedside physicians have to make most diagnostic and treatment decisions on critically ill patients every day without clear-cut evidence-based medicine (more subjective than objective evidence). Similarly, clinical researchers have to make most decisions about trial design with limited available data. Bayesian methodology allows both subjective and objective aspects of knowledge to be formally measured and transparently incorporated into the design, execution, and interpretation of clinical trials. In addition, various degrees of knowledge and several hypotheses can be tested at the same time in a single clinical trial without the risk of multiplicity. Notably, the Bayesian technology is naturally suited for the interpretation of clinical trial findings for the individualized care of critically ill patients and for the optimization of public health policies. We propose that the application of the versatile Bayesian methodology in conjunction with the conventional statistical methods is not only ripe for actual use in critical care clinical research but it is also a necessary step to maximize the performance of clinical trials and its translation to the practice of critical care medicine.

The human gut microbiome as a screening tool for colorectal cancer.

PubMed

Zackular, Joseph P; Rogers, Mary A M; Ruffin, Mack T; Schloss, Patrick D

2014-11-01

Recent studies have suggested that the gut microbiome may be an important factor in the development of colorectal cancer. Abnormalities in the gut microbiome have been reported in patients with colorectal cancer; however, this microbial community has not been explored as a potential screen for early-stage disease. We characterized the gut microbiome in patients from three clinical groups representing the stages of colorectal cancer development: healthy, adenoma, and carcinoma. Analysis of the gut microbiome from stool samples revealed both an enrichment and depletion of several bacterial populations associated with adenomas and carcinomas. Combined with known clinical risk factors of colorectal cancer (e.g., BMI, age, race), data from the gut microbiome significantly improved the ability to differentiate between healthy, adenoma, and carcinoma clinical groups relative to risk factors alone. Using Bayesian methods, we determined that using gut microbiome data as a screening tool improved the pretest to posttest probability of adenoma more than 50-fold. For example, the pretest probability in a 65-year-old was 0.17% and, after using the microbiome data, this increased to 10.67% (1 in 9 chance of having an adenoma). Taken together, the results of our study demonstrate the feasibility of using the composition of the gut microbiome to detect the presence of precancerous and cancerous lesions. Furthermore, these results support the need for more cross-sectional studies with diverse populations and linkage to other stool markers, dietary data, and personal health information. ©2014 American Association for Cancer Research.

A flexible Bayesian hierarchical model of preterm birth risk among US Hispanic subgroups in relation to maternal nativity and education

PubMed Central

2011-01-01

Background Previous research has documented heterogeneity in the effects of maternal education on adverse birth outcomes by nativity and Hispanic subgroup in the United States. In this article, we considered the risk of preterm birth (PTB) using 9 years of vital statistics birth data from New York City. We employed finer categorizations of exposure than used previously and estimated the risk dose-response across the range of education by nativity and ethnicity. Methods Using Bayesian random effects logistic regression models with restricted quadratic spline terms for years of completed maternal education, we calculated and plotted the estimated posterior probabilities of PTB (gestational age < 37 weeks) for each year of education by ethnic and nativity subgroups adjusted for only maternal age, as well as with more extensive covariate adjustments. We then estimated the posterior risk difference between native and foreign born mothers by ethnicity over the continuous range of education exposures. Results The risk of PTB varied substantially by education, nativity and ethnicity. Native born groups showed higher absolute risk of PTB and declining risk associated with higher levels of education beyond about 10 years, as did foreign-born Puerto Ricans. For most other foreign born groups, however, risk of PTB was flatter across the education range. For Mexicans, Central Americans, Dominicans, South Americans and "Others", the protective effect of foreign birth diminished progressively across the educational range. Only for Puerto Ricans was there no nativity advantage for the foreign born, although small numbers of foreign born Cubans limited precision of estimates for that group. Conclusions Using flexible Bayesian regression models with random effects allowed us to estimate absolute risks without strong modeling assumptions. Risk comparisons for any sub-groups at any exposure level were simple to calculate. Shrinkage of posterior estimates through the use of random effects allowed for finer categorization of exposures without restricting joint effects to follow a fixed parametric scale. Although foreign born Hispanic women with the least education appeared to generally have low risk, this seems likely to be a marker for unmeasured environmental and behavioral factors, rather than a causally protective effect of low education itself. PMID:21504612

A flexible Bayesian hierarchical model of preterm birth risk among US Hispanic subgroups in relation to maternal nativity and education.

PubMed

Kaufman, Jay S; MacLehose, Richard F; Torrone, Elizabeth A; Savitz, David A

2011-04-19

Previous research has documented heterogeneity in the effects of maternal education on adverse birth outcomes by nativity and Hispanic subgroup in the United States. In this article, we considered the risk of preterm birth (PTB) using 9 years of vital statistics birth data from New York City. We employed finer categorizations of exposure than used previously and estimated the risk dose-response across the range of education by nativity and ethnicity. Using Bayesian random effects logistic regression models with restricted quadratic spline terms for years of completed maternal education, we calculated and plotted the estimated posterior probabilities of PTB (gestational age < 37 weeks) for each year of education by ethnic and nativity subgroups adjusted for only maternal age, as well as with more extensive covariate adjustments. We then estimated the posterior risk difference between native and foreign born mothers by ethnicity over the continuous range of education exposures. The risk of PTB varied substantially by education, nativity and ethnicity. Native born groups showed higher absolute risk of PTB and declining risk associated with higher levels of education beyond about 10 years, as did foreign-born Puerto Ricans. For most other foreign born groups, however, risk of PTB was flatter across the education range. For Mexicans, Central Americans, Dominicans, South Americans and "Others", the protective effect of foreign birth diminished progressively across the educational range. Only for Puerto Ricans was there no nativity advantage for the foreign born, although small numbers of foreign born Cubans limited precision of estimates for that group. Using flexible Bayesian regression models with random effects allowed us to estimate absolute risks without strong modeling assumptions. Risk comparisons for any sub-groups at any exposure level were simple to calculate. Shrinkage of posterior estimates through the use of random effects allowed for finer categorization of exposures without restricting joint effects to follow a fixed parametric scale. Although foreign born Hispanic women with the least education appeared to generally have low risk, this seems likely to be a marker for unmeasured environmental and behavioral factors, rather than a causally protective effect of low education itself.

Distinct respiratory responses of soils to complex organic substrate are governed predominantly by soil architecture and its microbial community.

PubMed

Fraser, F C; Todman, L C; Corstanje, R; Deeks, L K; Harris, J A; Pawlett, M; Whitmore, A P; Ritz, K

2016-12-01

Factors governing the turnover of organic matter (OM) added to soils, including substrate quality, climate, environment and biology, are well known, but their relative importance has been difficult to ascertain due to the interconnected nature of the soil system. This has made their inclusion in mechanistic models of OM turnover or nutrient cycling difficult despite the potential power of these models to unravel complex interactions. Using high temporal-resolution respirometery (6 min measurement intervals), we monitored the respiratory response of 67 soils sampled from across England and Wales over a 5 day period following the addition of a complex organic substrate (green barley powder). Four respiratory response archetypes were observed, characterised by different rates of respiration as well as different time-dependent patterns. We also found that it was possible to predict, with 95% accuracy, which type of respiratory behaviour a soil would exhibit based on certain physical and chemical soil properties combined with the size and phenotypic structure of the microbial community. Bulk density, microbial biomass carbon, water holding capacity and microbial community phenotype were identified as the four most important factors in predicting the soils' respiratory responses using a Bayesian belief network. These results show that the size and constitution of the microbial community are as important as physico-chemical properties of a soil in governing the respiratory response to OM addition. Such a combination suggests that the 'architecture' of the soil, i.e. the integration of the spatial organisation of the environment and the interactions between the communities living and functioning within the pore networks, is fundamentally important in regulating such processes.

Familial Oral Microbial Imbalance and Dental Caries Occurrence in Their Children

PubMed Central

Bretz, Walter A.; Thomas, John G.; weyant, Robert J.

2013-01-01

Objective Develop a familial liability index for oral microbial status that reflects an imbalance of oral domains based on the presence of risk indicators in saliva, inter-proximal plaque, tongue, and throat. Methods Fifty-six mother-child pairs from Webster and Nicholas counties, West Virginia, USA, participated in this study. Saliva samples were assayed for mutans streptococci (MS), interproximal plaque samples for the BANA Test (BT) species, tongue swabs for BT, and throat swabs for any of the sentinel organisms (Staphylococcus aureus, Streptococcus pyogenes, and yeasts). The corresponding thresholds for a (+) risk indicator were, respectively, ≥105 CFU of MS salivary levels, one or more BT-(+) plaques (>105 CFU/mg of plaque of at least one of BT-(+) species), weak-(+) BT for a tongue swab (>104-<105), and >104 CFU/swab for any of the sentinel markers. Results The mean age of mothers and children was 41.6 and 14.6 years. Ninety-one % of both mothers and children had at least one (+) risk indicator. Overall, 76% of mother child-pairs had at least one (+) concordant oral microbial risk indicator. Accordingly, the relative risk (RR) of children having concordant results with their mothers was increased 1.36 (BT-plaque), 1.37 (BT-tongue), 0.94 (sentinel organisms) and 1.13 (MS) times. Principal component analysis revealed distinct sets of oral microbial risk indicators in mothers and children that correlated with dental caries prevalence rates in children. Conclusions Mother-child pairs shared similarities of oral microbial risk indicators that allow for the development of a liability index that can elucidate caries in the children. PMID:24600078

Assessing the origin of bacteria in tap water and distribution system in an unchlorinated drinking water system by SourceTracker using microbial community fingerprints.

PubMed

Liu, Gang; Zhang, Ya; van der Mark, Ed; Magic-Knezev, Aleksandra; Pinto, Ameet; van den Bogert, Bartholomeus; Liu, Wentso; van der Meer, Walter; Medema, Gertjan

2018-07-01

The general consensus is that the abundance of tap water bacteria is greatly influenced by water purification and distribution. Those bacteria that are released from biofilm in the distribution system are especially considered as the major potential risk for drinking water bio-safety. For the first time, this full-scale study has captured and identified the proportional contribution of the source water, treated water, and distribution system in shaping the tap water bacterial community based on their microbial community fingerprints using the Bayesian "SourceTracker" method. The bacterial community profiles and diversity analyses illustrated that the water purification process shaped the community of planktonic and suspended particle-associated bacteria in treated water. The bacterial communities associated with suspended particles, loose deposits, and biofilm were similar to each other, while the community of tap water planktonic bacteria varied across different locations in distribution system. The microbial source tracking results showed that there was not a detectable contribution of source water to bacterial community in the tap water and distribution system. The planktonic bacteria in the treated water was the major contributor to planktonic bacteria in the tap water (17.7-54.1%). The particle-associated bacterial community in the treated water seeded the bacterial community associated with loose deposits (24.9-32.7%) and biofilm (37.8-43.8%) in the distribution system. In return, the loose deposits and biofilm showed a significant influence on tap water planktonic and particle-associated bacteria, which were location dependent and influenced by hydraulic changes. This was revealed by the increased contribution of loose deposits to tap water planktonic bacteria (from 2.5% to 38.0%) and an increased contribution of biofilm to tap water particle-associated bacteria (from 5.9% to 19.7%) caused by possible hydraulic disturbance from proximal to distal regions. Therefore, our findings indicate that the tap water bacteria could possibly be managed by selecting and operating the purification process properly and cleaning the distribution system effectively. Copyright © 2018 Elsevier Ltd. All rights reserved.

Periodic benefit-risk assessment using Bayesian stochastic multi-criteria acceptability analysis

PubMed Central

Li, Kan; Yuan, Shuai Sammy; Wang, William; Wan, Shuyan Sabrina; Ceesay, Paulette; Heyse, Joseph F.; Mt-Isa, Shahrul; Luo, Sheng

2018-01-01

Benefit-risk (BR) assessment is essential to ensure the best decisions are made for a medical product in the clinical development process, regulatory marketing authorization, post-market surveillance, and coverage and reimbursement decisions. One challenge of BR assessment in practice is that the benefit and risk profile may keep evolving while new evidence is accumulating. Regulators and the International Conference on Harmonization (ICH) recommend performing periodic benefit-risk evaluation report (PBRER) through the product's lifecycle. In this paper, we propose a general statistical framework for periodic benefit-risk assessment, in which Bayesian meta-analysis and stochastic multi-criteria acceptability analysis (SMAA) will be combined to synthesize the accumulating evidence. The proposed approach allows us to compare the acceptability of different drugs dynamically and effectively and accounts for the uncertainty of clinical measurements and imprecise or incomplete preference information of decision makers. We apply our approaches to two real examples in a post-hoc way for illustration purpose. The proposed method may easily be modified for other pre and post market settings, and thus be an important complement to the current structured benefit-risk assessment (sBRA) framework to improve the transparent and consistency of the decision-making process. PMID:29505866

Improving default risk prediction using Bayesian model uncertainty techniques.

PubMed

Kazemi, Reza; Mosleh, Ali

2012-11-01

Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from "nominal predictions" due to "upsetting events" such as the 2008 global banking crisis. © 2012 Society for Risk Analysis.

A Bayesian model averaging approach for estimating the relative risk of mortality associated with heat waves in 105 U.S. cities.

PubMed

Bobb, Jennifer F; Dominici, Francesca; Peng, Roger D

2011-12-01

Estimating the risks heat waves pose to human health is a critical part of assessing the future impact of climate change. In this article, we propose a flexible class of time series models to estimate the relative risk of mortality associated with heat waves and conduct Bayesian model averaging (BMA) to account for the multiplicity of potential models. Applying these methods to data from 105 U.S. cities for the period 1987-2005, we identify those cities having a high posterior probability of increased mortality risk during heat waves, examine the heterogeneity of the posterior distributions of mortality risk across cities, assess sensitivity of the results to the selection of prior distributions, and compare our BMA results to a model selection approach. Our results show that no single model best predicts risk across the majority of cities, and that for some cities heat-wave risk estimation is sensitive to model choice. Although model averaging leads to posterior distributions with increased variance as compared to statistical inference conditional on a model obtained through model selection, we find that the posterior mean of heat wave mortality risk is robust to accounting for model uncertainty over a broad class of models. © 2011, The International Biometric Society.

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations

PubMed Central

Majumdar, Arunabha; Haldar, Tanushree; Bhattacharya, Sourabh; Witte, John S.

2018-01-01

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package ‘CPBayes’ implementing the proposed method. PMID:29432419

Understanding the complex relationships underlying hot flashes: a Bayesian network approach.

PubMed

Smith, Rebecca L; Gallicchio, Lisa M; Flaws, Jodi A

2018-02-01

The mechanism underlying hot flashes is not well-understood, primarily because of complex relationships between and among hot flashes and their risk factors. We explored those relationships using a Bayesian network approach based on a 2006 to 2015 cohort study of hot flashes among 776 female residents, 45 to 54 years old, in the Baltimore area. Bayesian networks were fit for each outcome (current hot flashes, hot flashes before the end of the study, hot flash severity, hot flash frequency, and age at first hot flashes) separately and together with a list of risk factors (estrogen, progesterone, testosterone, body mass index and obesity, race, income level, education level, smoking history, drinking history, and activity level). Each fitting was conducted separately on all women and only perimenopausal women, at enrollment and 4 years after enrollment. Hormone levels, almost always interrelated, were the most common variable linked to hot flashes; hormone levels were sometimes related to body mass index, but were not directly related to any other risk factors. Smoking was also frequently associated with increased likelihood of severe symptoms, but not through an antiestrogenic pathway. The age at first hot flashes was related only to race. All other factors were either not related to outcomes or were mediated entirely by race, hormone levels, or smoking. These models can serve as a guide for design of studies into the causal network underlying hot flashes.

Model estimation of claim risk and premium for motor vehicle insurance by using Bayesian method

NASA Astrophysics Data System (ADS)

Sukono; Riaman; Lesmana, E.; Wulandari, R.; Napitupulu, H.; Supian, S.

2018-01-01

Risk models need to be estimated by the insurance company in order to predict the magnitude of the claim and determine the premiums charged to the insured. This is intended to prevent losses in the future. In this paper, we discuss the estimation of risk model claims and motor vehicle insurance premiums using Bayesian methods approach. It is assumed that the frequency of claims follow a Poisson distribution, while a number of claims assumed to follow a Gamma distribution. The estimation of parameters of the distribution of the frequency and amount of claims are made by using Bayesian methods. Furthermore, the estimator distribution of frequency and amount of claims are used to estimate the aggregate risk models as well as the value of the mean and variance. The mean and variance estimator that aggregate risk, was used to predict the premium eligible to be charged to the insured. Based on the analysis results, it is shown that the frequency of claims follow a Poisson distribution with parameter values λ is 5.827. While a number of claims follow the Gamma distribution with parameter values p is 7.922 and θ is 1.414. Therefore, the obtained values of the mean and variance of the aggregate claims respectively are IDR 32,667,489.88 and IDR 38,453,900,000,000.00. In this paper the prediction of the pure premium eligible charged to the insured is obtained, which amounting to IDR 2,722,290.82. The prediction of the claims and premiums aggregate can be used as a reference for the insurance company’s decision-making in management of reserves and premiums of motor vehicle insurance.

Predicting microbial water quality with models: over-arching questions for managing risk in agricultural catchments

USDA-ARS?s Scientific Manuscript database

Determining the microbial quality of recreational, irrigation and shellfish-harvesting waters is important to ensure compliance with health-related standards and associated legislation. Animal faeces represent a significant human health risk, and concentrations of fecal indicator organisms (FIOs) pr...

Use of Quantitative Microbial Risk Assessment to Improve Interpretation of a Recreational Water Epidemiological Study

EPA Science Inventory

We conducted a supplemental water quality monitoring study and quantitative microbial risk assessment (QMRA) to complement the United States Environmental Protection Agency’s (U.S. EPA) National Epidemiological and Environmental Assessment of Recreational Water study at Boquerón ...

Incremental diagnostic quality gain of CTA over V/Q scan in the assessment of pulmonary embolism by means of a Wells score Bayesian model: results from the ACDC collaboration.

PubMed

Cochon, Laila; McIntyre, Kaitlin; Nicolás, José M; Baez, Amado Alejandro

2017-08-01

Our objective was to evaluate the diagnostic value of computed tomography angiography (CTA) and ventilation perfusion (V/Q) scan in the assessment of pulmonary embolism (PE) by means of a Bayesian statistical model. Wells criteria defined pretest probability. Sensitivity and specificity of CTA and V/Q scan for PE were derived from pooled meta-analysis data. Likelihood ratios calculated for CTA and V/Q were inserted in the nomogram. Absolute (ADG) and relative diagnostic gains (RDG) were analyzed comparing post- and pretest probability. Comparative gain difference was calculated for CTA ADG over V/Q scan integrating ANOVA p value set at 0.05. The sensitivity for CT was 86.0% (95% CI: 80.2%, 92.1%) and specificity of 93.7% (95% CI: 91.1%, 96.3%). The V/Q scan yielded a sensitivity of 96% (95% CI: 95%, 97%) and a specificity of 97% (95% CI: 96%, 98%). Bayes nomogram results for CTA were low risk and yielded a posttest probability of 71.1%, an ADG of 56.1%, and an RDG of 374%, moderate-risk posttest probability was 85.1%, an ADG of 56.1%, and an RDG of 193.4%, and high-risk posttest probability was 95.2%, an ADG of 36.2%, and an RDG of 61.35%. The comparative gain difference for low-risk population was 46.1%; in moderate-risk 41.6%; and in high-risk a 22.1% superiority. ANOVA analysis for LR+ and LR- showed no significant difference (p = 0.8745, p = 0.9841 respectively). This Bayesian model demonstrated a superiority of CTA when compared to V/Q scan for the diagnosis of pulmonary embolism. Low-risk patients are recognized to have a superior overall comparative gain favoring CTA.

Potable Water Reuse: What Are the Microbiological Risks?

PubMed

Nappier, Sharon P; Soller, Jeffrey A; Eftim, Sorina E

2018-06-01

With the increasing interest in recycling water for potable reuse purposes, it is important to understand the microbial risks associated with potable reuse. This review focuses on potable reuse systems that use high-level treatment and de facto reuse scenarios that include a quantifiable wastewater effluent component. In this article, we summarize the published human health studies related to potable reuse, including both epidemiology studies and quantitative microbial risk assessments (QMRA). Overall, there have been relatively few health-based studies evaluating the microbial risks associated with potable reuse. Several microbial risk assessments focused on risks associated with unplanned (or de facto) reuse, while others evaluated planned potable reuse, such as indirect potable reuse (IPR) or direct potable reuse (DPR). The reported QMRA-based risks for planned potable reuse varied substantially, indicating there is a need for risk assessors to use consistent input parameters and transparent assumptions, so that risk results are easily translated across studies. However, the current results overall indicate that predicted risks associated with planned potable reuse scenarios may be lower than those for de facto reuse scenarios. Overall, there is a clear need to carefully consider water treatment train choices when wastewater is a component of the drinking water supply (whether de facto, IPR, or DPR). More data from full-scale water treatment facilities would be helpful to quantify levels of viruses in raw sewage and reductions across unit treatment processes for both culturable and molecular detection methods.

Harnessing the theoretical foundations of the exponential and beta-Poisson dose-response models to quantify parameter uncertainty using Markov Chain Monte Carlo.

PubMed

Schmidt, Philip J; Pintar, Katarina D M; Fazil, Aamir M; Topp, Edward

2013-09-01

Dose-response models are the essential link between exposure assessment and computed risk values in quantitative microbial risk assessment, yet the uncertainty that is inherent to computed risks because the dose-response model parameters are estimated using limited epidemiological data is rarely quantified. Second-order risk characterization approaches incorporating uncertainty in dose-response model parameters can provide more complete information to decisionmakers by separating variability and uncertainty to quantify the uncertainty in computed risks. Therefore, the objective of this work is to develop procedures to sample from posterior distributions describing uncertainty in the parameters of exponential and beta-Poisson dose-response models using Bayes's theorem and Markov Chain Monte Carlo (in OpenBUGS). The theoretical origins of the beta-Poisson dose-response model are used to identify a decomposed version of the model that enables Bayesian analysis without the need to evaluate Kummer confluent hypergeometric functions. Herein, it is also established that the beta distribution in the beta-Poisson dose-response model cannot address variation among individual pathogens, criteria to validate use of the conventional approximation to the beta-Poisson model are proposed, and simple algorithms to evaluate actual beta-Poisson probabilities of infection are investigated. The developed MCMC procedures are applied to analysis of a case study data set, and it is demonstrated that an important region of the posterior distribution of the beta-Poisson dose-response model parameters is attributable to the absence of low-dose data. This region includes beta-Poisson models for which the conventional approximation is especially invalid and in which many beta distributions have an extreme shape with questionable plausibility. © Her Majesty the Queen in Right of Canada 2013. Reproduced with the permission of the Minister of the Public Health Agency of Canada.

Classifying and Tracking Dust Plumes from Passive Remote Sensing

NASA Astrophysics Data System (ADS)

Bachl, Fabian E.; Garbe, Christoph S.

2012-03-01

Recent studies emphasize the role mineral dust aerosols play in terms of the earth's climate system, its radiation budget and microbial nutrition cycles. In order to gain further insight into the genesis and long term characteristics of dust events, processing setellite imagery is inevitable. We propose a fully Bayesian multispectral classification method that significantly facilitates this task. Using MSG-SEVIRI imagery we show that our technique allows to extract dust activity well enough to pave the way for a tracking scheme. Based on this procedure we derive an approach to identify regions that are likely to be the origin of emerging dust plumes.

Human System Risk Management - Tools of our Trade

NASA Technical Reports Server (NTRS)

Ott, C. Mark

2009-01-01

The risk of infectious disease to select individuals has historically been difficult to predict in either spaceflight or on Earth with health care efforts relying on broad-based prevention and post-infection treatment. Over the past 10 years, quantitative microbial risk assessment evaluations have evolved to formalize the assessment process and quantify the risk. This process of hazard identification, exposure assessment, dose-response assessment, and risk characterization has been applied by the water and food safety industries to address the public health impacts associated with the occurrence of and human exposure to pathogens in water and food for the development of preventive strategies for microbial disease. NASA is currently investigating the feasibility of using these techniques to better understand the risks to astronauts and refine their microbiological requirements. To assess these techniques, NASA began an evaluation of the potable water system on the International Space Station to determine how the microbial risk from water consumption during flight differed from terrestrial sources, such as municipal water systems. The ultimate goal of this work is to optimize microbial requirements which would minimize unnecessary cargo and use of crew time, while still protecting the health of the crew. Successful demonstration of this risk assessment framework with the water system holds the potential to maximize the use of available resources during spaceflight missions and facilitate investigations into the evaluation of other routes of infection, such as through the spaceflight foods system.

Role of Waterborne Pathogens in the Food Supply Chain: Implications to Risk Management with Local and Global Perspectives

EPA Science Inventory

Microbial risk assessment (MRA) in the food industry is used to support HACCP – which largely focuses on bacterial pathogen control in processing foodstuffs Potential role of microbially-contaminated water used in food production is not as well understood Emergence...

Agreement between quantitative microbial risk assessment and epidemiology at low doses during waterborne outbreaks of protozoan disease

USDA-ARS?s Scientific Manuscript database

Quantitative microbial risk assessment (QMRA) is a valuable complement to epidemiology for understanding the health impacts of waterborne pathogens. The approach works by extrapolating available data in two ways. First, dose-response data are typically extrapolated from feeding studies, which use ...

Review of pathogen treatment reductions for onsite non-potable reuse of alternative source waters

EPA Science Inventory

Communities face a challenge when implementing onsite reuse of collected waters for non-potable purposes given the lack of national microbial standards. Quantitative Microbial Risk Assessment (QMRA) can be used to predict the pathogen risks associated with the non-potable reuse o...

Bayesian regression analyses of radiation modality effects on pericardial and pleural effusion and survival in esophageal cancer.

PubMed

He, Liru; Chapple, Andrew; Liao, Zhongxing; Komaki, Ritsuko; Thall, Peter F; Lin, Steven H

2016-10-01

To evaluate radiation modality effects on pericardial effusion (PCE), pleural effusion (PE) and survival in esophageal cancer (EC) patients. We analyzed data from 470 EC patients treated with definitive concurrent chemoradiotherapy (CRT). Bayesian semi-competing risks (SCR) regression models were fit to assess effects of radiation modality and prognostic covariates on the risks of PCE and PE, and death either with or without these preceding events. Bayesian piecewise exponential regression models were fit for overall survival, the time to PCE or death, and the time to PE or death. All models included propensity score as a covariate to correct for potential selection bias. Median times to onset of PCE and PE after RT were 7.1 and 6.1months for IMRT, and 6.5 and 5.4months for 3DCRT, respectively. Compared to 3DCRT, the IMRT group had significantly lower risks of PE, PCE, and death. The respective probabilities of a patient being alive without either PCE or PE at 3-years and 5-years were 0.29 and 0.21 for IMRT compared to 0.13 and 0.08 for 3DCRT. In the SCR regression analyses, IMRT was associated with significantly lower risks of PCE (HR=0.26) and PE (HR=0.49), and greater overall survival (probability of beneficial effect (pbe)>0.99), after controlling for known clinical prognostic factors. IMRT reduces the incidence and postpones the onset of PCE and PE, and increases survival probability, compared to 3DCRT. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Modelling pathogen log10 reduction values achieved by activated sludge treatment using naïve and semi naïve Bayes network models.

PubMed

Carvajal, Guido; Roser, David J; Sisson, Scott A; Keegan, Alexandra; Khan, Stuart J

2015-11-15

Risk management for wastewater treatment and reuse have led to growing interest in understanding and optimising pathogen reduction during biological treatment processes. However, modelling pathogen reduction is often limited by poor characterization of the relationships between variables and incomplete knowledge of removal mechanisms. The aim of this paper was to assess the applicability of Bayesian belief network models to represent associations between pathogen reduction, and operating conditions and monitoring parameters and predict AS performance. Naïve Bayes and semi-naïve Bayes networks were constructed from an activated sludge dataset including operating and monitoring parameters, and removal efficiencies for two pathogens (native Giardia lamblia and seeded Cryptosporidium parvum) and five native microbial indicators (F-RNA bacteriophage, Clostridium perfringens, Escherichia coli, coliforms and enterococci). First we defined the Bayesian network structures for the two pathogen log10 reduction values (LRVs) class nodes discretized into two states (< and ≥ 1 LRV) using two different learning algorithms. Eight metrics, such as Prediction Accuracy (PA) and Area Under the receiver operating Curve (AUC), provided a comparison of model prediction performance, certainty and goodness of fit. This comparison was used to select the optimum models. The optimum Tree Augmented naïve models predicted removal efficiency with high AUC when all system parameters were used simultaneously (AUCs for C. parvum and G. lamblia LRVs of 0.95 and 0.87 respectively). However, metrics for individual system parameters showed only the C. parvum model was reliable. By contrast individual parameters for G. lamblia LRV prediction typically obtained low AUC scores (AUC < 0.81). Useful predictors for C. parvum LRV included solids retention time, turbidity and total coliform LRV. The methodology developed appears applicable for predicting pathogen removal efficiency in water treatment systems generally. Copyright © 2015 Elsevier Ltd. All rights reserved.

BOP2: Bayesian optimal design for phase II clinical trials with simple and complex endpoints.

PubMed

Zhou, Heng; Lee, J Jack; Yuan, Ying

2017-09-20

We propose a flexible Bayesian optimal phase II (BOP2) design that is capable of handling simple (e.g., binary) and complicated (e.g., ordinal, nested, and co-primary) endpoints under a unified framework. We use a Dirichlet-multinomial model to accommodate different types of endpoints. At each interim, the go/no-go decision is made by evaluating a set of posterior probabilities of the events of interest, which is optimized to maximize power or minimize the number of patients under the null hypothesis. Unlike other existing Bayesian designs, the BOP2 design explicitly controls the type I error rate, thereby bridging the gap between Bayesian designs and frequentist designs. In addition, the stopping boundary of the BOP2 design can be enumerated prior to the onset of the trial. These features make the BOP2 design accessible to a wide range of users and regulatory agencies and particularly easy to implement in practice. Simulation studies show that the BOP2 design has favorable operating characteristics with higher power and lower risk of incorrectly terminating the trial than some existing Bayesian phase II designs. The software to implement the BOP2 design is freely available at www.trialdesign.org. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Microbial Contamination Detection in Water Resources: Interest of Current Optical Methods, Trends and Needs in the Context of Climate Change

PubMed Central

Jung, Aude-Valérie; Le Cann, Pierre; Roig, Benoit; Thomas, Olivier; Baurès, Estelle; Thomas, Marie-Florence

2014-01-01

Microbial pollution in aquatic environments is one of the crucial issues with regard to the sanitary state of water bodies used for drinking water supply, recreational activities and harvesting seafood due to a potential contamination by pathogenic bacteria, protozoa or viruses. To address this risk, microbial contamination monitoring is usually assessed by turbidity measurements performed at drinking water plants. Some recent studies have shown significant correlations of microbial contamination with the risk of endemic gastroenteresis. However the relevance of turbidimetry may be limited since the presence of colloids in water creates interferences with the nephelometric response. Thus there is a need for a more relevant, simple and fast indicator for microbial contamination detection in water, especially in the perspective of climate change with the increase of heavy rainfall events. This review focuses on the one hand on sources, fate and behavior of microorganisms in water and factors influencing pathogens’ presence, transportation and mobilization, and on the second hand, on the existing optical methods used for monitoring microbiological risks. Finally, this paper proposes new ways of research. PMID:24747537

Monitoring of Microbial Loads During Long Duration Missions as a Risk Reduction Tool

NASA Astrophysics Data System (ADS)

Roman, M. C.; Mena, K. D.

2012-01-01

Humans have been exploring space for more than 40 years. For all those years, microorganisms have accompanied both un-manned spacecraft/cargo and manned vessels. Microorganisms are everywhere on Earth, could easily adapt to new environments, and/or can rapidly mutate to survive in very harsh conditions. Their presence in spacecraft and cargo have caused a few inconveniences over the years of human spaceflight, ranging from crew health, life support systems challenges, and material degradation. The sterilization of spacecraft that will host humans in long duration mission would be a costly operation that will not provide a long-term solution to the microbial colonization of the vessels. As soon as a human is exposed to the spacecraft, microorganisms start populating the new environment during the mission. As the human presence in space increases in length, the risk from the microbial load to hardware and crew will also increase. Mitigation of this risk involves several different strategies that will include minimizing the microbial load (in numbers and diversity) and monitoring. This paper will provide a list of the risk mitigation strategies that should be implemented during ground processing, and during the mission. It will also discuss the areas that should be reviewed before an effective in-flight microbial monitoring regimen is implemented.

Bayesian-network-based safety risk assessment for steel construction projects.

PubMed

Leu, Sou-Sen; Chang, Ching-Miao

2013-05-01

There are four primary accident types at steel building construction (SC) projects: falls (tumbles), object falls, object collapse, and electrocution. Several systematic safety risk assessment approaches, such as fault tree analysis (FTA) and failure mode and effect criticality analysis (FMECA), have been used to evaluate safety risks at SC projects. However, these traditional methods ineffectively address dependencies among safety factors at various levels that fail to provide early warnings to prevent occupational accidents. To overcome the limitations of traditional approaches, this study addresses the development of a safety risk-assessment model for SC projects by establishing the Bayesian networks (BN) based on fault tree (FT) transformation. The BN-based safety risk-assessment model was validated against the safety inspection records of six SC building projects and nine projects in which site accidents occurred. The ranks of posterior probabilities from the BN model were highly consistent with the accidents that occurred at each project site. The model accurately provides site safety-management abilities by calculating the probabilities of safety risks and further analyzing the causes of accidents based on their relationships in BNs. In practice, based on the analysis of accident risks and significant safety factors, proper preventive safety management strategies can be established to reduce the occurrence of accidents on SC sites. Copyright © 2013 Elsevier Ltd. All rights reserved.

Discerning strain effects in microbial dose-response data.

PubMed

Coleman, Margaret E; Marks, Harry M; Golden, Neal J; Latimer, Heejeong K

In order to estimate the risk or probability of adverse events in risk assessment, it is necessary to identify the important variables that contribute to the risk and provide descriptions of distributions of these variables for well-defined populations. One component of modeling dose response that can create uncertainty is the inherent genetic variability among pathogenic bacteria. For many microbial risk assessments, the "default" assumption used for dose response does not account for strain or serotype variability in pathogenicity and virulence, other than perhaps, recognizing the existence of avirulent strains. However, an examination of data sets from human clinical trials in which Salmonella spp. and Campylobacter jejuni strains were administered reveals significant strain differences. This article discusses the evidence for strain variability and concludes that more biologically based alternatives are necessary to replace the default assumptions commonly used in microbial risk assessment, specifically regarding strain variability.

The impact of using informative priors in a Bayesian cost-effectiveness analysis: an application of endovascular versus open surgical repair for abdominal aortic aneurysms in high-risk patients.

PubMed

McCarron, C Elizabeth; Pullenayegum, Eleanor M; Thabane, Lehana; Goeree, Ron; Tarride, Jean-Eric

2013-04-01

Bayesian methods have been proposed as a way of synthesizing all available evidence to inform decision making. However, few practical applications of the use of Bayesian methods for combining patient-level data (i.e., trial) with additional evidence (e.g., literature) exist in the cost-effectiveness literature. The objective of this study was to compare a Bayesian cost-effectiveness analysis using informative priors to a standard non-Bayesian nonparametric method to assess the impact of incorporating additional information into a cost-effectiveness analysis. Patient-level data from a previously published nonrandomized study were analyzed using traditional nonparametric bootstrap techniques and bivariate normal Bayesian models with vague and informative priors. Two different types of informative priors were considered to reflect different valuations of the additional evidence relative to the patient-level data (i.e., "face value" and "skeptical"). The impact of using different distributions and valuations was assessed in a sensitivity analysis. Models were compared in terms of incremental net monetary benefit (INMB) and cost-effectiveness acceptability frontiers (CEAFs). The bootstrapping and Bayesian analyses using vague priors provided similar results. The most pronounced impact of incorporating the informative priors was the increase in estimated life years in the control arm relative to what was observed in the patient-level data alone. Consequently, the incremental difference in life years originally observed in the patient-level data was reduced, and the INMB and CEAF changed accordingly. The results of this study demonstrate the potential impact and importance of incorporating additional information into an analysis of patient-level data, suggesting this could alter decisions as to whether a treatment should be adopted and whether more information should be acquired.

Evaluation of Flagging Criteria of United States Kidney Transplant Center Performance: How to Best Define Outliers?

PubMed

Schold, Jesse D; Miller, Charles M; Henry, Mitchell L; Buccini, Laura D; Flechner, Stuart M; Goldfarb, David A; Poggio, Emilio D; Andreoni, Kenneth A

2017-06-01

Scientific Registry of Transplant Recipients report cards of US organ transplant center performance are publicly available and used for quality oversight. Low center performance (LP) evaluations are associated with changes in practice including reduced transplant rates and increased waitlist removals. In 2014, Scientific Registry of Transplant Recipients implemented new Bayesian methodology to evaluate performance which was not adopted by Center for Medicare and Medicaid Services (CMS). In May 2016, CMS altered their performance criteria, reducing the likelihood of LP evaluations. Our aims were to evaluate incidence, survival rates, and volume of LP centers with Bayesian, historical (old-CMS) and new-CMS criteria using 6 consecutive program-specific reports (PSR), January 2013 to July 2015 among adult kidney transplant centers. Bayesian, old-CMS and new-CMS criteria identified 13.4%, 8.3%, and 6.1% LP PSRs, respectively. Over the 3-year period, 31.9% (Bayesian), 23.4% (old-CMS), and 19.8% (new-CMS) of centers had 1 or more LP evaluation. For small centers (<83 transplants/PSR), there were 4-fold additional LP evaluations (52 vs 13 PSRs) for 1-year mortality with Bayesian versus new-CMS criteria. For large centers (>183 transplants/PSR), there were 3-fold additional LP evaluations for 1-year mortality with Bayesian versus new-CMS criteria with median differences in observed and expected patient survival of -1.6% and -2.2%, respectively. A significant proportion of kidney transplant centers are identified as low performing with relatively small survival differences compared with expected. Bayesian criteria have significantly higher flagging rates and new-CMS criteria modestly reduce flagging. Critical appraisal of performance criteria is needed to assess whether quality oversight is meeting intended goals and whether further modifications could reduce risk aversion, more efficiently allocate resources, and increase transplant opportunities.

Risk mapping of clonorchiasis in the People’s Republic of China: A systematic review and Bayesian geostatistical analysis

PubMed Central

Lai, Ying-Si; Zhou, Xiao-Nong; Pan, Zhi-Heng; Utzinger, Jürg; Vounatsou, Penelope

2017-01-01

Background Clonorchiasis, one of the most important food-borne trematodiases, affects more than 12 million people in the People’s Republic of China (P.R. China). Spatially explicit risk estimates of Clonorchis sinensis infection are needed in order to target control interventions. Methodology Georeferenced survey data pertaining to infection prevalence of C. sinensis in P.R. China from 2000 onwards were obtained via a systematic review in PubMed, ISI Web of Science, Chinese National Knowledge Internet, and Wanfang Data from January 1, 2000 until January 10, 2016, with no restriction of language or study design. Additional disease data were provided by the National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention in Shanghai. Environmental and socioeconomic proxies were extracted from remote-sensing and other data sources. Bayesian variable selection was carried out to identify the most important predictors of C. sinensis risk. Geostatistical models were applied to quantify the association between infection risk and the predictors of the disease, and to predict the risk of infection across P.R. China at high spatial resolution (over a grid with grid cell size of 5×5 km). Principal findings We obtained clonorchiasis survey data at 633 unique locations in P.R. China. We observed that the risk of C. sinensis infection increased over time, particularly from 2005 onwards. We estimate that around 14.8 million (95% Bayesian credible interval 13.8–15.8 million) people in P.R. China were infected with C. sinensis in 2010. Highly endemic areas (≥ 20%) were concentrated in southern and northeastern parts of the country. The provinces with the highest risk of infection and the largest number of infected people were Guangdong, Guangxi, and Heilongjiang. Conclusions/Significance Our results provide spatially relevant information for guiding clonorchiasis control interventions in P.R. China. The trend toward higher risk of C. sinensis infection in the recent past urges the Chinese government to pay more attention to the public health importance of clonorchiasis and to target interventions to high-risk areas. PMID:28253272

Hydrological modelling in a drinking water catchment area as a means of evaluating pathogen risk reduction

NASA Astrophysics Data System (ADS)

Bergion, Viktor; Sokolova, Ekaterina; Åström, Johan; Lindhe, Andreas; Sörén, Kaisa; Rosén, Lars

2017-01-01

Waterborne outbreaks of gastrointestinal diseases are of great concern to drinking water producers and can give rise to substantial costs to the society. The World Health Organisation promotes an approach where the emphasis is on mitigating risks close to the contamination source. In order to handle microbial risks efficiently, there is a need for systematic risk management. In this paper we present a framework for microbial risk management of drinking water systems. The framework incorporates cost-benefit analysis as a decision support method. The hydrological Soil and Water Assessment Tool (SWAT) model, which was set up for the Stäket catchment area in Sweden, was used to simulate the effects of four different mitigation measures on microbial concentrations. The modelling results showed that the two mitigation measures that resulted in a significant (p < 0.05) reduction of Cryptosporidium spp. and Escherichia coli concentrations were a vegetative filter strip linked to cropland and improved treatment (by one Log10 unit) at the wastewater treatment plants. The mitigation measure with a vegetative filter strip linked to grazing areas resulted in a significant reduction of Cryptosporidium spp., but not of E. coli concentrations. The mitigation measure with enhancing the removal efficiency of all on-site wastewater treatment systems (total removal of 2 Log10 units) did not achieve any significant reduction of E. coli or Cryptosporidium spp. concentrations. The SWAT model was useful when characterising the effect of different mitigation measures on microbial concentrations. Hydrological modelling implemented within an appropriate risk management framework is a key decision support element as it identifies the most efficient alternative for microbial risk reduction.

Microbial ecology of terrestrial Antarctica: Are microbial systems at risk from human activities?

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

White, G.J.

1996-08-01

Many of the ecological systems found in continental Antarctica are comprised entirely of microbial species. Concerns have arisen that these microbial systems might be at risk either directly through the actions of humans or indirectly through increased competition from introduced species. Although protection of native biota is covered by the Protocol on Environmental Protection to the Antarctic Treaty, strict measures for preventing the introduction on non-native species or for protecting microbial habitats may be impractical. This report summarizes the research conducted to date on microbial ecosystems in continental Antarctica and discusses the need for protecting these ecosystems. The focus ismore » on communities inhabiting soil and rock surfaces in non-coastal areas of continental Antarctica. Although current polices regarding waste management and other operations in Antarctic research stations serve to reduce the introduction on non- native microbial species, importation cannot be eliminated entirely. Increased awareness of microbial habitats by field personnel and protection of certain unique habitats from physical destruction by humans may be necessary. At present, small-scale impacts from human activities are occurring in certain areas both in terms of introduced species and destruction of habitat. On a large scale, however, it is questionable whether the introduction of non-native microbial species to terrestrial Antarctica merits concern.« less

Microbial and metabolic signatures of necrotizing enterocolitis in formula-fed piglets

USDA-ARS?s Scientific Manuscript database

Major risk factors for necrotizing enterocolitis (NEC) include premature birth, formula feeding, and microbial colonization of the gastrointestinal tract. We previously showed that feeding formula composed of lactose vs corn syrup solids protects against NEC in preterm pigs, however the microbial an...

76 FR 67439 - External Peer Review Meeting for Draft Microbial Risk Assessment Guideline: Pathogenic...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-01

... Draft Microbial Risk Assessment Guideline: Pathogenic Microorganisms With Focus on Food and Water AGENCY: Environmental Protection Agency (EPA). ACTION: Notice. SUMMARY: The Agency is announcing that Eastern Research... Water. EPA previously announced the release of the draft guidance for a 60 day comment period (76 FR...

IN SITU BIOREMEDIATION OF TRICHLOROETHYLENE USING BURKHOLDERIA CEPACIA G4 PR1: ANALYSIS OF MICROBIAL ECOLOGY PARAMETERS FOR RISK ASSESSMENT (RESEARCH BRIEF)

EPA Science Inventory

The introduction of bacteria into aquifers for bioremediation purposes requires monitoring of the persistence and activity of microbial populations for efficacy and risk assessment purposes. Burkholderia cepacia G4 PR1 constitutively expresses a toluene ortho-monooxygenase (tom) ...

A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people

NASA Astrophysics Data System (ADS)

Balbi, Stefano; Villa, Ferdinando; Mojtahed, Vahid; Hegetschweiler, Karin Tessa; Giupponi, Carlo

2016-06-01

This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quantitative data with subjective and local knowledge, improving the use of commonly available information; and produces estimates of model uncertainty by providing probability distributions for all of its outputs. Flood risk to people is modeled using a spatially explicit Bayesian network model calibrated on expert opinion. Risk is assessed in terms of (1) likelihood of non-fatal physical injury, (2) likelihood of post-traumatic stress disorder and (3) likelihood of death. The study area covers the lower part of the Sihl valley (Switzerland) including the city of Zurich. The model is used to estimate the effect of improving an existing early warning system, taking into account the reliability, lead time and scope (i.e., coverage of people reached by the warning). Model results indicate that the potential benefits of an improved early warning in terms of avoided human impacts are particularly relevant in case of a major flood event.

Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing methods: a simulation study

PubMed Central

2013-01-01

Background There is a rising public and political demand for prospective cancer cluster monitoring. But there is little empirical evidence on the performance of established cluster detection tests under conditions of small and heterogeneous sample sizes and varying spatial scales, such as are the case for most existing population-based cancer registries. Therefore this simulation study aims to evaluate different cluster detection methods, implemented in the open soure environment R, in their ability to identify clusters of lung cancer using real-life data from an epidemiological cancer registry in Germany. Methods Risk surfaces were constructed with two different spatial cluster types, representing a relative risk of RR = 2.0 or of RR = 4.0, in relation to the overall background incidence of lung cancer, separately for men and women. Lung cancer cases were sampled from this risk surface as geocodes using an inhomogeneous Poisson process. The realisations of the cancer cases were analysed within small spatial (census tracts, N = 1983) and within aggregated large spatial scales (communities, N = 78). Subsequently, they were submitted to the cluster detection methods. The test accuracy for cluster location was determined in terms of detection rates (DR), false-positive (FP) rates and positive predictive values. The Bayesian smoothing models were evaluated using ROC curves. Results With moderate risk increase (RR = 2.0), local cluster tests showed better DR (for both spatial aggregation scales > 0.90) and lower FP rates (both < 0.05) than the Bayesian smoothing methods. When the cluster RR was raised four-fold, the local cluster tests showed better DR with lower FPs only for the small spatial scale. At a large spatial scale, the Bayesian smoothing methods, especially those implementing a spatial neighbourhood, showed a substantially lower FP rate than the cluster tests. However, the risk increases at this scale were mostly diluted by data aggregation. Conclusion High resolution spatial scales seem more appropriate as data base for cancer cluster testing and monitoring than the commonly used aggregated scales. We suggest the development of a two-stage approach that combines methods with high detection rates as a first-line screening with methods of higher predictive ability at the second stage. PMID:24314148

Object-Oriented Bayesian Networks (OOBN) for Aviation Accident Modeling and Technology Portfolio Impact Assessment

NASA Technical Reports Server (NTRS)

Shih, Ann T.; Ancel, Ersin; Jones, Sharon M.

2012-01-01

The concern for reducing aviation safety risk is rising as the National Airspace System in the United States transforms to the Next Generation Air Transportation System (NextGen). The NASA Aviation Safety Program is committed to developing an effective aviation safety technology portfolio to meet the challenges of this transformation and to mitigate relevant safety risks. The paper focuses on the reasoning of selecting Object-Oriented Bayesian Networks (OOBN) as the technique and commercial software for the accident modeling and portfolio assessment. To illustrate the benefits of OOBN in a large and complex aviation accident model, the in-flight Loss-of-Control Accident Framework (LOCAF) constructed as an influence diagram is presented. An OOBN approach not only simplifies construction and maintenance of complex causal networks for the modelers, but also offers a well-organized hierarchical network that is easier for decision makers to exploit the model examining the effectiveness of risk mitigation strategies through technology insertions.

Reviewing Bayesian Networks potentials for climate change impacts assessment and management: A multi-risk perspective.

PubMed

Sperotto, Anna; Molina, José-Luis; Torresan, Silvia; Critto, Andrea; Marcomini, Antonio

2017-11-01

The evaluation and management of climate change impacts on natural and human systems required the adoption of a multi-risk perspective in which the effect of multiple stressors, processes and interconnections are simultaneously modelled. Despite Bayesian Networks (BNs) are popular integrated modelling tools to deal with uncertain and complex domains, their application in the context of climate change still represent a limited explored field. The paper, drawing on the review of existing applications in the field of environmental management, discusses the potential and limitation of applying BNs to improve current climate change risk assessment procedures. Main potentials include the advantage to consider multiple stressors and endpoints in the same framework, their flexibility in dealing and communicate with the uncertainty of climate projections and the opportunity to perform scenario analysis. Some limitations (i.e. representation of temporal and spatial dynamics, quantitative validation), however, should be overcome to boost BNs use in climate change impacts assessment and management. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Bayesian Approach to Determination of F, D, and Z Values Used in Steam Sterilization Validation.

PubMed

Faya, Paul; Stamey, James D; Seaman, John W

2017-01-01

For manufacturers of sterile drug products, steam sterilization is a common method used to provide assurance of the sterility of manufacturing equipment and products. The validation of sterilization processes is a regulatory requirement and relies upon the estimation of key resistance parameters of microorganisms. Traditional methods have relied upon point estimates for the resistance parameters. In this paper, we propose a Bayesian method for estimation of the well-known D T , z , and F o values that are used in the development and validation of sterilization processes. A Bayesian approach allows the uncertainty about these values to be modeled using probability distributions, thereby providing a fully risk-based approach to measures of sterility assurance. An example is given using the survivor curve and fraction negative methods for estimation of resistance parameters, and we present a means by which a probabilistic conclusion can be made regarding the ability of a process to achieve a specified sterility criterion. LAY ABSTRACT: For manufacturers of sterile drug products, steam sterilization is a common method used to provide assurance of the sterility of manufacturing equipment and products. The validation of sterilization processes is a regulatory requirement and relies upon the estimation of key resistance parameters of microorganisms. Traditional methods have relied upon point estimates for the resistance parameters. In this paper, we propose a Bayesian method for estimation of the critical process parameters that are evaluated in the development and validation of sterilization processes. A Bayesian approach allows the uncertainty about these parameters to be modeled using probability distributions, thereby providing a fully risk-based approach to measures of sterility assurance. An example is given using the survivor curve and fraction negative methods for estimation of resistance parameters, and we present a means by which a probabilistic conclusion can be made regarding the ability of a process to achieve a specified sterility criterion. © PDA, Inc. 2017.

Prediction of road accidents: A Bayesian hierarchical approach.

PubMed

Deublein, Markus; Schubert, Matthias; Adey, Bryan T; Köhler, Jochen; Faber, Michael H

2013-03-01

In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks in order to represent non-linearity between risk indicating and model response variables, as well as different types of uncertainties which might be present in the development of the specific models. Prior Bayesian Probabilistic Networks are first established by means of multivariate regression analysis of the observed frequencies of the model response variables, e.g. the occurrence of an accident, and observed values of the risk indicating variables, e.g. degree of road curvature. Subsequently, parameter learning is done using updating algorithms, to determine the posterior predictive probability distributions of the model response variables, conditional on the values of the risk indicating variables. The methodology is illustrated through a case study using data of the Austrian rural motorway network. In the case study, on randomly selected road segments the methodology is used to produce a model to predict the expected number of accidents in which an injury has occurred and the expected number of light, severe and fatally injured road users. Additionally, the methodology is used for geo-referenced identification of road sections with increased occurrence probabilities of injury accident events on a road link between two Austrian cities. It is shown that the proposed methodology can be used to develop models to estimate the occurrence of road accidents for any road network provided that the required data are available. Copyright © 2012 Elsevier Ltd. All rights reserved.

Hierarchical Bayesian method for mapping biogeochemical hot spots using induced polarization imaging

DOE PAGES

Wainwright, Haruko M.; Flores Orozco, Adrian; Bucker, Matthias; ...

2016-01-29

In floodplain environments, a naturally reduced zone (NRZ) is considered to be a common biogeochemical hot spot, having distinct microbial and geochemical characteristics. Although important for understanding their role in mediating floodplain biogeochemical processes, mapping the subsurface distribution of NRZs over the dimensions of a floodplain is challenging, as conventional wellbore data are typically spatially limited and the distribution of NRZs is heterogeneous. In this work, we present an innovative methodology for the probabilistic mapping of NRZs within a three-dimensional (3-D) subsurface domain using induced polarization imaging, which is a noninvasive geophysical technique. Measurements consist of surface geophysical surveys andmore » drilling-recovered sediments at the U.S. Department of Energy field site near Rifle, CO (USA). Inversion of surface time domain-induced polarization (TDIP) data yielded 3-D images of the complex electrical resistivity, in terms of magnitude and phase, which are associated with mineral precipitation and other lithological properties. By extracting the TDIP data values colocated with wellbore lithological logs, we found that the NRZs have a different distribution of resistivity and polarization from the other aquifer sediments. To estimate the spatial distribution of NRZs, we developed a Bayesian hierarchical model to integrate the geophysical and wellbore data. In addition, the resistivity images were used to estimate hydrostratigraphic interfaces under the floodplain. Validation results showed that the integration of electrical imaging and wellbore data using a Bayesian hierarchical model was capable of mapping spatially heterogeneous interfaces and NRZ distributions thereby providing a minimally invasive means to parameterize a hydrobiogeochemical model of the floodplain.« less

The Prevalences of Salmonella Genomic Island 1 Variants in Human and Animal Salmonella Typhimurium DT104 Are Distinguishable Using a Bayesian Approach

PubMed Central

Mather, Alison E.; Denwood, Matthew J.; Haydon, Daniel T.; Matthews, Louise; Mellor, Dominic J.; Coia, John E.; Brown, Derek J.; Reid, Stuart W. J.

2011-01-01

Throughout the 1990 s, there was an epidemic of multidrug resistant Salmonella Typhimurium DT104 in both animals and humans in Scotland. The use of antimicrobials in agriculture is often cited as a major source of antimicrobial resistance in pathogenic bacteria of humans, suggesting that DT104 in animals and humans should demonstrate similar prevalences of resistance determinants. Until very recently, only the application of molecular methods would allow such a comparison and our understanding has been hindered by the fact that surveillance data are primarily phenotypic in nature. Here, using large scale surveillance datasets and a novel Bayesian approach, we infer and compare the prevalence of Salmonella Genomic Island 1 (SGI1), SGI1 variants, and resistance determinants independent of SGI1 in animal and human DT104 isolates from such phenotypic data. We demonstrate differences in the prevalences of SGI1, SGI1-B, SGI1-C, absence of SGI1, and tetracycline resistance determinants independent of SGI1 between these human and animal populations, a finding that challenges established tenets that DT104 in domestic animals and humans are from the same well-mixed microbial population. PMID:22125606

A Bayesian approach to modelling the impact of hydrodynamic shear stress on biofilm deformation

PubMed Central

Wilkinson, Darren J.; Jayathilake, Pahala Gedara; Rushton, Steve P.; Bridgens, Ben; Li, Bowen; Zuliani, Paolo

2018-01-01

We investigate the feasibility of using a surrogate-based method to emulate the deformation and detachment behaviour of a biofilm in response to hydrodynamic shear stress. The influence of shear force, growth rate and viscoelastic parameters on the patterns of growth, structure and resulting shape of microbial biofilms was examined. We develop a statistical modelling approach to this problem, using combination of Bayesian Poisson regression and dynamic linear models for the emulation. We observe that the hydrodynamic shear force affects biofilm deformation in line with some literature. Sensitivity results also showed that the expected number of shear events, shear flow, yield coefficient for heterotrophic bacteria and extracellular polymeric substance (EPS) stiffness per unit EPS mass are the four principal mechanisms governing the bacteria detachment in this study. The sensitivity of the model parameters is temporally dynamic, emphasising the significance of conducting the sensitivity analysis across multiple time points. The surrogate models are shown to perform well, and produced ≈ 480 fold increase in computational efficiency. We conclude that a surrogate-based approach is effective, and resulting biofilm structure is determined primarily by a balance between bacteria growth, viscoelastic parameters and applied shear stress. PMID:29649240

Examining Gene-Environment Interactions in Comorbid Depressive and Disruptive Behavior Disorders using a Bayesian Approach

PubMed Central

Adrian, Molly; Kiff, Cara; Glazner, Chris; Kohen, Ruth; Tracy, Julia Helen; Zhou, Chuan; McCauley, Elizabeth; Stoep, Ann Vander

2015-01-01

Objective The objective of this study was to apply a Bayesian statistical analytic approach that minimizes multiple testing problems to explore the combined effects of chronic low familial support and variants in 12 candidate genes on risk for a common and debilitating childhood mental health condition. Method Bayesian mixture modeling was used to examine gene by environment interactions among genetic variants and environmental factors (family support) associated in previous studies with the occurrence of comorbid depression and disruptive behavior disorders youth, using a sample of 255 children. Results One main effects, variants in the oxytocin receptor (OXTR, rs53576) was associated with increased risk for comorbid disorders. Two significant gene x environment and one signification gene x gene interaction emerged. Variants in the nicotinic acetylcholine receptor α5 subunit (CHRNA5, rs16969968) and in the glucocorticoid receptor chaperone protein FK506 binding protein 5 (FKBP5, rs4713902) interacted with chronic low family support in association with child mental health status. One gene x gene interaction, 5-HTTLPR variant of the serotonin transporter (SERT/SLC6A4) in combination with μ opioid receptor (OPRM1, rs1799971) was associated with comorbid depression and conduct problems. Conclusions Results indicate that Bayesian modeling is a feasible strategy for conducting behavioral genetics research. This approach, combined with an optimized genetic selection strategy (Vrieze, Iacono, & McGue, 2012), revealed genetic variants involved in stress regulation ( FKBP5, SERTxOPMR), social bonding (OXTR), and nicotine responsivity (CHRNA5) in predicting comorbid status. PMID:26228411

A Bayesian CUSUM plot: Diagnosing quality of treatment.

PubMed

Rosthøj, Steen; Jacobsen, Rikke-Line

2017-12-01

To present a CUSUM plot based on Bayesian diagnostic reasoning displaying evidence in favour of "healthy" rather than "sick" quality of treatment (QOT), and to demonstrate a technique using Kaplan-Meier survival curves permitting application to case series with ongoing follow-up. For a case series with known final outcomes: Consider each case a diagnostic test of good versus poor QOT (expected vs. increased failure rates), determine the likelihood ratio (LR) of the observed outcome, convert LR to weight taking log to base 2, and add up weights sequentially in a plot showing how many times odds in favour of good QOT have been doubled. For a series with observed survival times and an expected survival curve: Divide the curve into time intervals, determine "healthy" and specify "sick" risks of failure in each interval, construct a "sick" survival curve, determine the LR of survival or failure at the given observation times, convert to weights, and add up. The Bayesian plot was applied retrospectively to 39 children with acute lymphoblastic leukaemia with completed follow-up, using Nordic collaborative results as reference, showing equal odds between good and poor QOT. In the ongoing treatment trial, with 22 of 37 children still at risk for event, QOT has been monitored with average survival curves as reference, odds so far favoring good QOT 2:1. QOT in small patient series can be assessed with a Bayesian CUSUM plot, retrospectively when all treatment outcomes are known, but also in ongoing series with unfinished follow-up. © 2017 John Wiley & Sons, Ltd.

Analysis of dengue fever risk using geostatistics model in bone regency

NASA Astrophysics Data System (ADS)

Amran, Stang, Mallongi, Anwar

2017-03-01

This research aim is to analysis of dengue fever risk based on Geostatistics model in Bone Regency. Risk levels of dengue fever are denoted by parameter of Binomial distribution. Effect of temperature, rainfalls, elevation, and larvae abundance are investigated through Geostatistics model. Bayesian hierarchical method is used in estimation process. Using dengue fever data in eleven locations this research shows that temperature and rainfall have significant effect of dengue fever risk in Bone regency.

An empirical Bayesian and Buhlmann approach with non-homogenous Poisson process

NASA Astrophysics Data System (ADS)

Noviyanti, Lienda

2015-12-01

All general insurance companies in Indonesia have to adjust their current premium rates according to maximum and minimum limit rates in the new regulation established by the Financial Services Authority (Otoritas Jasa Keuangan / OJK). In this research, we estimated premium rate by means of the Bayesian and the Buhlmann approach using historical claim frequency and claim severity in a five-group risk. We assumed a Poisson distributed claim frequency and a Normal distributed claim severity. Particularly, we used a non-homogenous Poisson process for estimating the parameters of claim frequency. We found that estimated premium rates are higher than the actual current rate. Regarding to the OJK upper and lower limit rates, the estimates among the five-group risk are varied; some are in the interval and some are out of the interval.

Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences.

PubMed

Constantinou, Anthony Costa; Yet, Barbaros; Fenton, Norman; Neil, Martin; Marsh, William

2016-01-01

Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision. The method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks. The method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%). We have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science. Copyright © 2015 Elsevier B.V. All rights reserved.

Bayesian networks: a new method for the modeling of bibliographic knowledge: application to fall risk assessment in geriatric patients.

PubMed

Lalande, Laure; Bourguignon, Laurent; Carlier, Chloé; Ducher, Michel

2013-06-01

Falls in geriatry are associated with important morbidity, mortality and high healthcare costs. Because of the large number of variables related to the risk of falling, determining patients at risk is a difficult challenge. The aim of this work was to validate a tool to detect patients with high risk of fall using only bibliographic knowledge. Thirty articles corresponding to 160 studies were used to modelize fall risk. A retrospective case-control cohort including 288 patients (88 ± 7 years) and a prospective cohort including 106 patients (89 ± 6 years) from two geriatric hospitals were used to validate the performances of our model. We identified 26 variables associated with an increased risk of fall. These variables were split into illnesses, medications, and environment. The combination of the three associated scores gives a global fall score. The sensitivity and the specificity were 31.4, 81.6, 38.5, and 90 %, respectively, for the retrospective and the prospective cohort. The performances of the model are similar to results observed with already existing prediction tools using model adjustment to data from numerous cohort studies. This work demonstrates that knowledge from the literature can be synthesized with Bayesian networks.

Periodic benefit-risk assessment using Bayesian stochastic multi-criteria acceptability analysis.

PubMed

Li, Kan; Yuan, Shuai Sammy; Wang, William; Wan, Shuyan Sabrina; Ceesay, Paulette; Heyse, Joseph F; Mt-Isa, Shahrul; Luo, Sheng

2018-04-01

Benefit-risk (BR) assessment is essential to ensure the best decisions are made for a medical product in the clinical development process, regulatory marketing authorization, post-market surveillance, and coverage and reimbursement decisions. One challenge of BR assessment in practice is that the benefit and risk profile may keep evolving while new evidence is accumulating. Regulators and the International Conference on Harmonization (ICH) recommend performing periodic benefit-risk evaluation report (PBRER) through the product's lifecycle. In this paper, we propose a general statistical framework for periodic benefit-risk assessment, in which Bayesian meta-analysis and stochastic multi-criteria acceptability analysis (SMAA) will be combined to synthesize the accumulating evidence. The proposed approach allows us to compare the acceptability of different drugs dynamically and effectively and accounts for the uncertainty of clinical measurements and imprecise or incomplete preference information of decision makers. We apply our approaches to two real examples in a post-hoc way for illustration purpose. The proposed method may easily be modified for other pre and post market settings, and thus be an important complement to the current structured benefit-risk assessment (sBRA) framework to improve the transparent and consistency of the decision-making process. Copyright © 2018 Elsevier Inc. All rights reserved.

PHYLOGENETIC AND FUNCTIONAL DIVERSITY OF SEAGULL AND CANADIAN GEESE FECAL MICROBIAL COMMUNITIES

EPA Science Inventory

In spite of increasing public health concerns on the risks associated with swimming in waters contaminated with waterfowl feces, there is little information on the gut microbial communities of aquatic birds. To address the molecular microbial diversity of waterfowl, 16S rDNA and ...

An Evaluation of Behavioral Health Compliance and Microbial Risk Factors on Student Populations within a High-Density Campus

ERIC Educational Resources Information Center

Decker, Jody F.; Slawson, Robin M.

2012-01-01

Objective: The aim of this Canadian study was to assess student behavioral response to disease transmission risk, while identifying high microbial deposition/transmission sites. Participants: A student survey was conducted during October 2009. Methods: The methods included a survey of students to assess use of health services, vaccination…

The Impact of Microbiology Instruction on Students' Perceptions of Risks Related to Microbial Illness

ERIC Educational Resources Information Center

Jones, Gail; Gardner, Grant E.; Lee, Tammy; Poland, Kayla; Robert, Sarah

2013-01-01

This study examined students' perceptions of the risks associated with microbial transmission before and after taking a microbiology class. Participants included undergraduate students (n = 132) enrolled in a microbiology course at two universities and one community college. Students completed a survey at the beginning and end of the course and a…

Family History as an Indicator of Risk for Reading Disability.

ERIC Educational Resources Information Center

Volger, George P.; And Others

1984-01-01

Self-reported reading ability of parents of 174 reading-disabled children and of 182 controls was used to estimate the probability that a child will become reading disabled. Using Bayesian inverse probability analysis, it was found that the risk for reading disability is increased substantially if either parent has had difficulty in learning to…

Status and risk of extinction for westslope cutthroat trout in the Upper River Basin, Montana

Treesearch

Bradley B. Shepard; Brian Sanborn; Linda Ulmer; Danny C. Lee

1997-01-01

Westslope cutthroat trout Oncorhynchus clarki lewisi now occupy less than 5% of the subspecies' historical range within the upper Missouri River drainage in Montana. We assessed the risk of extinction for 144 known populations inhabiting streams within federally managed lands in the upper Missouri River basin using a Bayesian...

Application of Bayesian networks in a hierarchical structure for environmental risk assessment: a case study of the Gabric Dam, Iran.

PubMed

Malekmohammadi, Bahram; Tayebzadeh Moghadam, Negar

2018-04-13

Environmental risk assessment (ERA) is a commonly used, effective tool applied to reduce adverse effects of environmental risk factors. In this study, ERA was investigated using the Bayesian network (BN) model based on a hierarchical structure of variables in an influence diagram (ID). ID facilitated ranking of the different alternatives under uncertainty that were then used to evaluate comparisons of the different risk factors. BN was used to present a new model for ERA applicable to complicated development projects such as dam construction. The methodology was applied to the Gabric Dam, in southern Iran. The main environmental risk factors in the region, presented by the Gabric Dam, were identified based on the Delphi technique and specific features of the study area. These included the following: flood, water pollution, earthquake, changes in land use, erosion and sedimentation, effects on the population, and ecosensitivity. These risk factors were then categorized based on results from the output decision node of the BN, including expected utility values for risk factors in the decision node. ERA was performed for the Gabric Dam using the analytical hierarchy process (AHP) method to compare results of BN modeling with those of conventional methods. Results determined that a BN-based hierarchical structure to ERA present acceptable and reasonable risk assessment prioritization in proposing suitable solutions to reduce environmental risks and can be used as a powerful decision support system for evaluating environmental risks.

Bayesian analysis of rare events

NASA Astrophysics Data System (ADS)

Straub, Daniel; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.

Application of bayesian networks to real-time flood risk estimation

NASA Astrophysics Data System (ADS)

Garrote, L.; Molina, M.; Blasco, G.

2003-04-01

This paper presents the application of a computational paradigm taken from the field of artificial intelligence - the bayesian network - to model the behaviour of hydrologic basins during floods. The final goal of this research is to develop representation techniques for hydrologic simulation models in order to define, develop and validate a mechanism, supported by a software environment, oriented to build decision models for the prediction and management of river floods in real time. The emphasis is placed on providing decision makers with tools to incorporate their knowledge of basin behaviour, usually formulated in terms of rainfall-runoff models, in the process of real-time decision making during floods. A rainfall-runoff model is only a step in the process of decision making. If a reliable rainfall forecast is available and the rainfall-runoff model is well calibrated, decisions can be based mainly on model results. However, in most practical situations, uncertainties in rainfall forecasts or model performance have to be incorporated in the decision process. The computation paradigm adopted for the simulation of hydrologic processes is the bayesian network. A bayesian network is a directed acyclic graph that represents causal influences between linked variables. Under this representation, uncertain qualitative variables are related through causal relations quantified with conditional probabilities. The solution algorithm allows the computation of the expected probability distribution of unknown variables conditioned to the observations. An approach to represent hydrologic processes by bayesian networks with temporal and spatial extensions is presented in this paper, together with a methodology for the development of bayesian models using results produced by deterministic hydrologic simulation models

A Web-Based System for Bayesian Benchmark Dose Estimation.

PubMed

Shao, Kan; Shapiro, Andrew J

2018-01-11

Benchmark dose (BMD) modeling is an important step in human health risk assessment and is used as the default approach to identify the point of departure for risk assessment. A probabilistic framework for dose-response assessment has been proposed and advocated by various institutions and organizations; therefore, a reliable tool is needed to provide distributional estimates for BMD and other important quantities in dose-response assessment. We developed an online system for Bayesian BMD (BBMD) estimation and compared results from this software with U.S. Environmental Protection Agency's (EPA's) Benchmark Dose Software (BMDS). The system is built on a Bayesian framework featuring the application of Markov chain Monte Carlo (MCMC) sampling for model parameter estimation and BMD calculation, which makes the BBMD system fundamentally different from the currently prevailing BMD software packages. In addition to estimating the traditional BMDs for dichotomous and continuous data, the developed system is also capable of computing model-averaged BMD estimates. A total of 518 dichotomous and 108 continuous data sets extracted from the U.S. EPA's Integrated Risk Information System (IRIS) database (and similar databases) were used as testing data to compare the estimates from the BBMD and BMDS programs. The results suggest that the BBMD system may outperform the BMDS program in a number of aspects, including fewer failed BMD and BMDL calculations and estimates. The BBMD system is a useful alternative tool for estimating BMD with additional functionalities for BMD analysis based on most recent research. Most importantly, the BBMD has the potential to incorporate prior information to make dose-response modeling more reliable and can provide distributional estimates for important quantities in dose-response assessment, which greatly facilitates the current trend for probabilistic risk assessment. https://doi.org/10.1289/EHP1289.

Bayesian updating in a fault tree model for shipwreck risk assessment.

PubMed

Landquist, H; Rosén, L; Lindhe, A; Norberg, T; Hassellöv, I-M

2017-07-15

Shipwrecks containing oil and other hazardous substances have been deteriorating on the seabeds of the world for many years and are threatening to pollute the marine environment. The status of the wrecks and the potential volume of harmful substances present in the wrecks are affected by a multitude of uncertainties. Each shipwreck poses a unique threat, the nature of which is determined by the structural status of the wreck and possible damage resulting from hazardous activities that could potentially cause a discharge. Decision support is required to ensure the efficiency of the prioritisation process and the allocation of resources required to carry out risk mitigation measures. Whilst risk assessments can provide the requisite decision support, comprehensive methods that take into account key uncertainties related to shipwrecks are limited. The aim of this paper was to develop a method for estimating the probability of discharge of hazardous substances from shipwrecks. The method is based on Bayesian updating of generic information on the hazards posed by different activities in the surroundings of the wreck, with information on site-specific and wreck-specific conditions in a fault tree model. Bayesian updating is performed using Monte Carlo simulations for estimating the probability of a discharge of hazardous substances and formal handling of intrinsic uncertainties. An example application involving two wrecks located off the Swedish coast is presented. Results show the estimated probability of opening, discharge and volume of the discharge for the two wrecks and illustrate the capability of the model to provide decision support. Together with consequence estimations of a discharge of hazardous substances, the suggested model enables comprehensive and probabilistic risk assessments of shipwrecks to be made. Copyright © 2017 Elsevier B.V. All rights reserved.

Empirical Bayesian Geographical Mapping of Occupational Accidents among Iranian Workers.

PubMed

Vahabi, Nasim; Kazemnejad, Anoshirvan; Datta, Somnath

2017-05-01

Work-related accidents are believed to be a serious preventable cause of mortality and disability worldwide. This study aimed to provide Bayesian geographical maps of occupational injury rates among workers insured by the Iranian Social Security Organization. The participants included all insured workers in the Iranian Social Security Organization database in 2012. One of the applications of the Bayesian approach called the Poisson-Gamma model was applied to estimate the relative risk of occupational accidents. Data analysis and mapping were performed using R 3.0.3, Open-Bugs 3.2.3 rev 1012 and ArcMap9.3. The majority of all 21,484 investigated occupational injury victims were male (98.3%) including 16,443 (76.5%) single workers aged 20 - 29 years. The accidents were more frequent in basic metal, electric, and non-electric machining jobs. About 0.4% (96) of work-related accidents led to death, 2.2% (457) led to disability (partial and total), 4.6% (980) led to fixed compensation, and 92.8% (19,951) of the injured victims recovered completely. The geographical maps of estimated relative risk of occupational accidents were also provided. The results showed that the highest estimations pertained to provinces which were mostly located along mountain chains, some of which are categorized as deprived provinces in Iran. The study revealed the need for further investigation of the role of economic and climatic factors in high risk areas. The application of geographical mapping together with statistical approaches can provide more accurate tools for policy makers to make better decisions in order to prevent and reduce the risks and adverse outcomes of work-related accidents.

From individual to population level effects of toxicants in the tubicifid Branchiura sowerbyi using threshold effect models in a Bayesian framework.

PubMed

Ducrot, Virginie; Billoir, Elise; Péry, Alexandre R R; Garric, Jeanne; Charles, Sandrine

2010-05-01

Effects of zinc were studied in the freshwater worm Branchiura sowerbyi using partial and full life-cycle tests. Only newborn and juveniles were sensitive to zinc, displaying effects on survival, growth, and age at first brood at environmentally relevant concentrations. Threshold effect models were proposed to assess toxic effects on individuals. They were fitted to life-cycle test data using Bayesian inference and adequately described life-history trait data in exposed organisms. The daily asymptotic growth rate of theoretical populations was then simulated with a matrix population model, based upon individual-level outputs. Population-level outputs were in accordance with existing literature for controls. Working in a Bayesian framework allowed incorporating parameter uncertainty in the simulation of the population-level response to zinc exposure, thus increasing the relevance of test results in the context of ecological risk assessment.

Predicting Software Suitability Using a Bayesian Belief Network

NASA Technical Reports Server (NTRS)

Beaver, Justin M.; Schiavone, Guy A.; Berrios, Joseph S.

2005-01-01

The ability to reliably predict the end quality of software under development presents a significant advantage for a development team. It provides an opportunity to address high risk components earlier in the development life cycle, when their impact is minimized. This research proposes a model that captures the evolution of the quality of a software product, and provides reliable forecasts of the end quality of the software being developed in terms of product suitability. Development team skill, software process maturity, and software problem complexity are hypothesized as driving factors of software product quality. The cause-effect relationships between these factors and the elements of software suitability are modeled using Bayesian Belief Networks, a machine learning method. This research presents a Bayesian Network for software quality, and the techniques used to quantify the factors that influence and represent software quality. The developed model is found to be effective in predicting the end product quality of small-scale software development efforts.

Improved control of multiple-antibiotic-resistance-related microbial risk in swine manure wastes by autothermal thermophilic aerobic digestion.

PubMed

Han, Il; Congeevaram, Shankar; Park, Joonhong

2009-01-01

In this study, we microbiologically evaluated antibiotic resistance and pathogenicity in livestock (swine) manure as well as its biologically stabilized products. One of new livestock manure stabilization techniques is ATAD (Autothermal Thermophilic Aerobic Digestion). Because of its high operation temperature (60-65 degrees C), it has been speculated to have effective microbial risk control in livestock manure. This hypothesis was tested by evaluating microbial risk in ATAD-treated swine manure. Antibiotic resistance, multiple antibiotic resistance (MAR), and pathogenicity were microbiologically examined for swine manure as well as its conventionally stabilized (anaerobically fermented) and ATAD-stabilized products. In the swine manure and its conventionally stabilized product, antibiotic resistant (tetracycline-, kanamycine-, ampicillin-, and rifampicin-resistant) bacteria and the pathogen indicator bacteria were detected. Furthermore, approximately 2-5% of the Staphylococcus and Salmonella colonies from their selective culture media were found to exhibit a MAR-phenotypes, suggesting a serious level of microbe induced health risk. In contrast, after the swine manure was stabilized with a pilot-scale ATAD treatment for 3 days at 60-65 degrees C, antibiotic resistant bacteria, pathogen indicator bacteria, and MAR-exhibiting pathogens were all undetected. These findings support the improved control of microbial risk in livestock wastes by ATAD treatment.

A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression.

PubMed

Liu, Fang; Eugenio, Evercita C

2018-04-01

Beta regression is an increasingly popular statistical technique in medical research for modeling of outcomes that assume values in (0, 1), such as proportions and patient reported outcomes. When outcomes take values in the intervals [0,1), (0,1], or [0,1], zero-or-one-inflated beta (zoib) regression can be used. We provide a thorough review on beta regression and zoib regression in the modeling, inferential, and computational aspects via the likelihood-based and Bayesian approaches. We demonstrate the statistical and practical importance of correctly modeling the inflation at zero/one rather than ad hoc replacing them with values close to zero/one via simulation studies; the latter approach can lead to biased estimates and invalid inferences. We show via simulation studies that the likelihood-based approach is computationally faster in general than MCMC algorithms used in the Bayesian inferences, but runs the risk of non-convergence, large biases, and sensitivity to starting values in the optimization algorithm especially with clustered/correlated data, data with sparse inflation at zero and one, and data that warrant regularization of the likelihood. The disadvantages of the regular likelihood-based approach make the Bayesian approach an attractive alternative in these cases. Software packages and tools for fitting beta and zoib regressions in both the likelihood-based and Bayesian frameworks are also reviewed.

Acute Gastrointestinal Illness Risks in North Carolina Community Water Systems: A Methodological Comparison.

PubMed

DeFelice, Nicholas B; Johnston, Jill E; Gibson, Jacqueline MacDonald

2015-08-18

The magnitude and spatial variability of acute gastrointestinal illness (AGI) cases attributable to microbial contamination of U.S. community drinking water systems are not well characterized. We compared three approaches (drinking water attributable risk, quantitative microbial risk assessment, and population intervention model) to estimate the annual number of emergency department visits for AGI attributable to microorganisms in North Carolina community water systems. All three methods used 2007-2013 water monitoring and emergency department data obtained from state agencies. The drinking water attributable risk method, which was the basis for previous U.S. Environmental Protection Agency national risk assessments, estimated that 7.9% of annual emergency department visits for AGI are attributable to microbial contamination of community water systems. However, the other methods' estimates were more than 2 orders of magnitude lower, each attributing 0.047% of annual emergency department visits for AGI to community water system contamination. The differences in results between the drinking water attributable risk method, which has been the main basis for previous national risk estimates, and the other two approaches highlight the need to improve methods for estimating endemic waterborne disease risks, in order to prioritize investments to improve community drinking water systems.

Bayesian Approach for Reliability Assessment of Sunshield Deployment on JWST

NASA Technical Reports Server (NTRS)

Kaminskiy, Mark P.; Evans, John W.; Gallo, Luis D.

2013-01-01

Deployable subsystems are essential to mission success of most spacecraft. These subsystems enable critical functions including power, communications and thermal control. The loss of any of these functions will generally result in loss of the mission. These subsystems and their components often consist of unique designs and applications, for which various standardized data sources are not applicable for estimating reliability and for assessing risks. In this study, a Bayesian approach for reliability estimation of spacecraft deployment was developed for this purpose. This approach was then applied to the James Webb Space Telescope (JWST) Sunshield subsystem, a unique design intended for thermal control of the observatory's telescope and science instruments. In order to collect the prior information on deployable systems, detailed studies of "heritage information", were conducted extending over 45 years of spacecraft launches. The NASA Goddard Space Flight Center (GSFC) Spacecraft Operational Anomaly and Reporting System (SOARS) data were then used to estimate the parameters of the conjugative beta prior distribution for anomaly and failure occurrence, as the most consistent set of available data and that could be matched to launch histories. This allows for an emperical Bayesian prediction for the risk of an anomaly occurrence of the complex Sunshield deployment, with credibility limits, using prior deployment data and test information.

A Gaussian random field model for similarity-based smoothing in Bayesian disease mapping.

PubMed

Baptista, Helena; Mendes, Jorge M; MacNab, Ying C; Xavier, Miguel; Caldas-de-Almeida, José

2016-08-01

Conditionally specified Gaussian Markov random field (GMRF) models with adjacency-based neighbourhood weight matrix, commonly known as neighbourhood-based GMRF models, have been the mainstream approach to spatial smoothing in Bayesian disease mapping. In the present paper, we propose a conditionally specified Gaussian random field (GRF) model with a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping. The model, named similarity-based GRF, is motivated for modelling disease mapping data in situations where the underlying small area relative risks and the associated determinant factors do not vary systematically in space, and the similarity is defined by "similarity" with respect to the associated disease determinant factors. The neighbourhood-based GMRF and the similarity-based GRF are compared and accessed via a simulation study and by two case studies, using new data on alcohol abuse in Portugal collected by the World Mental Health Survey Initiative and the well-known lip cancer data in Scotland. In the presence of disease data with no evidence of positive spatial correlation, the simulation study showed a consistent gain in efficiency from the similarity-based GRF, compared with the adjacency-based GMRF with the determinant risk factors as covariate. This new approach broadens the scope of the existing conditional autocorrelation models. © The Author(s) 2016.

Comparing the microbial risks associated with household drinking water supplies used in peri-urban communities of Phnom Penh, Cambodia.

PubMed

Thomas, K; McBean, E; Shantz, A; Murphy, H M

2015-03-01

Most Cambodians lack access to a safe source of drinking water. Piped distribution systems are typically limited to major urban centers in Cambodia, and the remaining population relies on a variety of surface, rain, and groundwater sources. This study examines the household water supplies available to Phnom Penh's resettled peri-urban residents through a case-study approach of two communities. A quantitative microbial risk assessment is performed to assess the level of diarrheal disease risk faced by community members due to microbial contamination of drinking water. Risk levels found in this study exceed those associated with households consuming piped water. Filtered and boiled rain and tank water stored in a kettle, bucket/cooler, bucket with spigot or a 500 mL bottle were found to provide risk levels within one order-of-magnitude to the piped water available in Phnom Penh. Two primary concerns identified are the negation of the risk reductions gained by boiling due to prevailing poor storage practices and the use of highly contaminated source water.

Goal-oriented Site Characterization in Hydrogeological Applications: An Overview

NASA Astrophysics Data System (ADS)

Nowak, W.; de Barros, F.; Rubin, Y.

2011-12-01

In this study, we address the importance of goal-oriented site characterization. Given the multiple sources of uncertainty in hydrogeological applications, information needs of modeling, prediction and decision support should be satisfied with efficient and rational field campaigns. In this work, we provide an overview of an optimal sampling design framework based on Bayesian decision theory, statistical parameter inference and Bayesian model averaging. It optimizes the field sampling campaign around decisions on environmental performance metrics (e.g., risk, arrival times, etc.) while accounting for parametric and model uncertainty in the geostatistical characterization, in forcing terms, and measurement error. The appealing aspects of the framework lie on its goal-oriented character and that it is directly linked to the confidence in a specified decision. We illustrate how these concepts could be applied in a human health risk problem where uncertainty from both hydrogeological and health parameters are accounted.

A Review of the Bayesian Occupancy Filter

PubMed Central

Saval-Calvo, Marcelo; Medina-Valdés, Luis; Castillo-Secilla, José María; Cuenca-Asensi, Sergio; Martínez-Álvarez, Antonio; Villagrá, Jorge

2017-01-01

Autonomous vehicle systems are currently the object of intense research within scientific and industrial communities; however, many problems remain to be solved. One of the most critical aspects addressed in both autonomous driving and robotics is environment perception, since it consists of the ability to understand the surroundings of the vehicle to estimate risks and make decisions on future movements. In recent years, the Bayesian Occupancy Filter (BOF) method has been developed to evaluate occupancy by tessellation of the environment. A review of the BOF and its variants is presented in this paper. Moreover, we propose a detailed taxonomy where the BOF is decomposed into five progressive layers, from the level closest to the sensor to the highest abstract level of risk assessment. In addition, we present a study of implemented use cases to provide a practical understanding on the main uses of the BOF and its taxonomy. PMID:28208638

Flood management: prediction of microbial contamination in large-scale floods in urban environments.

PubMed

Taylor, Jonathon; Lai, Ka Man; Davies, Mike; Clifton, David; Ridley, Ian; Biddulph, Phillip

2011-07-01

With a changing climate and increased urbanisation, the occurrence and the impact of flooding is expected to increase significantly. Floods can bring pathogens into homes and cause lingering damp and microbial growth in buildings, with the level of growth and persistence dependent on the volume and chemical and biological content of the flood water, the properties of the contaminating microbes, and the surrounding environmental conditions, including the restoration time and methods, the heat and moisture transport properties of the envelope design, and the ability of the construction material to sustain the microbial growth. The public health risk will depend on the interaction of these complex processes and the vulnerability and susceptibility of occupants in the affected areas. After the 2007 floods in the UK, the Pitt review noted that there is lack of relevant scientific evidence and consistency with regard to the management and treatment of flooded homes, which not only put the local population at risk but also caused unnecessary delays in the restoration effort. Understanding the drying behaviour of flooded buildings in the UK building stock under different scenarios, and the ability of microbial contaminants to grow, persist, and produce toxins within these buildings can help inform recovery efforts. To contribute to future flood management, this paper proposes the use of building simulations and biological models to predict the risk of microbial contamination in typical UK buildings. We review the state of the art with regard to biological contamination following flooding, relevant building simulation, simulation-linked microbial modelling, and current practical considerations in flood remediation. Using the city of London as an example, a methodology is proposed that uses GIS as a platform to integrate drying models and microbial risk models with the local building stock and flood models. The integrated tool will help local governments, health authorities, insurance companies and residents to better understand, prepare for and manage a large-scale flood in urban environments. Copyright © 2011 Elsevier Ltd. All rights reserved.

Risk-based cost-benefit analysis for evaluating microbial risk mitigation in a drinking water system.

PubMed

Bergion, Viktor; Lindhe, Andreas; Sokolova, Ekaterina; Rosén, Lars

2018-04-01

Waterborne outbreaks of gastrointestinal diseases can cause large costs to society. Risk management needs to be holistic and transparent in order to reduce these risks in an effective manner. Microbial risk mitigation measures in a drinking water system were investigated using a novel approach combining probabilistic risk assessment and cost-benefit analysis. Lake Vomb in Sweden was used to exemplify and illustrate the risk-based decision model. Four mitigation alternatives were compared, where the first three alternatives, A1-A3, represented connecting 25, 50 and 75%, respectively, of on-site wastewater treatment systems in the catchment to the municipal wastewater treatment plant. The fourth alternative, A4, represented installing a UV-disinfection unit in the drinking water treatment plant. Quantitative microbial risk assessment was used to estimate the positive health effects in terms of quality adjusted life years (QALYs), resulting from the four mitigation alternatives. The health benefits were monetised using a unit cost per QALY. For each mitigation alternative, the net present value of health and environmental benefits and investment, maintenance and running costs was calculated. The results showed that only A4 can reduce the risk (probability of infection) below the World Health Organization guidelines of 10 -4 infections per person per year (looking at the 95th percentile). Furthermore, all alternatives resulted in a negative net present value. However, the net present value would be positive (looking at the 50 th percentile using a 1% discount rate) if non-monetised benefits (e.g. increased property value divided evenly over the studied time horizon and reduced microbial risks posed to animals), estimated at 800-1200 SEK (€100-150) per connected on-site wastewater treatment system per year, were included. This risk-based decision model creates a robust and transparent decision support tool. It is flexible enough to be tailored and applied to local settings of drinking water systems. The model provides a clear and holistic structure for decisions related to microbial risk mitigation. To improve the decision model, we suggest to further develop the valuation and monetisation of health effects and to refine the propagation of uncertainties and variabilities between the included methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people

NASA Astrophysics Data System (ADS)

Balbi, S.; Villa, F.; Mojtahed, V.; Hegetschweiler, K. T.; Giupponi, C.

2015-10-01

This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quantitative data with subjective and local knowledge, improving the use of commonly available information; produces estimates of model uncertainty by providing probability distributions for all of its outputs. Flood risk to people is modeled using a spatially explicit Bayesian network model calibrated on expert opinion. Risk is assessed in terms of: (1) likelihood of non-fatal physical injury; (2) likelihood of post-traumatic stress disorder; (3) likelihood of death. The study area covers the lower part of the Sihl valley (Switzerland) including the city of Zurich. The model is used to estimate the benefits of improving an existing Early Warning System, taking into account the reliability, lead-time and scope (i.e. coverage of people reached by the warning). Model results indicate that the potential benefits of an improved early warning in terms of avoided human impacts are particularly relevant in case of a major flood event: about 75 % of fatalities, 25 % of injuries and 18 % of post-traumatic stress disorders could be avoided.

Uncertainty analysis for effluent trading planning using a Bayesian estimation-based simulation-optimization modeling approach.

PubMed

Zhang, J L; Li, Y P; Huang, G H; Baetz, B W; Liu, J

2017-06-01

In this study, a Bayesian estimation-based simulation-optimization modeling approach (BESMA) is developed for identifying effluent trading strategies. BESMA incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian estimation, and probabilistic-possibilistic interval programming with fuzzy random coefficients (PPI-FRC) within a general framework. Based on the water quality protocols provided by SWAT, posterior distributions of parameters can be analyzed through Bayesian estimation; stochastic characteristic of nutrient loading can be investigated which provides the inputs for the decision making. PPI-FRC can address multiple uncertainties in the form of intervals with fuzzy random boundaries and the associated system risk through incorporating the concept of possibility and necessity measures. The possibility and necessity measures are suitable for optimistic and pessimistic decision making, respectively. BESMA is applied to a real case of effluent trading planning in the Xiangxihe watershed, China. A number of decision alternatives can be obtained under different trading ratios and treatment rates. The results can not only facilitate identification of optimal effluent-trading schemes, but also gain insight into the effects of trading ratio and treatment rate on decision making. The results also reveal that decision maker's preference towards risk would affect decision alternatives on trading scheme as well as system benefit. Compared with the conventional optimization methods, it is proved that BESMA is advantageous in (i) dealing with multiple uncertainties associated with randomness and fuzziness in effluent-trading planning within a multi-source, multi-reach and multi-period context; (ii) reflecting uncertainties existing in nutrient transport behaviors to improve the accuracy in water quality prediction; and (iii) supporting pessimistic and optimistic decision making for effluent trading as well as promoting diversity of decision alternatives. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mapping malaria risk among children in Côte d'Ivoire using Bayesian geo-statistical models.

PubMed

Raso, Giovanna; Schur, Nadine; Utzinger, Jürg; Koudou, Benjamin G; Tchicaya, Emile S; Rohner, Fabian; N'goran, Eliézer K; Silué, Kigbafori D; Matthys, Barbara; Assi, Serge; Tanner, Marcel; Vounatsou, Penelope

2012-05-09

In Côte d'Ivoire, an estimated 767,000 disability-adjusted life years are due to malaria, placing the country at position number 14 with regard to the global burden of malaria. Risk maps are important to guide control interventions, and hence, the aim of this study was to predict the geographical distribution of malaria infection risk in children aged <16 years in Côte d'Ivoire at high spatial resolution. Using different data sources, a systematic review was carried out to compile and geo-reference survey data on Plasmodium spp. infection prevalence in Côte d'Ivoire, focusing on children aged <16 years. The period from 1988 to 2007 was covered. A suite of Bayesian geo-statistical logistic regression models was fitted to analyse malaria risk. Non-spatial models with and without exchangeable random effect parameters were compared to stationary and non-stationary spatial models. Non-stationarity was modelled assuming that the underlying spatial process is a mixture of separate stationary processes in each ecological zone. The best fitting model based on the deviance information criterion was used to predict Plasmodium spp. infection risk for entire Côte d'Ivoire, including uncertainty. Overall, 235 data points at 170 unique survey locations with malaria prevalence data for individuals aged <16 years were extracted. Most data points (n = 182, 77.4%) were collected between 2000 and 2007. A Bayesian non-stationary regression model showed the best fit with annualized rainfall and maximum land surface temperature identified as significant environmental covariates. This model was used to predict malaria infection risk at non-sampled locations. High-risk areas were mainly found in the north-central and western area, while relatively low-risk areas were located in the north at the country border, in the north-east, in the south-east around Abidjan, and in the central-west between two high prevalence areas. The malaria risk map at high spatial resolution gives an important overview of the geographical distribution of the disease in Côte d'Ivoire. It is a useful tool for the national malaria control programme and can be utilized for spatial targeting of control interventions and rational resource allocation.

Mapping malaria risk among children in Côte d’Ivoire using Bayesian geo-statistical models

PubMed Central

2012-01-01

Background In Côte d’Ivoire, an estimated 767,000 disability-adjusted life years are due to malaria, placing the country at position number 14 with regard to the global burden of malaria. Risk maps are important to guide control interventions, and hence, the aim of this study was to predict the geographical distribution of malaria infection risk in children aged <16 years in Côte d’Ivoire at high spatial resolution. Methods Using different data sources, a systematic review was carried out to compile and geo-reference survey data on Plasmodium spp. infection prevalence in Côte d’Ivoire, focusing on children aged <16 years. The period from 1988 to 2007 was covered. A suite of Bayesian geo-statistical logistic regression models was fitted to analyse malaria risk. Non-spatial models with and without exchangeable random effect parameters were compared to stationary and non-stationary spatial models. Non-stationarity was modelled assuming that the underlying spatial process is a mixture of separate stationary processes in each ecological zone. The best fitting model based on the deviance information criterion was used to predict Plasmodium spp. infection risk for entire Côte d’Ivoire, including uncertainty. Results Overall, 235 data points at 170 unique survey locations with malaria prevalence data for individuals aged <16 years were extracted. Most data points (n = 182, 77.4%) were collected between 2000 and 2007. A Bayesian non-stationary regression model showed the best fit with annualized rainfall and maximum land surface temperature identified as significant environmental covariates. This model was used to predict malaria infection risk at non-sampled locations. High-risk areas were mainly found in the north-central and western area, while relatively low-risk areas were located in the north at the country border, in the north-east, in the south-east around Abidjan, and in the central-west between two high prevalence areas. Conclusion The malaria risk map at high spatial resolution gives an important overview of the geographical distribution of the disease in Côte d’Ivoire. It is a useful tool for the national malaria control programme and can be utilized for spatial targeting of control interventions and rational resource allocation. PMID:22571469

Performance of two quantitative PCR methods for microbial source tracking of human sewage and implications for microbial risk assessment in recreational waters

EPA Science Inventory

Before new, rapid quantitative PCR (qPCR) methods for recreational water quality assessment and microbial source tracking (MST) can be useful in a regulatory context, an understanding of the ability of the method to detect a DNA target (marker) when the contaminant soure has been...

The multiple stressor ecological risk assessment for the mercury-contaminated South River and upper Shenandoah River using the Bayesian network-relative risk model.

PubMed

Landis, Wayne G; Ayre, Kimberley K; Johns, Annie F; Summers, Heather M; Stinson, Jonah; Harris, Meagan J; Herring, Carlie E; Markiewicz, April J

2017-01-01

We have conducted a regional scale risk assessment using the Bayesian Network Relative Risk Model (BN-RRM) to calculate the ecological risks to the South River and upper Shenandoah River study area. Four biological endpoints (smallmouth bass, white sucker, Belted Kingfisher, and Carolina Wren) and 4 abiotic endpoints (Fishing River Use, Swimming River Use, Boating River Use, and Water Quality Standards) were included in this risk assessment, based on stakeholder input. Although mercury (Hg) contamination was the original impetus for the site being remediated, other chemical and physical stressors were evaluated. There were 3 primary conclusions from the BN-RRM results. First, risk varies according to location, type and quality of habitat, and exposure to stressors within the landscape. The patterns of risk can be evaluated with reasonable certitude. Second, overall risk to abiotic endpoints was greater than overall risk to biotic endpoints. By including both biotic and abiotic endpoints, we are able to compare risk to endpoints that represent a wide range of stakeholder values. Third, whereas Hg reduction is the regulatory priority for the South River, Hg is not the only stressor driving risk to the endpoints. Ecological and habitat stressors contribute risk to the endpoints and should be considered when managing this site. This research provides the foundation for evaluating the risks of multiple stressors of the South River to a variety of endpoints. From this foundation, tools for the evaluation of management options and an adaptive management tools have been forged. Integr Environ Assess Manag 2017;13:85-99. © 2016 SETAC. © 2016 SETAC.

The epidemiology of microbial keratitis with silicone hydrogel contact lenses.

PubMed

Stapleton, Fiona; Keay, Lisa; Edwards, Katie; Holden, Brien

2013-01-01

It was widely anticipated that after the introduction of silicone hydrogel lenses, the risk of microbial keratitis would be lower than with hydrogel lenses because of the reduction in hypoxic effects on the corneal epithelium. Large-scale epidemiological studies have confirmed that the absolute and relative risk of microbial keratitis is unchanged with overnight use of silicone hydrogel materials. The key findings include the following: (1) The risk of infection with 30 nights of silicone hydrogel use is equivalent to 6 nights of hydrogel extended wear; (2) Occasional overnight lens use is associated with a greater risk than daily lens use; (3) The rate of vision loss due to corneal infection with silicone hydrogel contact lenses is similar to that seen in hydrogel lenses; (4) The spectrum of causative organisms is similar to that seen in hydrogel lenses, and the material type does not impact the corneal location of presumed microbial keratitis; and (5) Modifiable risk factors for infection include overnight lens use, the degree of exposure, failing to wash hands before lens handling, and storage case hygiene practice. The lack of change in the absolute risk of disease would suggest that exposure to large number of pathogenic organisms can overcome any advantages obtained from eliminating the hypoxic effects of contact lenses. Epidemiological studies remain important in the assessment of new materials and modalities. Consideration of an early adopter effect with studies involving new materials and modalities and further investigation of the impact of second-generation silicone hydrogel materials is warranted.

Potential Research and Development Synergies between Life support and Planetary protection

NASA Astrophysics Data System (ADS)

Lasseur, Ch.; Kminek, G.; Mergeay, M.

Long term manned missions of our Russian colleagues have demonstrated the risks associated with microbial contamination These risks concern both crew health via the metabolic consumables contamination water air but and also the hardware degradation Over the last six years ESA and IBMP have developed a collaboration to elaborate and document these microbial contamination issues The collaboration involved the mutual exchanges of knowledge as well as microbial samples and leads up to the microbial survey of the Russian module of the ISS Based on these results and in addition to an external expert report commissioned by ESA the agency initiated the development of a rapid and automated microbial detection and identification tool for use in future space missions In parallel to these developments and via several international meetings planetary protection experts have agreed to place clear specification of the microbial quality of future hardware landing on virgin planets as well as elaborate the preliminary requirements of contamination for manned missions on surface For these activities its is necessary to have a better understanding of microbial activity to create culture collection and to develop on-line detection tools Within this paper we present more deeply the life support activities related to microbial issues we identify some potential synergies with Planetary protection developments and we propose some pathway for collaboration between these two communities

A Risk Assessment of Antibiotic Pan-Drug-Resistance in the UK: Bayesian Analysis of an Expert Elicitation Study

PubMed Central

Carter, Daniel; Charlett, André; Conti, Stefano; Robotham, Julie V.; Johnson, Alan P.; Livermore, David M.; Fowler, Tom; Sharland, Mike; Hopkins, Susan; Woodford, Neil; Burgess, Philip; Dobra, Stephen

2017-01-01

To inform the UK antimicrobial resistance strategy, a risk assessment was undertaken of the likelihood, over a five-year time-frame, of the emergence and widespread dissemination of pan-drug-resistant (PDR) Gram-negative bacteria that would pose a major public health threat by compromising effective healthcare delivery. Subsequent impact over five- and 20-year time-frames was assessed in terms of morbidity and mortality attributable to PDR Gram-negative bacteraemia. A Bayesian approach, combining available data with expert prior opinion, was used to determine the probability of the emergence, persistence and spread of PDR bacteria. Overall probability was modelled using Monte Carlo simulation. Estimates of impact were also obtained using Bayesian methods. The estimated probability of widespread occurrence of PDR pathogens within five years was 0.2 (95% credibility interval (CrI): 0.07–0.37). Estimated annual numbers of PDR Gram-negative bacteraemias at five and 20 years were 6800 (95% CrI: 400–58,600) and 22,800 (95% CrI: 1500–160,000), respectively; corresponding estimates of excess deaths were 1900 (95% CrI: 0–23,000) and 6400 (95% CrI: 0–64,000). Over 20 years, cumulative estimates indicate 284,000 (95% CrI: 17,000–1,990,000) cases of PDR Gram-negative bacteraemia, leading to an estimated 79,000 (95% CrI: 0–821,000) deaths. This risk assessment reinforces the need for urgent national and international action to tackle antibiotic resistance. PMID:28272350

Bayesian analysis of rare events

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

Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into themore » probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.« less

Systems Reliability Framework for Surface Water Sustainability and Risk Management

NASA Astrophysics Data System (ADS)

Myers, J. R.; Yeghiazarian, L.

2016-12-01

With microbial contamination posing a serious threat to the availability of clean water across the world, it is necessary to develop a framework that evaluates the safety and sustainability of water systems in respect to non-point source fecal microbial contamination. The concept of water safety is closely related to the concept of failure in reliability theory. In water quality problems, the event of failure can be defined as the concentration of microbial contamination exceeding a certain standard for usability of water. It is pertinent in watershed management to know the likelihood of such an event of failure occurring at a particular point in space and time. Microbial fate and transport are driven by environmental processes taking place in complex, multi-component, interdependent environmental systems that are dynamic and spatially heterogeneous, which means these processes and therefore their influences upon microbial transport must be considered stochastic and variable through space and time. A physics-based stochastic model of microbial dynamics is presented that propagates uncertainty using a unique sampling method based on artificial neural networks to produce a correlation between watershed characteristics and spatial-temporal probabilistic patterns of microbial contamination. These results are used to address the question of water safety through several sustainability metrics: reliability, vulnerability, resilience and a composite sustainability index. System reliability is described uniquely though the temporal evolution of risk along watershed points or pathways. Probabilistic resilience describes how long the system is above a certain probability of failure, and the vulnerability metric describes how the temporal evolution of risk changes throughout a hierarchy of failure levels. Additionally our approach allows for the identification of contributions in microbial contamination and uncertainty from specific pathways and sources. We expect that this framework will significantly improve the efficiency and precision of sustainable watershed management strategies through providing a better understanding of how watershed characteristics and environmental parameters affect surface water quality and sustainability. With microbial contamination posing a serious threat to the availability of clean water across the world, it is necessary to develop a framework that evaluates the safety and sustainability of water systems in respect to non-point source fecal microbial contamination. The concept of water safety is closely related to the concept of failure in reliability theory. In water quality problems, the event of failure can be defined as the concentration of microbial contamination exceeding a certain standard for usability of water. It is pertinent in watershed management to know the likelihood of such an event of failure occurring at a particular point in space and time. Microbial fate and transport are driven by environmental processes taking place in complex, multi-component, interdependent environmental systems that are dynamic and spatially heterogeneous, which means these processes and therefore their influences upon microbial transport must be considered stochastic and variable through space and time. A physics-based stochastic model of microbial dynamics is presented that propagates uncertainty using a unique sampling method based on artificial neural networks to produce a correlation between watershed characteristics and spatial-temporal probabilistic patterns of microbial contamination. These results are used to address the question of water safety through several sustainability metrics: reliability, vulnerability, resilience and a composite sustainability index. System reliability is described uniquely though the temporal evolution of risk along watershed points or pathways. Probabilistic resilience describes how long the system is above a certain probability of failure, and the vulnerability metric describes how the temporal evolution of risk changes throughout a hierarchy of failure levels. Additionally our approach allows for the identification of contributions in microbial contamination and uncertainty from specific pathways and sources. We expect that this framework will significantly improve the efficiency and precision of sustainable watershed management strategies through providing a better understanding of how watershed characteristics and environmental parameters affect surface water quality and sustainability.

A Disease-Associated Microbial and Metabolomics State in Relatives of Pediatric Inflammatory Bowel Disease Patients.

PubMed

Jacobs, Jonathan P; Goudarzi, Maryam; Singh, Namita; Tong, Maomeng; McHardy, Ian H; Ruegger, Paul; Asadourian, Miro; Moon, Bo-Hyun; Ayson, Allyson; Borneman, James; McGovern, Dermot P B; Fornace, Albert J; Braun, Jonathan; Dubinsky, Marla

2016-11-01

Microbes may increase susceptibility to inflammatory bowel disease (IBD) by producing bioactive metabolites that affect immune activity and epithelial function. We undertook a family based study to identify microbial and metabolic features of IBD that may represent a predisease risk state when found in healthy first-degree relatives. Twenty-one families with pediatric IBD were recruited, comprising 26 Crohn's disease patients in clinical remission, 10 ulcerative colitis patients in clinical remission, and 54 healthy siblings/parents. Fecal samples were collected for 16S ribosomal RNA gene sequencing, untargeted liquid chromatography-mass spectrometry metabolomics, and calprotectin measurement. Individuals were grouped into microbial and metabolomics states using Dirichlet multinomial models. Multivariate models were used to identify microbes and metabolites associated with these states. Individuals were classified into 2 microbial community types. One was associated with IBD but irrespective of disease status, had lower microbial diversity, and characteristic shifts in microbial composition including increased Enterobacteriaceae, consistent with dysbiosis. This microbial community type was associated similarly with IBD and reduced microbial diversity in an independent pediatric cohort. Individuals also clustered bioinformatically into 2 subsets with shared fecal metabolomics signatures. One metabotype was associated with IBD and was characterized by increased bile acids, taurine, and tryptophan. The IBD-associated microbial and metabolomics states were highly correlated, suggesting that they represented an integrated ecosystem. Healthy relatives with the IBD-associated microbial community type had an increased incidence of elevated fecal calprotectin. Healthy first-degree relatives can have dysbiosis associated with an altered intestinal metabolome that may signify a predisease microbial susceptibility state or subclinical inflammation. Longitudinal prospective studies are required to determine whether these individuals have a clinically significant increased risk for developing IBD.

Microbial translocation in HIV infection is associated with dyslipidemia, insulin resistance, and risk of myocardial infarction.

PubMed

Pedersen, Karin K; Pedersen, Maria; Trøseid, Marius; Gaardbo, Julie C; Lund, Tamara T; Thomsen, Carsten; Gerstoft, Jan; Kvale, Dag; Nielsen, Susanne D

2013-12-15

Microbial translocation has been suggested to be a driver of immune activation and inflammation. It is hypothesized that microbial translocation may be related to dyslipidemia, insulin resistance, and the risk of coronary heart disease in HIV-infected individuals. Cross-sectional study of 60 HIV-infected patients on combination antiretroviral therapy with viral suppression >2 years and 31 healthy age-matched controls. Lipopolysaccharide (LPS) was analyzed by limulus amebocyte lysate colorimetric assay. Lipids, including cholesterol, low-density lipoprotein (LDL), and triglycerides, were measured. Glucose metabolism was determined using an oral glucose tolerance test. Body composition was determined using whole-body dual-energy x-ray absorptiometry scans and magnetic resonance imaging. The Framingham risk score was used to assess risk of cardiovascular disease and myocardial infarction. HIV-infected patients had higher level of LPS compared with controls (64 pg/mL vs. 50 pg/mL, P = 0.002). Likewise, HIV-infected patients had higher triglycerides, LDL, and fasting insulin as well as evidence of lower insulin sensitivity compared with controls. Among HIV-infected patients, high LPS was associated with a higher level of triglycerides and LDL and with lower insulin sensitivity. Importantly, among HIV-infected patients, high LPS was associated with a higher Framingham risk score. HIV-infected patients with suppressed viral replication had increased level of microbial translocation as measured by LPS. LPS was associated with cardiometabolic risk factors and increased Framingham risk score. Hence, the gastrointestinal mucosal barrier may be a potential therapeutic target to prevent dyslipidemia and future cardiovascular complications in HIV infection.

A Risk Analysis of the Molybdenum-99 Supply Chain Using Bayesian Networks

NASA Astrophysics Data System (ADS)

Liang, Jeffrey Ryan

The production of Molybdenum-99 (99Mo) is critical to the field of nuclear medicine, where it is utilized in roughly 80% of all nuclear imaging procedures. In October of 2016, the National Research Universal (NRU) reactor in Canada, which historically had the highest 99Mo production capability worldwide, ceased routine production and will be permanently shut down in 2018. This loss of capacity has led to widespread concern over the ability of the 99Mo supply chain and to meet demand. There is significant disagreement among analyses from trade groups, governments, and other researchers, predicting everything from no significant impact to major worldwide shortages. Using Bayesian networks, this research focused on modeling the 99Mo supply chain to quantify how a disrupting event, such as the unscheduled downtime of a reactor, will impact the global supply. This not only includes quantifying the probability of a shortage occurring, but also identifying which nodes in the supply chain introduce the most risk to better inform decision makers on where future facilities or other risk mitigation techniques should be applied.

Hierarchical Bayesian models to assess between- and within-batch variability of pathogen contamination in food.

PubMed

Commeau, Natalie; Cornu, Marie; Albert, Isabelle; Denis, Jean-Baptiste; Parent, Eric

2012-03-01

Assessing within-batch and between-batch variability is of major interest for risk assessors and risk managers in the context of microbiological contamination of food. For example, the ratio between the within-batch variability and the between-batch variability has a large impact on the results of a sampling plan. Here, we designed hierarchical Bayesian models to represent such variability. Compatible priors were built mathematically to obtain sound model comparisons. A numeric criterion is proposed to assess the contamination structure comparing the ability of the models to replicate grouped data at the batch level using a posterior predictive loss approach. Models were applied to two case studies: contamination by Listeria monocytogenes of pork breast used to produce diced bacon and contamination by the same microorganism on cold smoked salmon at the end of the process. In the first case study, a contamination structure clearly exists and is located at the batch level, that is, between batches variability is relatively strong, whereas in the second a structure also exists but is less marked. © 2012 Society for Risk Analysis.

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment.

PubMed

Marvin, Hans J P; Bouzembrak, Yamine; Janssen, Esmée M; van der Zande, Meike; Murphy, Finbarr; Sheehan, Barry; Mullins, Martin; Bouwmeester, Hans

2017-02-01

In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal- and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g., in vitro/in vivo data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO 2 , SiO 2 , Ag, CeO 2 , ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.

How Many Batches Are Needed for Process Validation under the New FDA Guidance?

PubMed

Yang, Harry

2013-01-01

The newly updated FDA Guidance for Industry on Process Validation: General Principles and Practices ushers in a life cycle approach to process validation. While the guidance no longer considers the use of traditional three-batch validation appropriate, it does not prescribe the number of validation batches for a prospective validation protocol, nor does it provide specific methods to determine it. This potentially could leave manufacturers in a quandary. In this paper, I develop a Bayesian method to address the issue. By combining process knowledge gained from Stage 1 Process Design (PD) with expected outcomes of Stage 2 Process Performance Qualification (PPQ), the number of validation batches for PPQ is determined to provide a high level of assurance that the process will consistently produce future batches meeting quality standards. Several examples based on simulated data are presented to illustrate the use of the Bayesian method in helping manufacturers make risk-based decisions for Stage 2 PPQ, and they highlight the advantages of the method over traditional Frequentist approaches. The discussions in the paper lend support for a life cycle and risk-based approach to process validation recommended in the new FDA guidance. The newly updated FDA Guidance for Industry on Process Validation: General Principles and Practices ushers in a life cycle approach to process validation. While the guidance no longer considers the use of traditional three-batch validation appropriate, it does not prescribe the number of validation batches for a prospective validation protocol, nor does it provide specific methods to determine it. This potentially could leave manufacturers in a quandary. In this paper, I develop a Bayesian method to address the issue. By combining process knowledge gained from Stage 1 Process Design (PD) with expected outcomes of Stage 2 Process Performance Qualification (PPQ), the number of validation batches for PPQ is determined to provide a high level of assurance that the process will consistently produce future batches meeting quality standards. Several examples based on simulated data are presented to illustrate the use of the Bayesian method in helping manufacturers make risk-based decisions for Stage 2 PPQ, and THEY highlight the advantages of the method over traditional Frequentist approaches. The discussions in the paper lend support for a life cycle and risk-based approach to process validation recommended in the new FDA guidance.

Bacterial and fungal composition profiling of microbial based cleaning products.

PubMed

Subasinghe, R M; Samarajeewa, A D; Meier, M; Coleman, G; Clouthier, H; Crosthwait, J; Tayabali, A F; Scroggins, R; Shwed, P S; Beaudette, L A

2018-06-01

Microbial based cleaning products (MBCPs) are a new generation of cleaning products that are gaining greater use in household, institutional, and industrial settings. Little is known about the exact microbial composition of these products because they are not identified in detail on product labels and formulations are often proprietary. To gain a better understanding of their microbial and fungal composition towards risk assessment, the cultivable microorganisms and rDNA was surveyed for microbial content in five different MBCPs manufactured and sold in North America. Individual bacterial and fungal colonies were identified by ribosequencing and fatty acid methyl ester (FAME) gas chromatography. Metagenomic DNA (mDNA) corresponding to each of the products was subjected to amplification and short read sequencing of seven of the variable regions of the bacterial 16S ribosomal DNA. Taken together, the cultivable microorganism and rDNA survey analyses showed that three of the products were simple mixtures of Bacillus species. The two other products featured a mixture of cultivable fungi with Bacilli, and by rDNA survey analysis, they featured greater microbial complexity. This study improves our understanding of the microbial composition of several MBCPs towards a more comprehensive risk assessment. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

An assessment of water quality and microbial risk in Rio Grande Basin in the United States-Mexican border region.

PubMed

Ryu, Hodon; Alum, Absar; Alvarez, Maria; Mendoza, Jose; Abbaszadegan, Morteza

2005-06-01

Increased reliance of urban populations on Rio Grande water has necessitated an expanded microbial surveillance of the river to help identify and evaluate sources of human pathogens, which could pose a public health risk. The objectives of this study were to investigate microbial and chemical water quality in Rio Grande water and to perform risk assessment analyses for Cryptosporidium. No oocysts in any of the ten-litre samples were detected. However, the limit of detection in the water samples ranged between 20 and 200 oocysts/100 L. The limits of detection obtained in this study would result in one to two orders of magnitude higher risk of infection for Cryptosporidium than the U.S.EPA annual acceptable risk level of 10(-4). The bacterial data showed the significance of animal farming and raw sewage as sources of fecal pollution. Male specific and somatic coliphages were detected in 52% (11/21) and 62% (24/39) of the samples, respectively. Somatic coliphages were greater by one order of magnitude, and were better correlated with total (r2 = 0.6801; p < or = 0.05) and fecal coliform bacteria (r2 = 0.7366; p < or = 0.05) than male specific coliphages. The dissolved organic carbon (DOC) and specific ultraviolet absorbance (SUVA) values ranged 2.58-5.59mg/L and 1.23-2.29 m(-1) (mg/I)(-1), respectively. Low SUVA values of raw water condition make it difficult to remove DOC during physical and chemical treatment processes. The microbial and chemical data provided from this study can help drinking water utilities to maintain balance between greater microbial inactivation and reduced disinfection by-products (DBPs) formation.

Evaluating nonindigenous species management in a Bayesian networks derived relative risk framework for Padilla Bay, WA, USA.

PubMed

Herring, Carlie E; Stinson, Jonah; Landis, Wayne G

2015-10-01

Many coastal regions are encountering issues with the spread of nonindigenous species (NIS). In this study, we conducted a regional risk assessment using a Bayesian network relative risk model (BN-RRM) to analyze multiple vectors of NIS introductions to Padilla Bay, Washington, a National Estuarine Research Reserve. We had 3 objectives in this study. The 1st objective was to determine whether the BN-RRM could be used to calculate risk from NIS introductions for Padilla Bay. Our 2nd objective was to determine which regions and endpoints were at greatest risk from NIS introductions. Our 3rd objective was to incorporate a management option into the model and predict endpoint risk if it were to be implemented. Eradication can occur at different stages of NIS invasions, such as the elimination of these species before being introduced to the habitat or removal of the species after settlement. We incorporated the ballast water treatment management scenario into the model, observed the risk to the endpoints, and compared this risk with the initial risk estimates. The model results indicated that the southern portion of the bay was at greatest risk because of NIS. Changes in community composition, Dungeness crab, and eelgrass were the endpoints most at risk from NIS introductions. The currents node, which controls the exposure of NIS to the bay from the surrounding marine environment, was the parameter that had the greatest influence on risk. The ballast water management scenario displayed an approximate 1% reduction in risk in this Padilla Bay case study. The models we developed provide an adaptable template for decision makers interested in managing NIS in other coastal regions and large bodies of water. © 2015 SETAC.

Effect of temperature on microbial growth rate-mathematical analysis: the Arrhenius and Eyring-Polanyi connections.

PubMed

Huang, Lihan; Hwang, Andy; Phillips, John

2011-10-01

The objective of this work is to develop a mathematical model for evaluating the effect of temperature on the rate of microbial growth. The new mathematical model is derived by combination and modification of the Arrhenius equation and the Eyring-Polanyi transition theory. The new model, suitable for both suboptimal and the entire growth temperature ranges, was validated using a collection of 23 selected temperature-growth rate curves belonging to 5 groups of microorganisms, including Pseudomonas spp., Listeria monocytogenes, Salmonella spp., Clostridium perfringens, and Escherichia coli, from the published literature. The curve fitting is accomplished by nonlinear regression using the Levenberg-Marquardt algorithm. The resulting estimated growth rate (μ) values are highly correlated to the data collected from the literature (R(2) = 0.985, slope = 1.0, intercept = 0.0). The bias factor (B(f) ) of the new model is very close to 1.0, while the accuracy factor (A(f) ) ranges from 1.0 to 1.22 for most data sets. The new model is compared favorably with the Ratkowsky square root model and the Eyring equation. Even with more parameters, the Akaike information criterion, Bayesian information criterion, and mean square errors of the new model are not statistically different from the square root model and the Eyring equation, suggesting that the model can be used to describe the inherent relationship between temperature and microbial growth rates. The results of this work show that the new growth rate model is suitable for describing the effect of temperature on microbial growth rate. Practical Application:  Temperature is one of the most significant factors affecting the growth of microorganisms in foods. This study attempts to develop and validate a mathematical model to describe the temperature dependence of microbial growth rate. The findings show that the new model is accurate and can be used to describe the effect of temperature on microbial growth rate in foods. Journal of Food Science © 2011 Institute of Food Technologists® No claim to original US government works.

Interaction effects and mechanism of Pb pollution and soil microorganism in the presence of earthworm.

PubMed

Liu, Gao; Ling, Siyuan; Zhan, Xiuping; Lin, Zhifen; Zhang, Wei; Lin, Kuangfei

2017-04-01

Heavy metals usually cause great damage to soil ecosystem. Lead (Pb) was chosen as a research object in the present study. Here repeated exposure of Pb was designed for the soil artificially contaminated. A laboratory study was conducted to determine the changes in the Pb availability and biological activity in the presence of earthworm, and the risk assessment code (RAC) was applied to evaluate the remediated soil. Results demonstrated that Pb gradually transformed to more stable fractions (OMB- and FeMnOX-Pb) under microbial action, indicating the risk level of Pb was declined. On the other hand, Pb also caused the inhibition of soil respiration and microbial biomass, and the higher the concentration of Pb, the stronger the inhibition; While in the presence of earthworm, it could absorb Pb and facilitate microbial activity, reflected the decrease of Pb content and the increase of respiration intensity in soil, as well as microbial biomass. Additionally, a good dose-response relationship between EXCH-Pb content and respiration intensity might provide a basis for ecological risk assessment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparing probabilistic microbial risk assessments for drinking water against daily rather than annualised infection probability targets.

PubMed

Signor, R S; Ashbolt, N J

2009-12-01

Some national drinking water guidelines provide guidance on how to define 'safe' drinking water. Regarding microbial water quality, a common position is that the chance of an individual becoming infected by some reference waterborne pathogen (e.g. Cryptsporidium) present in the drinking water should < 10(-4) in any year. However the instantaneous levels of risk to a water consumer vary over the course of a year, and waterborne disease outbreaks have been associated with shorter-duration periods of heightened risk. Performing probabilistic microbial risk assessments is becoming commonplace to capture the impacts of temporal variability on overall infection risk levels. A case is presented here for adoption of a shorter-duration reference period (i.e. daily) infection probability target over which to assess, report and benchmark such risks. A daily infection probability benchmark may provide added incentive and guidance for exercising control over short-term adverse risk fluctuation events and their causes. Management planning could involve outlining measures so that the daily target is met under a variety of pre-identified event scenarios. Other benefits of a daily target could include providing a platform for managers to design and assess management initiatives, as well as simplifying the technical components of the risk assessment process.

Attribution of the French human Salmonellosis cases to the main food-sources according to the type of surveillance data.

PubMed

David, J M; Sanders, P; Bemrah, N; Granier, S A; Denis, M; Weill, F-X; Guillemot, D; Watier, L

2013-05-15

Salmonella are the most common bacterial cause of foodborne infections in France and ubiquitous pathogens present in many animal productions. Assessing the relative contribution of the different food-animal sources to the burden of human cases is a key step towards the conception, prioritization and assessment of efficient control policy measures. For this purpose, we considered a Bayesian microbial subtyping attribution approach based on a previous published model (Hald et al., 2004). It requires quality integrated data on human cases and on the contamination of their food sources, per serotype and microbial subtype, which were retrieved from the French integrated surveillance system for Salmonella. The quality of the data available for such an approach is an issue for many countries in which the surveillance system has not been designed for this purpose. In France, the sources are monitored simultaneously by an active, regulation-based surveillance system that produces representative prevalence data (as ideally required for the approach) and a passive system relying on voluntary laboratories that produces data not meeting the standards set by Hald et al. (2004) but covering a broader range of sources. These data allowed us to study the impact of data quality on the attribution results, globally and focusing on specific features of the data (number of sources and contamination indicator). The microbial subtyping attribution model was run using an adapted parameterization previously proposed (David et al., 2012). A total of 9076 domestic sporadic cases were included in the analyses as well as 9 sources among which 5 were common to the active and the passive datasets. The greatest impact on the attribution results was observed for the number of sources. Thus, especially in the absence of data on imported products, the attribution estimates presented here should be considered with caution. The results were comparable for both types of surveillance, leading to the conclusion that passive data constitute a potential cost-effective complement to active data collection, especially interesting because the former encompass a greater number of sources. The model appeared robust to the type of surveillance, and provided that some methodological aspects of the model can be enhanced, it could also serve as a risk-based guidance tool for active surveillance systems. Copyright © 2013 Elsevier B.V. All rights reserved.

The composition of the gut microbiota throughout life, with an emphasis on early life

PubMed Central

Rodríguez, Juan Miguel; Murphy, Kiera; Stanton, Catherine; Ross, R. Paul; Kober, Olivia I.; Juge, Nathalie; Avershina, Ekaterina; Rudi, Knut; Narbad, Arjan; Jenmalm, Maria C.; Marchesi, Julian R.; Collado, Maria Carmen

2015-01-01

The intestinal microbiota has become a relevant aspect of human health. Microbial colonization runs in parallel with immune system maturation and plays a role in intestinal physiology and regulation. Increasing evidence on early microbial contact suggest that human intestinal microbiota is seeded before birth. Maternal microbiota forms the first microbial inoculum, and from birth, the microbial diversity increases and converges toward an adult-like microbiota by the end of the first 3–5 years of life. Perinatal factors such as mode of delivery, diet, genetics, and intestinal mucin glycosylation all contribute to influence microbial colonization. Once established, the composition of the gut microbiota is relatively stable throughout adult life, but can be altered as a result of bacterial infections, antibiotic treatment, lifestyle, surgical, and a long-term change in diet. Shifts in this complex microbial system have been reported to increase the risk of disease. Therefore, an adequate establishment of microbiota and its maintenance throughout life would reduce the risk of disease in early and late life. This review discusses recent studies on the early colonization and factors influencing this process which impact on health. PMID:25651996

Valence-Dependent Belief Updating: Computational Validation

PubMed Central

Kuzmanovic, Bojana; Rigoux, Lionel

2017-01-01

People tend to update beliefs about their future outcomes in a valence-dependent way: they are likely to incorporate good news and to neglect bad news. However, belief formation is a complex process which depends not only on motivational factors such as the desire for favorable conclusions, but also on multiple cognitive variables such as prior beliefs, knowledge about personal vulnerabilities and resources, and the size of the probabilities and estimation errors. Thus, we applied computational modeling in order to test for valence-induced biases in updating while formally controlling for relevant cognitive factors. We compared biased and unbiased Bayesian models of belief updating, and specified alternative models based on reinforcement learning. The experiment consisted of 80 trials with 80 different adverse future life events. In each trial, participants estimated the base rate of one of these events and estimated their own risk of experiencing the event before and after being confronted with the actual base rate. Belief updates corresponded to the difference between the two self-risk estimates. Valence-dependent updating was assessed by comparing trials with good news (better-than-expected base rates) with trials with bad news (worse-than-expected base rates). After receiving bad relative to good news, participants' updates were smaller and deviated more strongly from rational Bayesian predictions, indicating a valence-induced bias. Model comparison revealed that the biased (i.e., optimistic) Bayesian model of belief updating better accounted for data than the unbiased (i.e., rational) Bayesian model, confirming that the valence of the new information influenced the amount of updating. Moreover, alternative computational modeling based on reinforcement learning demonstrated higher learning rates for good than for bad news, as well as a moderating role of personal knowledge. Finally, in this specific experimental context, the approach based on reinforcement learning was superior to the Bayesian approach. The computational validation of valence-dependent belief updating represents a novel support for a genuine optimism bias in human belief formation. Moreover, the precise control of relevant cognitive variables justifies the conclusion that the motivation to adopt the most favorable self-referential conclusions biases human judgments. PMID:28706499

Valence-Dependent Belief Updating: Computational Validation.

PubMed

Kuzmanovic, Bojana; Rigoux, Lionel

2017-01-01

People tend to update beliefs about their future outcomes in a valence-dependent way: they are likely to incorporate good news and to neglect bad news. However, belief formation is a complex process which depends not only on motivational factors such as the desire for favorable conclusions, but also on multiple cognitive variables such as prior beliefs, knowledge about personal vulnerabilities and resources, and the size of the probabilities and estimation errors. Thus, we applied computational modeling in order to test for valence-induced biases in updating while formally controlling for relevant cognitive factors. We compared biased and unbiased Bayesian models of belief updating, and specified alternative models based on reinforcement learning. The experiment consisted of 80 trials with 80 different adverse future life events. In each trial, participants estimated the base rate of one of these events and estimated their own risk of experiencing the event before and after being confronted with the actual base rate. Belief updates corresponded to the difference between the two self-risk estimates. Valence-dependent updating was assessed by comparing trials with good news (better-than-expected base rates) with trials with bad news (worse-than-expected base rates). After receiving bad relative to good news, participants' updates were smaller and deviated more strongly from rational Bayesian predictions, indicating a valence-induced bias. Model comparison revealed that the biased (i.e., optimistic) Bayesian model of belief updating better accounted for data than the unbiased (i.e., rational) Bayesian model, confirming that the valence of the new information influenced the amount of updating. Moreover, alternative computational modeling based on reinforcement learning demonstrated higher learning rates for good than for bad news, as well as a moderating role of personal knowledge. Finally, in this specific experimental context, the approach based on reinforcement learning was superior to the Bayesian approach. The computational validation of valence-dependent belief updating represents a novel support for a genuine optimism bias in human belief formation. Moreover, the precise control of relevant cognitive variables justifies the conclusion that the motivation to adopt the most favorable self-referential conclusions biases human judgments.

Origin of marine planktonic cyanobacteria.

PubMed

Sánchez-Baracaldo, Patricia

2015-12-01

Marine planktonic cyanobacteria contributed to the widespread oxygenation of the oceans towards the end of the Pre-Cambrian and their evolutionary origin represents a key transition in the geochemical evolution of the Earth surface. Little is known, however, about the evolutionary events that led to the appearance of marine planktonic cyanobacteria. I present here phylogenomic (135 proteins and two ribosomal RNAs), Bayesian relaxed molecular clock (18 proteins, SSU and LSU) and Bayesian stochastic character mapping analyses from 131 cyanobacteria genomes with the aim to unravel key evolutionary steps involved in the origin of marine planktonic cyanobacteria. While filamentous cell types evolved early on at around 2,600-2,300 Mya and likely dominated microbial mats in benthic environments for most of the Proterozoic (2,500-542 Mya), marine planktonic cyanobacteria evolved towards the end of the Proterozoic and early Phanerozoic. Crown groups of modern terrestrial and/or benthic coastal cyanobacteria appeared during the late Paleoproterozoic to early Mesoproterozoic. Decrease in cell diameter and loss of filamentous forms contributed to the evolution of unicellular planktonic lineages during the middle of the Mesoproterozoic (1,600-1,000 Mya) in freshwater environments. This study shows that marine planktonic cyanobacteria evolved from benthic marine and some diverged from freshwater ancestors during the Neoproterozoic (1,000-542 Mya).

Origin of marine planktonic cyanobacteria

PubMed Central

Sánchez-Baracaldo, Patricia

2015-01-01

Marine planktonic cyanobacteria contributed to the widespread oxygenation of the oceans towards the end of the Pre-Cambrian and their evolutionary origin represents a key transition in the geochemical evolution of the Earth surface. Little is known, however, about the evolutionary events that led to the appearance of marine planktonic cyanobacteria. I present here phylogenomic (135 proteins and two ribosomal RNAs), Bayesian relaxed molecular clock (18 proteins, SSU and LSU) and Bayesian stochastic character mapping analyses from 131 cyanobacteria genomes with the aim to unravel key evolutionary steps involved in the origin of marine planktonic cyanobacteria. While filamentous cell types evolved early on at around 2,600–2,300 Mya and likely dominated microbial mats in benthic environments for most of the Proterozoic (2,500–542 Mya), marine planktonic cyanobacteria evolved towards the end of the Proterozoic and early Phanerozoic. Crown groups of modern terrestrial and/or benthic coastal cyanobacteria appeared during the late Paleoproterozoic to early Mesoproterozoic. Decrease in cell diameter and loss of filamentous forms contributed to the evolution of unicellular planktonic lineages during the middle of the Mesoproterozoic (1,600–1,000 Mya) in freshwater environments. This study shows that marine planktonic cyanobacteria evolved from benthic marine and some diverged from freshwater ancestors during the Neoproterozoic (1,000–542 Mya). PMID:26621203

Diagnosis and Prognostic of Wastewater Treatment System Based on Bayesian Network

NASA Astrophysics Data System (ADS)

Li, Dan; Yang, Haizhen; Liang, XiaoFeng

2010-11-01

Wastewater treatment is a complicated and dynamic process. The treatment effect can be influenced by many variables in microbial, chemical and physical aspects. These variables are always uncertain. Due to the complex biological reaction mechanisms, the highly time-varying and multivariable aspects, the diagnosis and prognostic of wastewater treatment system are still difficult in practice. Bayesian network (BN) is one of the best methods for dealing with uncertainty in the artificial intelligence field. Because of the powerful inference ability and convenient decision mechanism, BN can be employed into the model description and influencing factor analysis of wastewater treatment system with great flexibility and applicability.In this paper, taking modified sequencing batch reactor (MSBR) as an analysis object, BN model was constructed according to the influent water quality, operational condition and effluent effect data of MSBR, and then a novel approach based on BN is proposed to analyze the influencing factors of the wastewater treatment system. The approach presented gives an effective tool for diagnosing and predicting analysis of the wastewater treatment system. On the basis of the influent water quality and operational condition, effluent effect can be predicted. Moreover, according to the effluent effect, the influent water quality and operational condition also can be deduced.

FDA-iRISK--a comparative risk assessment system for evaluating and ranking food-hazard pairs: case studies on microbial hazards.

PubMed

Chen, Yuhuan; Dennis, Sherri B; Hartnett, Emma; Paoli, Greg; Pouillot, Régis; Ruthman, Todd; Wilson, Margaret

2013-03-01

Stakeholders in the system of food safety, in particular federal agencies, need evidence-based, transparent, and rigorous approaches to estimate and compare the risk of foodborne illness from microbial and chemical hazards and the public health impact of interventions. FDA-iRISK (referred to here as iRISK), a Web-based quantitative risk assessment system, was developed to meet this need. The modeling tool enables users to assess, compare, and rank the risks posed by multiple food-hazard pairs at all stages of the food supply system, from primary production, through manufacturing and processing, to retail distribution and, ultimately, to the consumer. Using standard data entry templates, built-in mathematical functions, and Monte Carlo simulation techniques, iRISK integrates data and assumptions from seven components: the food, the hazard, the population of consumers, process models describing the introduction and fate of the hazard up to the point of consumption, consumption patterns, dose-response curves, and health effects. Beyond risk ranking, iRISK enables users to estimate and compare the impact of interventions and control measures on public health risk. iRISK provides estimates of the impact of proposed interventions in various ways, including changes in the mean risk of illness and burden of disease metrics, such as losses in disability-adjusted life years. Case studies for Listeria monocytogenes and Salmonella were developed to demonstrate the application of iRISK for the estimation of risks and the impact of interventions for microbial hazards. iRISK was made available to the public at http://irisk.foodrisk.org in October 2012.

Assessing State Nuclear Weapons Proliferation: Using Bayesian Network Analysis of Social Factors

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

Coles, Garill A.; Brothers, Alan J.; Olson, Jarrod

A Bayesian network (BN) model of social factors can support proliferation assessments by estimating the likelihood that a state will pursue a nuclear weapon. Social factors including political, economic, nuclear capability, security, and national identity and psychology factors may play as important a role in whether a State pursues nuclear weapons as more physical factors. This paper will show how using Bayesian reasoning on a generic case of a would-be proliferator State can be used to combine evidence that supports proliferation assessment. Theories and analysis by political scientists can be leveraged in a quantitative and transparent way to indicate proliferationmore » risk. BN models facilitate diagnosis and inference in a probabilistic environment by using a network of nodes and acyclic directed arcs between the nodes whose connections, or absence of, indicate probabilistic relevance, or independence. We propose a BN model that would use information from both traditional safeguards and the strengthened safeguards associated with the Additional Protocol to indicate countries with a high risk of proliferating nuclear weapons. This model could be used in a variety of applications such a prioritization tool and as a component of state safeguards evaluations. This paper will discuss the benefits of BN reasoning, the development of Pacific Northwest National Laboratory’s (PNNL) BN state proliferation model and how it could be employed as an analytical tool.« less

Estimated value of insurance premium due to Citarum River flood by using Bayesian method

NASA Astrophysics Data System (ADS)

Sukono; Aisah, I.; Tampubolon, Y. R. H.; Napitupulu, H.; Supian, S.; Subiyanto; Sidi, P.

2018-03-01

Citarum river flood in South Bandung, West Java Indonesia, often happens every year. It causes property damage, producing economic loss. The risk of loss can be mitigated by following the flood insurance program. In this paper, we discussed about the estimated value of insurance premiums due to Citarum river flood by Bayesian method. It is assumed that the risk data for flood losses follows the Pareto distribution with the right fat-tail. The estimation of distribution model parameters is done by using Bayesian method. First, parameter estimation is done with assumption that prior comes from Gamma distribution family, while observation data follow Pareto distribution. Second, flood loss data is simulated based on the probability of damage in each flood affected area. The result of the analysis shows that the estimated premium value of insurance based on pure premium principle is as follows: for the loss value of IDR 629.65 million of premium IDR 338.63 million; for a loss of IDR 584.30 million of its premium IDR 314.24 million; and the loss value of IDR 574.53 million of its premium IDR 308.95 million. The premium value estimator can be used as neither a reference in the decision of reasonable premium determination, so as not to incriminate the insured, nor it result in loss of the insurer.

Which is best for osteoporotic vertebral compression fractures: balloon kyphoplasty, percutaneous vertebroplasty or non-surgical treatment? A study protocol for a Bayesian network meta-analysis

PubMed Central

Kan, Shun-Li; Yuan, Zhi-Fang; Chen, Ling-Xiao; Sun, Jing-Cheng; Ning, Guang-Zhi; Feng, Shi-Qing

2017-01-01

Introduction Osteoporotic vertebral compression fractures (OVCFs) commonly cause both acute and chronic back pain, substantial spinal deformity, functional disability and decreased quality of life and increase the risk of future vertebral fractures and mortality. Percutaneous vertebroplasty (PVP), balloon kyphoplasty (BK) and non-surgical treatment (NST) are mostly used for the treatment of OVCFs. However, which treatment is preferred is unknown. The purpose of this study is to comprehensively review the literature and ascertain the relative efficacy and safety of BK, PVP and NST for patients with OVCFs using a Bayesian network meta-analysis. Methods and analysis We will comprehensively search PubMed, EMBASE and the Cochrane Central Register of Controlled Trials, to include randomided controlled trials that compare BK, PVP or NST for treating OVCFs. The risk of bias for individual studies will be assessed according to the Cochrane Handbook. Bayesian network meta-analysis will be performed to compare the efficacy and safety of BK, PVP and NST. The quality of evidence will be evaluated by GRADE. Ethics and dissemination Ethical approval and patient consent are not required since this study is a meta-analysis based on published studies. The results of this network meta-analysis will be submitted to a peer-reviewed journal for publication. PROSPERO registration number CRD42016039452; Pre-results. PMID:28093431

Scheduling structural health monitoring activities for optimizing life-cycle costs and reliability of wind turbines

NASA Astrophysics Data System (ADS)

Hanish Nithin, Anu; Omenzetter, Piotr

2017-04-01

Optimization of the life-cycle costs and reliability of offshore wind turbines (OWTs) is an area of immense interest due to the widespread increase in wind power generation across the world. Most of the existing studies have used structural reliability and the Bayesian pre-posterior analysis for optimization. This paper proposes an extension to the previous approaches in a framework for probabilistic optimization of the total life-cycle costs and reliability of OWTs by combining the elements of structural reliability/risk analysis (SRA), the Bayesian pre-posterior analysis with optimization through a genetic algorithm (GA). The SRA techniques are adopted to compute the probabilities of damage occurrence and failure associated with the deterioration model. The probabilities are used in the decision tree and are updated using the Bayesian analysis. The output of this framework would determine the optimal structural health monitoring and maintenance schedules to be implemented during the life span of OWTs while maintaining a trade-off between the life-cycle costs and risk of the structural failure. Numerical illustrations with a generic deterioration model for one monitoring exercise in the life cycle of a system are demonstrated. Two case scenarios, namely to build initially an expensive and robust or a cheaper but more quickly deteriorating structures and to adopt expensive monitoring system, are presented to aid in the decision-making process.

Bayesian assessment of overtriage and undertriage at a level I trauma centre.

PubMed

DiDomenico, Paul B; Pietzsch, Jan B; Paté-Cornell, M Elisabeth

2008-07-13

We analysed the trauma triage system at a specific level I trauma centre to assess rates of over- and undertriage and to support recommendations for system improvements. The triage process is designed to estimate the severity of patient injury and allocate resources accordingly, with potential errors of overestimation (overtriage) consuming excess resources and underestimation (undertriage) potentially leading to medical errors.We first modelled the overall trauma system using risk analysis methods to understand interdependencies among the actions of the participants. We interviewed six experienced trauma surgeons to obtain their expert opinion of the over- and undertriage rates occurring in the trauma centre. We then assessed actual over- and undertriage rates in a random sample of 86 trauma cases collected over a six-week period at the same centre. We employed Bayesian analysis to quantitatively combine the data with the prior probabilities derived from expert opinion in order to obtain posterior distributions. The results were estimates of overtriage and undertriage in 16.1 and 4.9% of patients, respectively. This Bayesian approach, which provides a quantitative assessment of the error rates using both case data and expert opinion, provides a rational means of obtaining a best estimate of the system's performance. The overall approach that we describe in this paper can be employed more widely to analyse complex health care delivery systems, with the objective of reduced errors, patient risk and excess costs.

Selecting Strategies to Reduce High-Risk Unsafe Work Behaviors Using the Safety Behavior Sampling Technique and Bayesian Network Analysis.

PubMed

Ghasemi, Fakhradin; Kalatpour, Omid; Moghimbeigi, Abbas; Mohammadfam, Iraj

2017-03-04

High-risk unsafe behaviors (HRUBs) have been known as the main cause of occupational accidents. Considering the financial and societal costs of accidents and the limitations of available resources, there is an urgent need for managing unsafe behaviors at workplaces. The aim of the present study was to find strategies for decreasing the rate of HRUBs using an integrated approach of safety behavior sampling technique and Bayesian networks analysis. A cross-sectional study. The Bayesian network was constructed using a focus group approach. The required data was collected using the safety behavior sampling, and the parameters of the network were estimated using Expectation-Maximization algorithm. Using sensitivity analysis and belief updating, it was determined that which factors had the highest influences on unsafe behavior. Based on BN analyses, safety training was the most important factor influencing employees' behavior at the workplace. High quality safety training courses can reduce the rate of HRUBs about 10%. Moreover, the rate of HRUBs increased by decreasing the age of employees. The rate of HRUBs was higher in the afternoon and last days of a week. Among the investigated variables, training was the most important factor affecting safety behavior of employees. By holding high quality safety training courses, companies would be able to reduce the rate of HRUBs significantly.

A Bayesian-based two-stage inexact optimization method for supporting stream water quality management in the Three Gorges Reservoir region.

PubMed

Hu, X H; Li, Y P; Huang, G H; Zhuang, X W; Ding, X W

2016-05-01

In this study, a Bayesian-based two-stage inexact optimization (BTIO) method is developed for supporting water quality management through coupling Bayesian analysis with interval two-stage stochastic programming (ITSP). The BTIO method is capable of addressing uncertainties caused by insufficient inputs in water quality model as well as uncertainties expressed as probabilistic distributions and interval numbers. The BTIO method is applied to a real case of water quality management for the Xiangxi River basin in the Three Gorges Reservoir region to seek optimal water quality management schemes under various uncertainties. Interval solutions for production patterns under a range of probabilistic water quality constraints have been generated. Results obtained demonstrate compromises between the system benefit and the system failure risk due to inherent uncertainties that exist in various system components. Moreover, information about pollutant emission is accomplished, which would help managers to adjust production patterns of regional industry and local policies considering interactions of water quality requirement, economic benefit, and industry structure.

Bayesian Knowledge Fusion in Prognostics and Health Management—A Case Study

NASA Astrophysics Data System (ADS)

Rabiei, Masoud; Modarres, Mohammad; Mohammad-Djafari, Ali

2011-03-01

In the past few years, a research effort has been in progress at University of Maryland to develop a Bayesian framework based on Physics of Failure (PoF) for risk assessment and fleet management of aging airframes. Despite significant achievements in modelling of crack growth behavior using fracture mechanics, it is still of great interest to find practical techniques for monitoring the crack growth instances using nondestructive inspection and to integrate such inspection results with the fracture mechanics models to improve the predictions. The ultimate goal of this effort is to develop an integrated probabilistic framework for utilizing all of the available information to come up with enhanced (less uncertain) predictions for structural health of the aircraft in future missions. Such information includes material level fatigue models and test data, health monitoring measurements and inspection field data. In this paper, a case study of using Bayesian fusion technique for integrating information from multiple sources in a structural health management problem is presented.

FECAL POLLUTION, PUBLIC HEALTH AND MICROBIAL SOURCE TRACKING

EPA Science Inventory

Microbial source tracking (MST) seeks to provide information about sources of fecal water contamination. Without knowledge of sources, it is difficult to accurately model risk assessments, choose effective remediation strategies, or bring chronically polluted waters into complian...

Developing a Comprehensive Risk Assessment Framework for Geological Storage CO 2

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

Duncan, Ian

2014-08-31

The operational risks for CCS projects include: risks of capturing, compressing, transporting and injecting CO₂; risks of well blowouts; risk that CO 2 will leak into shallow aquifers and contaminate potable water; and risk that sequestered CO 2 will leak into the atmosphere. This report examines these risks by using information on the risks associated with analogue activities such as CO 2 based enhanced oil recovery (CO 2-EOR), natural gas storage and acid gas disposal. We have developed a new analysis of pipeline risk based on Bayesian statistical analysis. Bayesian theory probabilities may describe states of partial knowledge, even perhapsmore » those related to non-repeatable events. The Bayesian approach enables both utilizing existing data and at the same time having the capability to adsorb new information thus to lower uncertainty in our understanding of complex systems. Incident rates for both natural gas and CO 2 pipelines have been widely used in papers and reports on risk of CO 2 pipelines as proxies for the individual risk created by such pipelines. Published risk studies of CO 2 pipelines suggest that the individual risk associated with CO2 pipelines is between 10 -3 and 10 -4, which reflects risk levels approaching those of mountain climbing, which many would find unacceptably high. This report concludes, based on a careful analysis of natural gas pipeline failures, suggests that the individual risk of CO 2 pipelines is likely in the range of 10-6 to 10-7, a risk range considered in the acceptable to negligible range in most countries. If, as is commonly thought, pipelines represent the highest risk component of CCS outside of the capture plant, then this conclusion suggests that most (if not all) previous quantitative- risk assessments of components of CCS may be orders of magnitude to high. The potential lethality of unexpected CO 2 releases from pipelines or wells are arguably the highest risk aspects of CO 2 enhanced oil recovery (CO2-EOR), carbon capture, and storage (CCS). Assertions in the CCS literature, that CO 2 levels of 10% for ten minutes, or 20 to 30% for a few minutes are lethal to humans, are not supported by the available evidence. The results of published experiments with animals exposed to CO 2, from mice to monkeys, at both normal and depleted oxygen levels, suggest that lethal levels of CO 2 toxicity are in the range 50 to 60%. These experiments demonstrate that CO 2 does not kill by asphyxia, but rather is toxic at high concentrations. It is concluded that quantitative risk assessments of CCS have overestimated the risk of fatalities by using values of lethality a factor two to six lower than the values estimated in this paper. In many dispersion models of CO 2 releases from pipelines, no fatalities would be predicted if appropriate levels of lethality for CO 2 had been used in the analysis.« less

Applying biotic ligand models and Bayesian techniques: ecological risk assessment of copper and nickel in Tokyo rivers.

PubMed

Hayashi, Takehiko I

2013-01-01

Biotic ligand models (BLMs) have been broadly accepted and used in ecological risk assessment of heavy metals for toxicity normalization with respect to water chemistry. However, the importance of assessing bioavailability by using BLMs has not been widely recognized among Japanese stakeholders. Failing to consider bioavailability may result in less effective risk management than would be possible if currently available state-of-the-art methods were used to relate bioavailable concentrations to toxic effects. In this study, an ecological risk assessment was conducted using BLMs for 6 rivers in Tokyo to stimulate discussion about bioavailability of heavy metals and the use of BLMs in ecological risk management in Japan. In the risk analysis, a Bayesian approach was used to take advantage of information from previous analyses and to calculate uncertainties in the estimation of risk. Risks were judged to be a concern if the predicted environmental concentration exceeded the 5th percentile concentration (HC5) of the species sensitivity distribution. Based on this criterion, risks to stream biota from exposure to Cu were judged not to be very severe, but it would be desirable to conduct further monitoring and field surveys to determine whether temporary exposure to concentrations exceeding the HC5 causes any irreversible effects on the river ecosystem. The risk of exposure to Ni was a concern at only 1 of the 6 sites. BLM corrections affected these conclusions in the case of Cu but were moot in the case of Ni. The use of BLMs in risk assessment calculations for Japanese rivers requires water quality information that is, unfortunately, not always available. Copyright © 2012 SETAC.

Modeling the impact of the indigenous microbial population on the maximum population density of Salmonella on alfalfa.

PubMed

Rijgersberg, Hajo; Franz, Eelco; Nierop Groot, Masja; Tromp, Seth-Oscar

2013-07-01

Within a microbial risk assessment framework, modeling the maximum population density (MPD) of a pathogenic microorganism is important but often not considered. This paper describes a model predicting the MPD of Salmonella on alfalfa as a function of the initial contamination level, the total count of the indigenous microbial population, the maximum pathogen growth rate and the maximum population density of the indigenous microbial population. The model is parameterized by experimental data describing growth of Salmonella on sprouting alfalfa seeds at inoculum size, native microbial load and Pseudomonas fluorescens 2-79. The obtained model fits well to the experimental data, with standard errors less than ten percent of the fitted average values. The results show that the MPD of Salmonella is not only dictated by performance characteristics of Salmonella but depends on the characteristics of the indigenous microbial population like total number of cells and its growth rate. The model can improve the predictions of microbiological growth in quantitative microbial risk assessments. Using this model, the effects of preventive measures to reduce pathogenic load and a concurrent effect on the background population can be better evaluated. If competing microorganisms are more sensitive to a particular decontamination method, a pathogenic microorganism may grow faster and reach a higher level. More knowledge regarding the effect of the indigenous microbial population (size, diversity, composition) of food products on pathogen dynamics is needed in order to make adequate predictions of pathogen dynamics on various food products.

Risk factors and causative organisms in microbial keratitis in daily disposable contact lens wear.

PubMed

Stapleton, Fiona; Naduvilath, Thomas; Keay, Lisa; Radford, Cherry; Dart, John; Edwards, Katie; Carnt, Nicole; Minassian, Darwin; Holden, Brien

2017-01-01

This study investigated independent risk factors and causative organisms in microbial keratitis in daily disposable contact lens (CL)-wearers. A multisite prospective case-control study was undertaken. Cases were daily disposable CL-wearers attending Moorfields Eye Hospital with microbial keratitis and those reported through a one-year surveillance study in Australia and in New Zealand. A population-based telephone survey identified daily disposable CL-wearing controls. Subjects completed a questionnaire describing CL-wear history, hygiene and demographics. The sample used for risk factor analysis was weighted in proportion to the CL-wearing population at each location. Corneal scrape results were accessed. Independent risk factors were determined using multiple binary logistic regression. Causative organisms in different CL-wear modalities were compared using a chi-squared test. 963 daily disposable CL-wearers were identified, from which 67 cases and 374 controls were sampled. Independent risk factors were; wearing CLs every day compared with less frequent use (OR 10.4x; 95% CI 2.9-56.4), any overnight wear (OR 1.8x; 95% CI 1.6-2.1), less frequent hand washing (OR 1.8x; 95% CI 1.6-2.0), and smoking (OR 1.3x; 95% CI 1.1-1.6). Certain daily disposable CLs (OR 0.2x; 95% CI 0.1-0.2) had protective effects. Environmental organisms were less frequently recovered with daily disposable CLs (20%), compared with other modalities (36%; p<0.02). Overnight wear, increased exposure in daily wear, smoking and poor hand hygiene are significant risk factors for microbial keratitis with daily disposable CLs. Risk varied with daily disposable CL type. The profile of causative organisms is consistent with less severe disease.

Evaluation of virus reduction efficiency in wastewater treatment unit processes as a credit value in the multiple-barrier system for wastewater reclamation and reuse.

PubMed

Ito, Toshihiro; Kato, Tsuyoshi; Hasegawa, Makoto; Katayama, Hiroyuki; Ishii, Satoshi; Okabe, Satoshi; Sano, Daisuke

2016-12-01

The virus reduction efficiency of each unit process is commonly determined based on the ratio of virus concentration in influent to that in effluent of a unit, but the virus concentration in wastewater has often fallen below the analytical quantification limit, which does not allow us to calculate the concentration ratio at each sampling event. In this study, left-censored datasets of norovirus (genogroup I and II), and adenovirus were used to calculate the virus reduction efficiency in unit processes of secondary biological treatment and chlorine disinfection. Virus concentration in influent, effluent from the secondary treatment, and chlorine-disinfected effluent of four municipal wastewater treatment plants were analyzed by a quantitative polymerase chain reaction (PCR) approach, and the probabilistic distributions of log reduction (LR) were estimated by a Bayesian estimation algorithm. The mean values of LR in the secondary treatment units ranged from 0.9 and 2.2, whereas those in the free chlorine disinfection units were from -0.1 and 0.5. The LR value in the secondary treatment was virus type and unit process dependent, which raised the importance for accumulating the data of virus LR values applicable to the multiple-barrier system, which is a global concept of microbial risk management in wastewater reclamation and reuse.

Bayesian Design of Superiority Clinical Trials for Recurrent Events Data with Applications to Bleeding and Transfusion Events in Myelodyplastic Syndrome

PubMed Central

Chen, Ming-Hui; Zeng, Donglin; Hu, Kuolung; Jia, Catherine

2014-01-01

Summary In many biomedical studies, patients may experience the same type of recurrent event repeatedly over time, such as bleeding, multiple infections and disease. In this article, we propose a Bayesian design to a pivotal clinical trial in which lower risk myelodysplastic syndromes (MDS) patients are treated with MDS disease modifying therapies. One of the key study objectives is to demonstrate the investigational product (treatment) effect on reduction of platelet transfusion and bleeding events while receiving MDS therapies. In this context, we propose a new Bayesian approach for the design of superiority clinical trials using recurrent events frailty regression models. Historical recurrent events data from an already completed phase 2 trial are incorporated into the Bayesian design via the partial borrowing power prior of Ibrahim et al. (2012, Biometrics 68, 578–586). An efficient Gibbs sampling algorithm, a predictive data generation algorithm, and a simulation-based algorithm are developed for sampling from the fitting posterior distribution, generating the predictive recurrent events data, and computing various design quantities such as the type I error rate and power, respectively. An extensive simulation study is conducted to compare the proposed method to the existing frequentist methods and to investigate various operating characteristics of the proposed design. PMID:25041037

Stoffenmanager exposure model: company-specific exposure assessments using a Bayesian methodology.

PubMed

van de Ven, Peter; Fransman, Wouter; Schinkel, Jody; Rubingh, Carina; Warren, Nicholas; Tielemans, Erik

2010-04-01

The web-based tool "Stoffenmanager" was initially developed to assist small- and medium-sized enterprises in the Netherlands to make qualitative risk assessments and to provide advice on control at the workplace. The tool uses a mechanistic model to arrive at a "Stoffenmanager score" for exposure. In a recent study it was shown that variability in exposure measurements given a certain Stoffenmanager score is still substantial. This article discusses an extension to the tool that uses a Bayesian methodology for quantitative workplace/scenario-specific exposure assessment. This methodology allows for real exposure data observed in the company of interest to be combined with the prior estimate (based on the Stoffenmanager model). The output of the tool is a company-specific assessment of exposure levels for a scenario for which data is available. The Bayesian approach provides a transparent way of synthesizing different types of information and is especially preferred in situations where available data is sparse, as is often the case in small- and medium sized-enterprises. Real-world examples as well as simulation studies were used to assess how different parameters such as sample size, difference between prior and data, uncertainty in prior, and variance in the data affect the eventual posterior distribution of a Bayesian exposure assessment.

A Bayes linear Bayes method for estimation of correlated event rates.

PubMed

Quigley, John; Wilson, Kevin J; Walls, Lesley; Bedford, Tim

2013-12-01

Typically, full Bayesian estimation of correlated event rates can be computationally challenging since estimators are intractable. When estimation of event rates represents one activity within a larger modeling process, there is an incentive to develop more efficient inference than provided by a full Bayesian model. We develop a new subjective inference method for correlated event rates based on a Bayes linear Bayes model under the assumption that events are generated from a homogeneous Poisson process. To reduce the elicitation burden we introduce homogenization factors to the model and, as an alternative to a subjective prior, an empirical method using the method of moments is developed. Inference under the new method is compared against estimates obtained under a full Bayesian model, which takes a multivariate gamma prior, where the predictive and posterior distributions are derived in terms of well-known functions. The mathematical properties of both models are presented. A simulation study shows that the Bayes linear Bayes inference method and the full Bayesian model provide equally reliable estimates. An illustrative example, motivated by a problem of estimating correlated event rates across different users in a simple supply chain, shows how ignoring the correlation leads to biased estimation of event rates. © 2013 Society for Risk Analysis.

Gut microbial metabolite TMAO enhances platelet hyperreactivity and thrombosis risk

PubMed Central

Zhu, Weifei; Gregory, Jill C.; Org, Elin; Buffa, Jennifer A.; Gupta, Nilaksh; Wang, Zeneng; Li, Lin; Fu, Xiaoming; Wu, Yuping; Mehrabian, Margarete; Sartor, R. Balfour; McIntyre, Thomas M.; Silverstein, Roy L.; Tang, W.H. Wilson; DiDonato, Joseph A.; Brown, J. Mark; Lusis, Aldons J.; Hazen, Stanley L.

2016-01-01

SUMMARY Normal platelet function is critical to blood hemostasis and maintenance of a closed circulatory system. Heightened platelet reactivity, however, is associated with cardiometabolic diseases and enhanced potential for thrombotic events. We now show gut microbes, through generation of trimethylamine N-oxide (TMAO), directly contribute to platelet hyperreactivity and enhanced thrombosis potential. Plasma TMAO levels in subjects (N>4000) independently predicted incident (3 yr) thrombosis (heart attack, stroke) risk. Direct exposure of platelets to TMAO enhanced submaximal stimulus-dependent platelet activation from multiple agonists through augmented Ca2+ release from intracellular stores. Animal model studies employing dietary choline or TMAO, germ-free mice, and microbial transplantation, collectively confirm a role for gut microbiota and TMAO in modulating platelet hyperresponsiveness and thrombosis potential, and identify microbial taxa associated with plasma TMAO and thrombosis potential. Collectively, the present results reveal a previously unrecognized mechanistic link between specific dietary nutrients, gut microbes, platelet function, and thrombosis risk. PMID:26972052

Microbial biofilm formation and its consequences for the CELSS program

NASA Technical Reports Server (NTRS)

Mitchell, R.

1994-01-01

A major goal of the Controlled Ecology Life Support System (CELSS) program is to provide reliable and efficient life support systems for long-duration space flights. A principal focus of the program is on the growth of higher plants in growth chambers. These crops should be grown without the risk of damage from microbial contamination. While it is unlikely that plant pathogens will pose a risk, there are serious hazards associated with microorganisms carried in the nutrient delivery systems and in the atmosphere of the growth chamber. Our experience in surface microbiology showed that colonization of surfaces with microorganisms is extremely rapid even when the inoculum is small. After initial colonization extensive biofilms accumulate on moist surfaces. These microbial films metabolize actively and slough off continuously to the air and water. During plant growth in the CELSS program, microbial biofilms have the potential to foul sensors and to plug nutrient delivery systems. In addition both metabolic products of microbial growth and degradation products of materials being considered for use as nutrient reservoirs and for delivery are likely sources of chemicals known to adversly affect plant growth.

Prediction of near-term breast cancer risk using a Bayesian belief network

NASA Astrophysics Data System (ADS)

Zheng, Bin; Ramalingam, Pandiyarajan; Hariharan, Harishwaran; Leader, Joseph K.; Gur, David

2013-03-01

Accurately predicting near-term breast cancer risk is an important prerequisite for establishing an optimal personalized breast cancer screening paradigm. In previous studies, we investigated and tested the feasibility of developing a unique near-term breast cancer risk prediction model based on a new risk factor associated with bilateral mammographic density asymmetry between the left and right breasts of a woman using a single feature. In this study we developed a multi-feature based Bayesian belief network (BBN) that combines bilateral mammographic density asymmetry with three other popular risk factors, namely (1) age, (2) family history, and (3) average breast density, to further increase the discriminatory power of our cancer risk model. A dataset involving "prior" negative mammography examinations of 348 women was used in the study. Among these women, 174 had breast cancer detected and verified in the next sequential screening examinations, and 174 remained negative (cancer-free). A BBN was applied to predict the risk of each woman having cancer detected six to 18 months later following the negative screening mammography. The prediction results were compared with those using single features. The prediction accuracy was significantly increased when using the BBN. The area under the ROC curve increased from an AUC=0.70 to 0.84 (p<0.01), while the positive predictive value (PPV) and negative predictive value (NPV) also increased from a PPV=0.61 to 0.78 and an NPV=0.65 to 0.75, respectively. This study demonstrates that a multi-feature based BBN can more accurately predict the near-term breast cancer risk than with a single feature.

Microbial Risk Assessment of Air Conditioning Condensate Reuse

EPA Science Inventory

Air conditioning condensate can provide a substantial water source for building-scale collection and non-potable use. Although produced water is anticipated to be of generally high quality, the potential for microbial contamination by biofilm-associated opportunistic pathogens t...

OCCURRENCE AND EXPOSURE ASSESSMENT FOR THE ...

EPA Pesticide Factsheets

Describes the occurrence of Cryptosporidium and other pathogens in the raw and finished water of public water systems (PWS) based on modeling of source water survey data. Analysis of microbial occurrence data to support LT2ESWTR microbial risk assessment

Iowa radon leukaemia study: a hierarchical population risk model for spatially correlated exposure measured with error.

PubMed

Smith, Brian J; Zhang, Lixun; Field, R William

2007-11-10

This paper presents a Bayesian model that allows for the joint prediction of county-average radon levels and estimation of the associated leukaemia risk. The methods are motivated by radon data from an epidemiologic study of residential radon in Iowa that include 2726 outdoor and indoor measurements. Prediction of county-average radon is based on a geostatistical model for the radon data which assumes an underlying continuous spatial process. In the radon model, we account for uncertainties due to incomplete spatial coverage, spatial variability, characteristic differences between homes, and detector measurement error. The predicted radon averages are, in turn, included as a covariate in Poisson models for incident cases of acute lymphocytic (ALL), acute myelogenous (AML), chronic lymphocytic (CLL), and chronic myelogenous (CML) leukaemias reported to the Iowa cancer registry from 1973 to 2002. Since radon and leukaemia risk are modelled simultaneously in our approach, the resulting risk estimates accurately reflect uncertainties in the predicted radon exposure covariate. Posterior mean (95 per cent Bayesian credible interval) estimates of the relative risk associated with a 1 pCi/L increase in radon for ALL, AML, CLL, and CML are 0.91 (0.78-1.03), 1.01 (0.92-1.12), 1.06 (0.96-1.16), and 1.12 (0.98-1.27), respectively. Copyright 2007 John Wiley & Sons, Ltd.

Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction.

PubMed

Zhao, Di; Weng, Chunhua

2011-10-01

In this paper, we propose a novel method that combines PubMed knowledge and Electronic Health Records to develop a weighted Bayesian Network Inference (BNI) model for pancreatic cancer prediction. We selected 20 common risk factors associated with pancreatic cancer and used PubMed knowledge to weigh the risk factors. A keyword-based algorithm was developed to extract and classify PubMed abstracts into three categories that represented positive, negative, or neutral associations between each risk factor and pancreatic cancer. Then we designed a weighted BNI model by adding the normalized weights into a conventional BNI model. We used this model to extract the EHR values for patients with or without pancreatic cancer, which then enabled us to calculate the prior probabilities for the 20 risk factors in the BNI. The software iDiagnosis was designed to use this weighted BNI model for predicting pancreatic cancer. In an evaluation using a case-control dataset, the weighted BNI model significantly outperformed the conventional BNI and two other classifiers (k-Nearest Neighbor and Support Vector Machine). We conclude that the weighted BNI using PubMed knowledge and EHR data shows remarkable accuracy improvement over existing representative methods for pancreatic cancer prediction. Copyright © 2011 Elsevier Inc. All rights reserved.

Combining PubMed Knowledge and EHR Data to Develop a Weighted Bayesian Network for Pancreatic Cancer Prediction

PubMed Central

Zhao, Di; Weng, Chunhua

2011-01-01

In this paper, we propose a novel method that combines PubMed knowledge and Electronic Health Records to develop a weighted Bayesian Network Inference (BNI) model for pancreatic cancer prediction. We selected 20 common risk factors associated with pancreatic cancer and used PubMed knowledge to weigh the risk factors. A keyword-based algorithm was developed to extract and classify PubMed abstracts into three categories that represented positive, negative, or neutral associations between each risk factor and pancreatic cancer. Then we designed a weighted BNI model by adding the normalized weights into a conventional BNI model. We used this model to extract the EHR values for patients with or without pancreatic cancer, which then enabled us to calculate the prior probabilities for the 20 risk factors in the BNI. The software iDiagnosis was designed to use this weighted BNI model for predicting pancreatic cancer. In an evaluation using a case-control dataset, the weighted BNI model significantly outperformed the conventional BNI and two other classifiers (k-Nearest Neighbor and Support Vector Machine). We conclude that the weighted BNI using PubMed knowledge and EHR data shows remarkable accuracy improvement over existing representative methods for pancreatic cancer prediction. PMID:21642013

Timing and effect of a safe routes to school program on child pedestrian injury risk during school travel hours: Bayesian changepoint and difference-in-differences analysis.

PubMed

DiMaggio, Charles; Chen, Qixuan; Muennig, Peter A; Li, Guohua

2014-12-01

In 2005, the US Congress allocated $612 million for a national Safe Routes to School (SRTS) program to encourage walking and bicycling to schools. We evaluated the effectiveness of a SRTS in controlling pedestrian injuries among school-age children. Bayesian changepoint analysis was applied to model the quarterly counts of pedestrian injuries among 5- to 19-year old children in New York City between 2001 and 2010 during school-travel hours in census tracts with and without SRTS. Overdispersed Poisson model was used to estimate difference-in-differences in injury risk between census tracts with and without SRTS following the changepoint. In SRTS-intervention census tracts, a change point in the quarterly counts of injuries was identified in the second quarter of 2008, which was consistent with the timing of the implementation of SRTS interventions. In census tracts with SRTS interventions, the estimated quarterly rates of pedestrian injury per 10,000 population among school-age children during school-travel hours were 3.47 (95% Credible Interval [CrI] 2.67, 4.39) prior to the changepoint, and 0.74 (95% CrI 0.30, 1.50) after the changepoint. There was no change in the average number of quarterly injuries in non-SRTS census tracts. Overdispersed Poisson modeling revealed that SRTS implementation was associated with a 44% reduction (95% Confidence Interval [CI] 87% decrease to 130% increase) in school-age pedestrian injury risk during school-travel hours. Bayesian changepoint analysis of quarterly counts of school-age pedestrian injuries successfully identified the timing of SRTS intervention in New York City. Implementation of the SRTS program in New York City appears to be effective in reducing school-age pedestrian injuries during school-travel hours.

A Bayesian Framework for Analysis of Pseudo-Spatial Models of Comparable Engineered Systems with Application to Spacecraft Anomaly Prediction Based on Precedent Data

NASA Astrophysics Data System (ADS)

Ndu, Obibobi Kamtochukwu

To ensure that estimates of risk and reliability inform design and resource allocation decisions in the development of complex engineering systems, early engagement in the design life cycle is necessary. An unfortunate constraint on the accuracy of such estimates at this stage of concept development is the limited amount of high fidelity design and failure information available on the actual system under development. Applying the human ability to learn from experience and augment our state of knowledge to evolve better solutions mitigates this limitation. However, the challenge lies in formalizing a methodology that takes this highly abstract, but fundamentally human cognitive, ability and extending it to the field of risk analysis while maintaining the tenets of generalization, Bayesian inference, and probabilistic risk analysis. We introduce an integrated framework for inferring the reliability, or other probabilistic measures of interest, of a new system or a conceptual variant of an existing system. Abstractly, our framework is based on learning from the performance of precedent designs and then applying the acquired knowledge, appropriately adjusted based on degree of relevance, to the inference process. This dissertation presents a method for inferring properties of the conceptual variant using a pseudo-spatial model that describes the spatial configuration of the family of systems to which the concept belongs. Through non-metric multidimensional scaling, we formulate the pseudo-spatial model based on rank-ordered subjective expert perception of design similarity between systems that elucidate the psychological space of the family. By a novel extension of Kriging methods for analysis of geospatial data to our "pseudo-space of comparable engineered systems", we develop a Bayesian inference model that allows prediction of the probabilistic measure of interest.

A model to systematically employ professional judgment in the Bayesian Decision Analysis for a semiconductor industry exposure assessment.

PubMed

Torres, Craig; Jones, Rachael; Boelter, Fred; Poole, James; Dell, Linda; Harper, Paul

2014-01-01

Bayesian Decision Analysis (BDA) uses Bayesian statistics to integrate multiple types of exposure information and classify exposures within the exposure rating categorization scheme promoted in American Industrial Hygiene Association (AIHA) publications. Prior distributions for BDA may be developed from existing monitoring data, mathematical models, or professional judgment. Professional judgments may misclassify exposures. We suggest that a structured qualitative risk assessment (QLRA) method can provide consistency and transparency in professional judgments. In this analysis, we use a structured QLRA method to define prior distributions (priors) for BDA. We applied this approach at three semiconductor facilities in South Korea, and present an evaluation of the performance of structured QLRA for determination of priors, and an evaluation of occupational exposures using BDA. Specifically, the structured QLRA was applied to chemical agents in similar exposure groups to identify provisional risk ratings. Standard priors were developed for each risk rating before review of historical monitoring data. Newly collected monitoring data were used to update priors informed by QLRA or historical monitoring data, and determine the posterior distribution. Exposure ratings were defined by the rating category with the highest probability--i.e., the most likely. We found the most likely exposure rating in the QLRA-informed priors to be consistent with historical and newly collected monitoring data, and the posterior exposure ratings developed with QLRA-informed priors to be equal to or greater than those developed with data-informed priors in 94% of comparisons. Overall, exposures at these facilities are consistent with well-controlled work environments. That is, the 95th percentile of exposure distributions are ≤50% of the occupational exposure limit (OEL) for all chemical-SEG combinations evaluated; and are ≤10% of the limit for 94% of chemical-SEG combinations evaluated.

Bayesian approach to the assessment of the population-specific risk of inhibitors in hemophilia A patients: a case study

PubMed Central

Cheng, Ji; Iorio, Alfonso; Marcucci, Maura; Romanov, Vadim; Pullenayegum, Eleanor M; Marshall, John K; Thabane, Lehana

2016-01-01

Background Developing inhibitors is a rare event during the treatment of hemophilia A. The multifacets and uncertainty surrounding the development of inhibitors further complicate the process of estimating inhibitor rate from the limited data. Bayesian statistical modeling provides a useful tool in generating, enhancing, and exploring the evidence through incorporating all the available information. Methods We built our Bayesian analysis using three study cases to estimate the inhibitor rates of patients with hemophilia A in three different scenarios: Case 1, a single cohort of previously treated patients (PTPs) or previously untreated patients; Case 2, a meta-analysis of PTP cohorts; and Case 3, a previously unexplored patient population – patients with baseline low-titer inhibitor or history of inhibitor development. The data used in this study were extracted from three published ADVATE (antihemophilic factor [recombinant] is a product of Baxter for treating hemophilia A) post-authorization surveillance studies. Noninformative and informative priors were applied to Bayesian standard (Case 1) or random-effects (Case 2 and Case 3) logistic models. Bayesian probabilities of satisfying three meaningful thresholds of the risk of developing a clinical significant inhibitor (10/100, 5/100 [high rates], and 1/86 [the Food and Drug Administration mandated cutoff rate in PTPs]) were calculated. The effect of discounting prior information or scaling up the study data was evaluated. Results Results based on noninformative priors were similar to the classical approach. Using priors from PTPs lowered the point estimate and narrowed the 95% credible intervals (Case 1: from 1.3 [0.5, 2.7] to 0.8 [0.5, 1.1]; Case 2: from 1.9 [0.6, 6.0] to 0.8 [0.5, 1.1]; Case 3: 2.3 [0.5, 6.8] to 0.7 [0.5, 1.1]). All probabilities of satisfying a threshold of 1/86 were above 0.65. Increasing the number of patients by two and ten times substantially narrowed the credible intervals for the single cohort study (1.4 [0.7, 2.3] and 1.4 [1.1, 1.8], respectively). Increasing the number of studies by two and ten times for the multiple study scenarios (Case 2: 1.9 [0.6, 4.0] and 1.9 [1.5, 2.6]; Case 3: 2.4 [0.9, 5.0] and 2.6 [1.9, 3.5], respectively) had a similar effect. Conclusion Bayesian approach as a robust, transparent, and reproducible analytic method can be efficiently used to estimate the inhibitor rate of hemophilia A in complex clinical settings. PMID:27822129

Bayesian approach to the assessment of the population-specific risk of inhibitors in hemophilia A patients: a case study.

PubMed

Cheng, Ji; Iorio, Alfonso; Marcucci, Maura; Romanov, Vadim; Pullenayegum, Eleanor M; Marshall, John K; Thabane, Lehana

2016-01-01

Developing inhibitors is a rare event during the treatment of hemophilia A. The multifacets and uncertainty surrounding the development of inhibitors further complicate the process of estimating inhibitor rate from the limited data. Bayesian statistical modeling provides a useful tool in generating, enhancing, and exploring the evidence through incorporating all the available information. We built our Bayesian analysis using three study cases to estimate the inhibitor rates of patients with hemophilia A in three different scenarios: Case 1, a single cohort of previously treated patients (PTPs) or previously untreated patients; Case 2, a meta-analysis of PTP cohorts; and Case 3, a previously unexplored patient population - patients with baseline low-titer inhibitor or history of inhibitor development. The data used in this study were extracted from three published ADVATE (antihemophilic factor [recombinant] is a product of Baxter for treating hemophilia A) post-authorization surveillance studies. Noninformative and informative priors were applied to Bayesian standard (Case 1) or random-effects (Case 2 and Case 3) logistic models. Bayesian probabilities of satisfying three meaningful thresholds of the risk of developing a clinical significant inhibitor (10/100, 5/100 [high rates], and 1/86 [the Food and Drug Administration mandated cutoff rate in PTPs]) were calculated. The effect of discounting prior information or scaling up the study data was evaluated. Results based on noninformative priors were similar to the classical approach. Using priors from PTPs lowered the point estimate and narrowed the 95% credible intervals (Case 1: from 1.3 [0.5, 2.7] to 0.8 [0.5, 1.1]; Case 2: from 1.9 [0.6, 6.0] to 0.8 [0.5, 1.1]; Case 3: 2.3 [0.5, 6.8] to 0.7 [0.5, 1.1]). All probabilities of satisfying a threshold of 1/86 were above 0.65. Increasing the number of patients by two and ten times substantially narrowed the credible intervals for the single cohort study (1.4 [0.7, 2.3] and 1.4 [1.1, 1.8], respectively). Increasing the number of studies by two and ten times for the multiple study scenarios (Case 2: 1.9 [0.6, 4.0] and 1.9 [1.5, 2.6]; Case 3: 2.4 [0.9, 5.0] and 2.6 [1.9, 3.5], respectively) had a similar effect. Bayesian approach as a robust, transparent, and reproducible analytic method can be efficiently used to estimate the inhibitor rate of hemophilia A in complex clinical settings.

Gut microbial profile analysis by MiSeq sequencing of pancreatic carcinoma patients in China

PubMed Central

Xie, Haiyang; Li, Ang; Lu, Haifeng; Xu, Shaoyan; Zhou, Lin; Zhang, Hua; Cui, Guangying; Chen, Xinhua; Liu, Yuanxing; Wu, Liming; Qin, Nan; Sun, Ranran; Wang, Wei; Li, Lanjuan; Wang, Weilin; Zheng, Shusen

2017-01-01

Pancreatic carcinoma (PC) is a lethal cancer. Gut microbiota is associated with some risk factors of PC, e.g. obesity and types II diabetes. However, the specific gut microbial profile in clinical PC in China has never been reported. This prospective study collected 85 PC and 57 matched healthy controls (HC) to analyze microbial characteristics by MiSeq sequencing. The results showed that gut microbial diversity was decreased in PC with an unique microbial profile, which partly attributed to its decrease of alpha diversity. Microbial alterations in PC featured by the increase of certain pathogens and lipopolysaccharides-producing bacteria, and the decrease of probiotics and butyrate-producing bacteria. Microbial community in obstruction cases was separated from the un-obstructed cases. Streptococcus was associated with the bile. Furthermore, 23 microbial functions e.g. Leucine and LPS biosynthesis were enriched, while 13 functions were reduced in PC. Importantly, based on 40 genera associated with PC, microbial markers achieves a high classification power with AUC of 0.842. In conclusion, gut microbial profile was unique in PC, providing a microbial marker for non-invasive PC diagnosis. PMID:29221120

The Pittsburgh Cervical Cancer Screening Model: a risk assessment tool.

PubMed

Austin, R Marshall; Onisko, Agnieszka; Druzdzel, Marek J

2010-05-01

Evaluation of cervical cancer screening has grown increasingly complex with the introduction of human papillomavirus (HPV) vaccination and newer screening technologies approved by the US Food and Drug Administration. To create a unique Pittsburgh Cervical Cancer Screening Model (PCCSM) that quantifies risk for histopathologic cervical precancer (cervical intraepithelial neoplasia [CIN] 2, CIN3, and adenocarcinoma in situ) and cervical cancer in an environment predominantly using newer screening technologies. The PCCSM is a dynamic Bayesian network consisting of 19 variables available in the laboratory information system, including patient history data (most recent HPV vaccination data), Papanicolaou test results, high-risk HPV results, procedure data, and histopathologic results. The model's graphic structure was based on the published literature. Results from 375 441 patient records from 2005 through 2008 were used to build and train the model. Additional data from 45 930 patients were used to test the model. The PCCSM compares risk quantitatively over time for histopathologically verifiable CIN2, CIN3, adenocarcinoma in situ, and cervical cancer in screened patients for each current cytology result category and for each HPV result. For each current cytology result, HPV test results affect risk; however, the degree of cytologic abnormality remains the largest positive predictor of risk. Prior history also alters the CIN2, CIN3, adenocarcinoma in situ, and cervical cancer risk for patients with common current cytology and HPV test results. The PCCSM can also generate negative risk projections, estimating the likelihood of the absence of histopathologic CIN2, CIN3, adenocarcinoma in situ, and cervical cancer in screened patients. The PCCSM is a dynamic Bayesian network that computes quantitative cervical disease risk estimates for patients undergoing cervical screening. Continuously updatable with current system data, the PCCSM provides a new tool to monitor cervical disease risk in the evolving postvaccination era.

The Society of Thoracic Surgeons Composite Measure of Individual Surgeon Performance for Adult Cardiac Surgery: A Report of The Society of Thoracic Surgeons Quality Measurement Task Force.

PubMed

Shahian, David M; He, Xia; Jacobs, Jeffrey P; Kurlansky, Paul A; Badhwar, Vinay; Cleveland, Joseph C; Fazzalari, Frank L; Filardo, Giovanni; Normand, Sharon-Lise T; Furnary, Anthony P; Magee, Mitchell J; Rankin, J Scott; Welke, Karl F; Han, Jane; O'Brien, Sean M

2015-10-01

Previous composite performance measures of The Society of Thoracic Surgeons (STS) were estimated at the STS participant level, typically a hospital or group practice. The STS Quality Measurement Task Force has now developed a multiprocedural, multidimensional composite measure suitable for estimating the performance of individual surgeons. The development sample from the STS National Database included 621,489 isolated coronary artery bypass grafting procedures, isolated aortic valve replacement, aortic valve replacement plus coronary artery bypass grafting, mitral, or mitral plus coronary artery bypass grafting procedures performed by 2,286 surgeons between July 1, 2011, and June 30, 2014. Each surgeon's composite score combined their aggregate risk-adjusted mortality and major morbidity rates (each weighted inversely by their standard deviations) and reflected the proportion of case types they performed. Model parameters were estimated in a Bayesian framework. Composite star ratings were examined using 90%, 95%, or 98% Bayesian credible intervals. Measure reliability was estimated using various 3-year case thresholds. The final composite measure was defined as 0.81 × (1 minus risk-standardized mortality rate) + 0.19 × (1 minus risk-standardized complication rate). Risk-adjusted mortality (median, 2.3%; interquartile range, 1.7% to 3.0%), morbidity (median, 13.7%; interquartile range, 10.8% to 17.1%), and composite scores (median, 95.4%; interquartile range, 94.4% to 96.3%) varied substantially across surgeons. Using 98% Bayesian credible intervals, there were 207 1-star (lower performance) surgeons (9.1%), 1,701 2-star (as-expected performance) surgeons (74.4%), and 378 3-star (higher performance) surgeons (16.5%). With an eligibility threshold of 100 cases over 3 years, measure reliability was 0.81. The STS has developed a multiprocedural composite measure suitable for evaluating performance at the individual surgeon level. Copyright © 2015 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Microbial water quality communication: public and practitioner insights from British Columbia, Canada.

PubMed

Dunn, G; Henrich, N; Holmes, B; Harris, L; Prystajecky, N

2014-09-01

This work examines the communication interactions of water suppliers and health authorities with the general public regarding microbial source water quality for recreational and drinking water. We compare current approaches to risk communication observable in British Columbia (BC), Canada, with best practices derived from the communications literature, finding significant gaps between theory and practice. By considering public views and government practices together, we identify key disconnects, leading to the conclusion that at present, neither the public's needs nor public health officials' goals are being met. We find: (1) there is a general lack of awareness and poor understanding by the public of microbial threats to water and the associated health implications; (2) the public often does not know where to find water quality information; (3) public information needs are not identified or met; (4) information sharing by authorities is predominantly one-way and reactive (crisis-oriented); and (5) the effectiveness of communications is not evaluated. There is a need for both improved public understanding of water quality-related risks, and new approaches to ensure information related to water quality reaches audiences. Overall, greater attention should be given to planning and goal setting related to microbial water risk communication.

Deriving site-specific soil clean-up values for metals and metalloids: Rationale for including protection of soil microbial processes

PubMed Central

Kuperman, Roman G; Siciliano, Steven D; Römbke, Jörg; Oorts, Koen

2014-01-01

Although it is widely recognized that microorganisms are essential for sustaining soil fertility, structure, nutrient cycling, groundwater purification, and other soil functions, soil microbial toxicity data were excluded from the derivation of Ecological Soil Screening Levels (Eco-SSL) in the United States. Among the reasons for such exclusion were claims that microbial toxicity tests were too difficult to interpret because of the high variability of microbial responses, uncertainty regarding the relevance of the various endpoints, and functional redundancy. Since the release of the first draft of the Eco-SSL Guidance document by the US Environmental Protection Agency in 2003, soil microbial toxicity testing and its use in ecological risk assessments have substantially improved. A wide range of standardized and nonstandardized methods became available for testing chemical toxicity to microbial functions in soil. Regulatory frameworks in the European Union and Australia have successfully incorporated microbial toxicity data into the derivation of soil threshold concentrations for ecological risk assessments. This article provides the 3-part rationale for including soil microbial processes in the development of soil clean-up values (SCVs): 1) presenting a brief overview of relevant test methods for assessing microbial functions in soil, 2) examining data sets for Cu, Ni, Zn, and Mo that incorporated soil microbial toxicity data into regulatory frameworks, and 3) offering recommendations on how to integrate the best available science into the method development for deriving site-specific SCVs that account for bioavailability of metals and metalloids in soil. Although the primary focus of this article is on the development of the approach for deriving SCVs for metals and metalloids in the United States, the recommendations provided in this article may also be applicable in other jurisdictions that aim at developing ecological soil threshold values for protection of microbial processes in contaminated soils. PMID:24376192

Mining pharmacovigilance data using Bayesian logistic regression with James-Stein type shrinkage estimation.

PubMed

An, Lihua; Fung, Karen Y; Krewski, Daniel

2010-09-01

Spontaneous adverse event reporting systems are widely used to identify adverse reactions to drugs following their introduction into the marketplace. In this article, a James-Stein type shrinkage estimation strategy was developed in a Bayesian logistic regression model to analyze pharmacovigilance data. This method is effective in detecting signals as it combines information and borrows strength across medically related adverse events. Computer simulation demonstrated that the shrinkage estimator is uniformly better than the maximum likelihood estimator in terms of mean squared error. This method was used to investigate the possible association of a series of diabetic drugs and the risk of cardiovascular events using data from the Canada Vigilance Online Database.

Implications of Nonlinear Concentration Response Curve for Ozone related Mortality on Health Burden Assessment

EPA Science Inventory

We characterize the sensitivity of the ozone attributable health burden assessment with respect to different modeling strategies of concentration-response function. For this purpose, we develop a flexible Bayesian hierarchical model allowing for a nonlinear ozone risk curve with ...

Spatial analysis of county-based gonorrhoea incidence in mainland China, from 2004 to 2009.

PubMed

Yin, Fei; Feng, Zijian; Li, Xiaosong

2012-07-01

Gonorrhoea is one of the most common sexually transmissible infections in mainland China. Effective spatial monitoring of gonorrhoea incidence is important for successful implementation of control and prevention programs. The county-level gonorrhoea incidence rates for all of mainland China was monitored through examining spatial patterns. County-level data on gonorrhoea cases between 2004 and 2009 were obtained from the China Information System for Disease Control and Prevention. Bayesian smoothing and exploratory spatial data analysis (ESDA) methods were used to characterise the spatial distribution pattern of gonorrhoea cases. During the 6-year study period, the average annual gonorrhoea incidence was 12.41 cases per 100000 people. Using empirical Bayes smoothed rates, the local Moran test identified one significant single-centre cluster and two significant multi-centre clusters of high gonorrhoea risk (all P-values <0.01). Bayesian smoothing and ESDA methods can assist public health officials in using gonorrhoea surveillance data to identify high risk areas. Allocating more resources to such areas could effectively reduce gonorrhoea incidence.

A Bayesian approach for incorporating economic factors in sample size design for clinical trials of individual drugs and portfolios of drugs.

PubMed

Patel, Nitin R; Ankolekar, Suresh

2007-11-30

Classical approaches to clinical trial design ignore economic factors that determine economic viability of a new drug. We address the choice of sample size in Phase III trials as a decision theory problem using a hybrid approach that takes a Bayesian view from the perspective of a drug company and a classical Neyman-Pearson view from the perspective of regulatory authorities. We incorporate relevant economic factors in the analysis to determine the optimal sample size to maximize the expected profit for the company. We extend the analysis to account for risk by using a 'satisficing' objective function that maximizes the chance of meeting a management-specified target level of profit. We extend the models for single drugs to a portfolio of clinical trials and optimize the sample sizes to maximize the expected profit subject to budget constraints. Further, we address the portfolio risk and optimize the sample sizes to maximize the probability of achieving a given target of expected profit.

Bayesian Approach for Flexible Modeling of Semicompeting Risks Data

PubMed Central

Han, Baoguang; Yu, Menggang; Dignam, James J.; Rathouz, Paul J.

2016-01-01

Summary Semicompeting risks data arise when two types of events, non-terminal and terminal, are observed. When the terminal event occurs first, it censors the non-terminal event, but not vice versa. To account for possible dependent censoring of the non-terminal event by the terminal event and to improve prediction of the terminal event using the non-terminal event information, it is crucial to model their association properly. Motivated by a breast cancer clinical trial data analysis, we extend the well-known illness-death models to allow flexible random effects to capture heterogeneous association structures in the data. Our extension also represents a generalization of the popular shared frailty models that usually assume that the non-terminal event does not affect the hazards of the terminal event beyond a frailty term. We propose a unified Bayesian modeling approach that can utilize existing software packages for both model fitting and individual specific event prediction. The approach is demonstrated via both simulation studies and a breast cancer data set analysis. PMID:25274445

Phylogenetic analysis and victim contact tracing of rabies virus from humans and dogs in Bali, Indonesia.

PubMed

Mahardika, G N K; Dibia, N; Budayanti, N S; Susilawathi, N M; Subrata, K; Darwinata, A E; Wignall, F S; Richt, J A; Valdivia-Granda, W A; Sudewi, A A R

2014-06-01

The emergence of human and animal rabies in Bali since November 2008 has attracted local, national and international interest. The potential origin and time of introduction of rabies virus to Bali is described. The nucleoprotein (N) gene of rabies virus from dog brain and human clinical specimens was sequenced using an automated DNA sequencer. Phylogenetic inference with Bayesian Markov Chain Monte Carlo (MCMC) analysis using the Bayesian Evolutionary Analysis by Sampling Trees (BEAST) v. 1.7.5 software confirmed that the outbreak of rabies in Bali was caused by an Indonesian lineage virus following a single introduction. The ancestor of Bali viruses was the descendant of a virus from Kalimantan. Contact tracing showed that the event most likely occurred in early 2008. The introduction of rabies into a large unvaccinated dog population in Bali clearly demonstrates the risk of disease transmission for government agencies and should lead to an increased preparedness and efforts for sustained risk reduction to prevent such events from occurring in future.

The Risk of Microbial Contamination in Multiple-Dose Preservative-Free Ophthalmic Preparations.

PubMed

Saisyo, Atsuyuki; Shimono, Rima; Oie, Shigeharu; Kimura, Kazuhiro; Furukawa, Hiroyuki

2017-01-01

Multiple-dose ophthalmic preparations that do not contain preservatives carry high risks of microbial contamination. However, there are various types of hospital preparations, with different physicochemical properties. In the present study, we evaluated the association between physicochemical properties and microbial contamination in ophthalmic preparations. The investigated hospital preparations included ophthalmic preparations of physiological saline, 0.2% fluconazole, 0.5% vancomycin hydrochloride, and 2% cyclosporine. We investigated the microbial dynamics of each ophthalmic preparation and microbial contamination in ophthalmic preparations used by patients. Remarkable growth of Pseudomonas aeruginosa, Burkholderia cepacia, and Serratia marcescens was observed in ophthalmic preparations of physiological saline and 0.2% fluconazole. All tested microorganisms displayed decreased counts after inoculation in 0.5% vancomycin hydrochloride. In 2% cyclosporine, all investigated microorganisms were below the limit of detection after inoculation for 6 h. The microbial contamination rates of ophthalmic preparations used by patients were 16.7% (3/18 samples) for 0.5% vancomycin hydrochloride and 0% (0/30 samples) for 2% cyclosporine. All detected contaminants in 0.5% vancomycin hydrochloride were Candida spp., one of which was present at a level of 1×10 4 colony-forming units/mL. The storage method for in-use ophthalmic preparations should be considered on the basis of their physicochemical properties.

Reestablishment of recipient-associated microbiota in the lung allograft is linked to reduced risk of bronchiolitis obliterans syndrome.

PubMed

Willner, Dana L; Hugenholtz, Philip; Yerkovich, Stephanie T; Tan, Maxine E; Daly, Joshua N; Lachner, Nancy; Hopkins, Peter M; Chambers, Daniel C

2013-03-15

Bronchiolitis obliterans syndrome (BOS) is the primary limiting factor for long-term survival after lung transplantation, and has previously been associated with microbial infections. To cross-sectionally and longitudinally characterize microbial communities in allografts from transplant recipients with and without BOS using a culture-independent method based on high-throughput sequencing. Allografts were sampled by bronchoalveolar lavage, and microbial communities were profiled using 16S rRNA gene amplicon pyrosequencing. Community profiles were compared using the weighted Unifrac metric and the relationship between microbial populations, BOS, and other covariates was explored using PERMANOVA and logistic regression. Microbial communities in transplant patients fell into two main groups: those dominated by Pseudomonas or those dominated by Streptococcus and Veillonella, which seem to be mutually exclusive lung microbiomes. Aspergillus culture was also negatively correlated with the Pseudomonas-dominated group. The reestablishment of dominant populations present in patients pretransplant, notably Pseudomonas in individuals with cystic fibrosis, was negatively correlated with BOS. Recolonization of the allograft by Pseudomonas in individuals with cystic fibrosis is not associated with BOS. In general, reestablishment of pretransplant lung populations in the allograft seems to have a protective effect against BOS, whereas de novo acquisition of microbial populations often belonging to the same genera may increase the risk of BOS.

Gut microbiota and cardiometabolic outcomes: influence of dietary patterns and their associated components.

PubMed

Wong, Julia M W

2014-07-01

Many dietary patterns have been associated with cardiometabolic risk reduction. A commonality between these dietary patterns is the emphasis on plant-based foods. Studies in individuals who consume vegetarian and vegan diets have shown a reduced risk of cardiovascular events and incidence of diabetes. Plant-based dietary patterns may promote a more favorable gut microbial profile. Such diets are high in dietary fiber and fermentable substrate (ie, nondigestible or undigested carbohydrates), which are sources of metabolic fuel for gut microbial fermentation and, in turn, result in end products that may be used by the host (eg, short-chain fatty acids). These end products may have direct or indirect effects on modulating the health of their host. Modulation of the gut microbiota is an area of growing interest, and it has been suggested to have the potential to reduce risk factors associated with chronic diseases. Examples of dietary components that alter the gut microbial composition include prebiotics and resistant starches. Emerging evidence also suggests a potential link between interindividual differences in the gut microbiota and variations in physiology or predisposition to certain chronic disease risk factors. Alterations in the gut microbiota may also stimulate certain populations and may assist in biotransformation of bioactive components found in plant foods. Strategies to modify microbial communities may therefore provide a novel approach in the treatment and management of chronic diseases. © 2014 American Society for Nutrition.

Are antimicrobial defences in bird eggs related to climatic conditions associated with risk of trans-shell microbial infection?

PubMed Central

2014-01-01

Introduction All bird eggs are exposed to microbes in the environment, which if transmitted to the developing embryo, could cause hatching failure. However, the risk of trans-shell infection varies with environmental conditions and is higher for eggs laid in wetter environments. This might relate to generally higher microbial abundances and diversity in more humid environments, including on the surface of eggshells, as well as the need for moisture to facilitate microbial penetration of the eggshell. To protect against microbial infection, the albumen of avian eggs contains antimicrobial proteins, including lysozyme and ovotransferrin. We tested whether lysozyme and ovotransferrin activities varied in eggs of larks (Alaudidae) living along an arid-mesic gradient of environmental aridity, which we used as a proxy for risk of trans-shell infection. Results Contrary to expectations, lysozyme activity was highest in eggs from hotter, more arid locations, where we predicted the risk of trans-shell infection would be lower. Ovotransferrin concentrations did not vary with climatic factors. Temperature was a much better predictor of antimicrobial protein activity than precipitation, a result inconsistent with studies stressing the importance of moisture for trans-shell infection. Conclusions Our study raises interesting questions about the links between temperature and lysozyme activity in eggs, but we find no support for the hypothesis that antimicrobial protein deposition is higher in eggs laid in wetter environments. PMID:25057281

Are antimicrobial defences in bird eggs related to climatic conditions associated with risk of trans-shell microbial infection?

PubMed

Horrocks, Nicholas Pc; Hine, Kathryn; Hegemann, Arne; Ndithia, Henry K; Shobrak, Mohammed; Ostrowski, Stéphane; Williams, Joseph B; Matson, Kevin D; Tieleman, B Irene

2014-01-01

All bird eggs are exposed to microbes in the environment, which if transmitted to the developing embryo, could cause hatching failure. However, the risk of trans-shell infection varies with environmental conditions and is higher for eggs laid in wetter environments. This might relate to generally higher microbial abundances and diversity in more humid environments, including on the surface of eggshells, as well as the need for moisture to facilitate microbial penetration of the eggshell. To protect against microbial infection, the albumen of avian eggs contains antimicrobial proteins, including lysozyme and ovotransferrin. We tested whether lysozyme and ovotransferrin activities varied in eggs of larks (Alaudidae) living along an arid-mesic gradient of environmental aridity, which we used as a proxy for risk of trans-shell infection. Contrary to expectations, lysozyme activity was highest in eggs from hotter, more arid locations, where we predicted the risk of trans-shell infection would be lower. Ovotransferrin concentrations did not vary with climatic factors. Temperature was a much better predictor of antimicrobial protein activity than precipitation, a result inconsistent with studies stressing the importance of moisture for trans-shell infection. Our study raises interesting questions about the links between temperature and lysozyme activity in eggs, but we find no support for the hypothesis that antimicrobial protein deposition is higher in eggs laid in wetter environments.

Microbiology and Crew Medical Events on the International Space Station

NASA Technical Reports Server (NTRS)

Oubre, Cherie M.; Charvat, Jacqueline M.; Kadwa, Biniafer; Taiym, Wafa; Ott, C. Mark; Pierson, Duane; Baalen, Mary Van

2014-01-01

The closed environment of the International Space Station (ISS) creates an ideal environment for microbial growth. Previous studies have identified the ubiquitous nature of microorganisms throughout the space station environment. To ensure safety of the crew, microbial monitoring of air and surface within ISS began in December 2000 and continues to be monitored on a quarterly basis. Water monitoring began in 2009 when the potable water dispenser was installed on ISS. However, it is unknown if high microbial counts are associated with inflight medical events. The microbial counts are determined for the air, surface, and water samples collected during flight operations and samples are returned to the Microbiology laboratory at the Johnson Space Center for identification. Instances of microbial counts above the established microbial limit requirements were noted and compared inflight medical events (any non-injury event such as illness, rashes, etc.) that were reported during the same calendar-quarter. Data were analyzed using repeated measures logistic regression for the forty-one US astronauts flew on ISS between 2000 and 2012. In that time frame, instances of microbial counts being above established limits were found for 10 times for air samples, 22 times for surface samples and twice for water. Seventy-eight inflight medical events were reported among the astronauts. A three times greater risk of a medical event was found when microbial samples were found to be high (OR = 3.01; p =.007). Engineering controls, crew training, and strict microbial limits have been established to mitigate the crew medical events and environmental risks. Due to the timing issues of sampling and the samples return to earth, identification of particular microorganisms causing a particular inflight medical event is difficult. Further analyses are underway.

Risk of fire occurrence in arid and semi-arid ecosystems of Iran: an investigation using Bayesian belief networks.

PubMed

Bashari, Hossein; Naghipour, Ali Asghar; Khajeddin, Seyed Jamaleddin; Sangoony, Hamed; Tahmasebi, Pejman

2016-09-01

Identifying areas that have a high risk of burning is a main component of fire management planning. Although the available tools can predict the fire risks, these are poor in accommodating uncertainties in their predictions. In this study, we accommodated uncertainty in wildfire prediction using Bayesian belief networks (BBNs). An influence diagram was developed to identify the factors influencing wildfire in arid and semi-arid areas of Iran, and it was populated with probabilities to produce a BBNs model. The behavior of the model was tested using scenario and sensitivity analysis. Land cover/use, mean annual rainfall, mean annual temperature, elevation, and livestock density were recognized as the main variables determining wildfire occurrence. The produced model had good accuracy as its ROC area under the curve was 0.986. The model could be applied in both predictive and diagnostic analysis for answering "what if" and "how" questions. The probabilistic relationships within the model can be updated over time using observation and monitoring data. The wildfire BBN model may be updated as new knowledge emerges; hence, it can be used to support the process of adaptive management.

Human values and beliefs and concern about climate change: a Bayesian longitudinal analysis.

PubMed

Prati, Gabriele; Pietrantoni, Luca; Albanesi, Cinzia

2018-01-01

The aim of this study was to investigate the influence of human values on beliefs and concern about climate change using a longitudinal design and Bayesian analysis. A sample of 298 undergraduate/master students filled out the same questionnaire on two occasions at an interval of 2 months. The questionnaire included measures of beliefs and concern about climate change (i.e., perceived consequences, risk perception, and skepticism) and human values (i.e., the Portrait Values Questionnaire). After controlling for gender and the respective baseline score, universalism at Time 1 was associated with higher levels of perceived consequences of climate change and lower levels of climate change skepticism. Self-direction at Time 1 predicted Time 2 climate change risk perception and perceived consequences of climate change. Hedonism at Time 1 was associated with Time 2 climate change risk perception. The other human values at Time 1 were not associated with any of the measures of beliefs and concern about climate change at Time 2. The results of this study suggest that a focus on universalism and self-direction values seems to be a more successful approach to stimulate public engagement with climate change than a focus on other human values.

Assessment of accident severity in the construction industry using the Bayesian theorem.

PubMed

Alizadeh, Seyed Shamseddin; Mortazavi, Seyed Bagher; Mehdi Sepehri, Mohammad

2015-01-01

Construction is a major source of employment in many countries. In construction, workers perform a great diversity of activities, each one with a specific associated risk. The aim of this paper is to identify workers who are at risk of accidents with severe consequences and classify these workers to determine appropriate control measures. We defined 48 groups of workers and used the Bayesian theorem to estimate posterior probabilities about the severity of accidents at the level of individuals in construction sector. First, the posterior probabilities of injuries based on four variables were provided. Then the probabilities of injury for 48 groups of workers were determined. With regard to marginal frequency of injury, slight injury (0.856), fatal injury (0.086) and severe injury (0.058) had the highest probability of occurrence. It was observed that workers with <1 year's work experience (0.168) had the highest probability of injury occurrence. The first group of workers, who were extensively exposed to risk of severe and fatal accidents, involved workers ≥ 50 years old, married, with 1-5 years' work experience, who had no past accident experience. The findings provide a direction for more effective safety strategies and occupational accident prevention and emergency programmes.

Use of a commercial enzyme-linked immunosorbent assay for rapid detection of Giardia duodenalis in dog stools in the environment: a Bayesian evaluation.

PubMed

Papini, Roberto; Carreras, Giulia; Marangi, Marianna; Mancianti, Francesca; Giangaspero, Annunziata

2013-05-01

Giardia duodenalis is considered a potentially zoonotic protozoan. Some animal species, including infected dogs, may play an important role in the spread of Giardia cysts through environmental contamination with their feces. In the present study, a commercial enzyme-linked immunosorbent assay (ELISA) was used to examine 143 samples of dog feces collected in urban areas as an indicator of the risk of field contamination. Using a Bayesian statistical approach, the ELISA showed a sensitivity of 88.9% and a specificity of 95.8% with positive and negative predictive values of 89.6% and 95.4%, respectively. The test affords the advantage of rapid processing of fecal samples without a complex technical structure and extensive costly labor. Moreover, the present results show that the assay provides public health veterinarians with a practical tool that can be used in screening programs, as a valid alternative or as an adjunct to other tests, in order to assess the biological risk of exposure to G. duodenalis cysts from dogs in human settlements. However, the test may not be completely accurate for human health risk evaluation, as it does not discriminate between zoonotic and non-zoonotic isolates.

Estimating micro area behavioural risk factor prevalence from large population-based surveys: a full Bayesian approach.

PubMed

Seliske, L; Norwood, T A; McLaughlin, J R; Wang, S; Palleschi, C; Holowaty, E

2016-06-07

An important public health goal is to decrease the prevalence of key behavioural risk factors, such as tobacco use and obesity. Survey information is often available at the regional level, but heterogeneity within large geographic regions cannot be assessed. Advanced spatial analysis techniques are demonstrated to produce sensible micro area estimates of behavioural risk factors that enable identification of areas with high prevalence. A spatial Bayesian hierarchical model was used to estimate the micro area prevalence of current smoking and excess bodyweight for the Erie-St. Clair region in southwestern Ontario. Estimates were mapped for male and female respondents of five cycles of the Canadian Community Health Survey (CCHS). The micro areas were 2006 Census Dissemination Areas, with an average population of 400-700 people. Two individual-level models were specified: one controlled for survey cycle and age group (model 1), and one controlled for survey cycle, age group and micro area median household income (model 2). Post-stratification was used to derive micro area behavioural risk factor estimates weighted to the population structure. SaTScan analyses were conducted on the granular, postal-code level CCHS data to corroborate findings of elevated prevalence. Current smoking was elevated in two urban areas for both sexes (Sarnia and Windsor), and an additional small community (Chatham) for males only. Areas of excess bodyweight were prevalent in an urban core (Windsor) among males, but not females. Precision of the posterior post-stratified current smoking estimates was improved in model 2, as indicated by narrower credible intervals and a lower coefficient of variation. For excess bodyweight, both models had similar precision. Aggregation of the micro area estimates to CCHS design-based estimates validated the findings. This is among the first studies to apply a full Bayesian model to complex sample survey data to identify micro areas with variation in risk factor prevalence, accounting for spatial correlation and other covariates. Application of micro area analysis techniques helps define areas for public health planning, and may be informative to surveillance and research modeling of relevant chronic disease outcomes.

Occurrence of Mycobacterium avium subspecies paratuberculosis and Neospora caninum in Alberta cow-calf operations.

PubMed

Pruvot, M; Kutz, S; Barkema, H W; De Buck, J; Orsel, K

2014-11-01

Mycobacterium avium subsp. paratuberculosis (MAP) and Neospora caninum (NC) are two pathogens causing important production limiting diseases in the cattle industry. Significant impacts of MAP and NC have been reported on dairy cattle herds, but little is known about the importance, risk factors and transmission patterns in western Canadian cow-calf herds. In this cross-sectional study, the prevalence of MAP and NC infection in southwest Alberta cow-calf herds was estimated, risk factors for NC were identified, and the reproductive impacts of the two pathogens were assessed. Blood and fecal samples were collected from 840 cows on 28 cow-calf operations. Individual cow and herd management information was collected by self-administered questionnaires and one-on-one interviews. Bayesian estimates of the true prevalence of MAP and NC were computed, and bivariable and multivariable statistical analysis were done to assess the association between the NC serological status and ranch management risk factors, and the clinical effects of the two pathogens. Bayesian estimates of true prevalence indicated that 20% (95% probability interval: 8-38%) of herds had at least one MAP-positive cow, with a within-herd prevalence in positive herds of 22% (8-45%). From the Bayesian posterior distributions of NC prevalence, the median herd-level prevalence was 66% (33-95%) with 10% (4-21%) cow-level prevalence in positive herds. Multivariable analysis indicated that introducing purchased animals in the herd might increase the risk of NC. The negative association of NC with proper carcass disposal and presence of horses on ranch (possibly in relation to herd monitoring and guarding activities), may suggest the importance of wild carnivores in the dynamics of this pathogen in the study area. We also observed an association between MAP and NC serological status and the number of abortions. Additional studies should be done to further examine specific risk factors for MAP and NC, assess the consequences on the reproductive performances in cow-calf herds, and evaluate the overall impact of these pathogens on cow-calf operations. Copyright © 2014 Elsevier B.V. All rights reserved.

Microbial community in a full-scale drinking water biosand filter.

PubMed

Feng, Shuo; Chen, Chao; Wang, Qingfeng; Yang, Zhiyu; Zhang, Xiaojian; Xie, Shuguang

2013-04-01

To remove turbidity and minimize microbiological risks, rapid sand filtration is one of main drinking water treatment processes in the world. However, after a long-term operation, sand particles will be colonized by microorganisms which can remove biodegradable organic matters and nitrogen compounds. In this study, 16S rRNA gene clone library analysis was applied to characterize the microbial community in a full-scale biosand filter used for drinking water treatment. The results indicate that phylum Nitrospirae and class Alphaproteobacteria were the dominant bacterial groups in the biosand sample collected from the upper filter layer. The dominance of Sphingomonas species might pose a microbiological risk. This work could provide some new insights into microbial community in drinking water biofilter.

Occurrence and quantitative microbial risk assessment of Cryptosporidium and Giardia in soil and air samples.

PubMed

Balderrama-Carmona, Ana Paola; Gortáres-Moroyoqui, Pablo; Álvarez-Valencia, Luis Humberto; Castro-Espinoza, Luciano; Mondaca-Fernández, Iram; Balderas-Cortés, José de Jesús; Chaidez-Quiroz, Cristóbal; Meza-Montenegro, María Mercedes

2014-09-01

Cryptosporidium oocysts and Giardia cysts can be transmitted by the fecal-oral route and may cause gastrointestinal parasitic zoonoses. These zoonoses are common in rural zones due to the parasites being harbored in fecally contaminated soil. This study assessed the risk of illness (giardiasis and cryptosporidiosis) from inhaling and/or ingesting soil and/or airborne dust in Potam, Mexico. To assess the risk of infection, Quantitative Microbial Risk Assessment (QMRA) was employed, with the following steps: (1) hazard identification, (2) hazard exposure, (3) dose-response, and (4) risk characterization. Cryptosporidium oocysts and Giardia cysts were observed in 52% and 57%, respectively, of total soil samples (n=21), and in 60% and 80%, respectively, of air samples (n=12). The calculated annual risks were higher than 9.9 × 10(-1) for both parasites in both types of sample. Soil and air inhalation and/or ingestion are important vehicles for these parasites. To our knowledge, the results obtained in the present study represent the first QMRAs for cryptosporidiosis and giardiasis due to soil and air inhalation/ingestion in Mexico. In addition, this is the first evidence of the microbial air quality around these parasites in rural zones. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

The Impact of Human Activities on Microbial Quality of Rivers in the Vhembe District, South Africa.

PubMed

Traoré, Afsatou N; Mulaudzi, Khodani; Chari, Gamuchirai J E; Foord, Stefan H; Mudau, Lutendo S; Barnard, Tobias G; Potgieter, Natasha

2016-08-12

Water quality testing is dictated by microbial agents found at the time of sampling in reference to their acceptable risk levels. Human activities might contaminate valuable water resources and add to the microbial load present in water bodies. Therefore, the effects of human activities on the microbial quality of rivers collected from twelve catchments in the Vhembe District in South Africa were investigated, with samples analyzed for total coliform (TC) and Eschericha coli (E. coli) contents. Physical parameters and various human activities were recorded for each sampling site. The Quanti-Tray(®) method was adopted for the assessment of TC and E. coli contents in the rivers over a two-year period. A multiplex polymerase chain (PCR) method was used to characterize the strains of E. coli found. The microbial quality of the rivers was poor with both TC and E. coli contents found to be over acceptable limits set by the South African Department of Water and Sanitation (DWS). No significant difference (p > 0.05) was detected between TC and E. coli risks in dry and wet seasons. All six pathogenic E. coli strains were identified and Enteroaggregative E. coli (EAEC), atypical Enteropathogenic E. coli (a-EPEC) and Enterotoxigenic E. coli (ETEC) were the most prevalent E. coli strains detected (respectively, 87%, 86% and 83%). The study indicated that contamination in the majority of sampling sites, due to human activities such as car wash, animal grazing and farming, poses health risks to communities using the rivers for various domestic chores. It is therefore recommended that more education by the respective departments is done to avert pollution of rivers and prevent health risks to the communities in the Vhembe District.

The Impact of Human Activities on Microbial Quality of Rivers in the Vhembe District, South Africa

PubMed Central

Traoré, Afsatou N.; Mulaudzi, Khodani; Chari, Gamuchirai J.E.; Foord, Stefan H.; Mudau, Lutendo S.; Barnard, Tobias G.; Potgieter, Natasha

2016-01-01

Background: Water quality testing is dictated by microbial agents found at the time of sampling in reference to their acceptable risk levels. Human activities might contaminate valuable water resources and add to the microbial load present in water bodies. Therefore, the effects of human activities on the microbial quality of rivers collected from twelve catchments in the Vhembe District in South Africa were investigated, with samples analyzed for total coliform (TC) and Eschericha coli (E. coli) contents. Methods: Physical parameters and various human activities were recorded for each sampling site. The Quanti-Tray® method was adopted for the assessment of TC and E. coli contents in the rivers over a two-year period. A multiplex polymerase chain (PCR) method was used to characterize the strains of E. coli found. Results: The microbial quality of the rivers was poor with both TC and E. coli contents found to be over acceptable limits set by the South African Department of Water and Sanitation (DWS). No significant difference (p > 0.05) was detected between TC and E. coli risks in dry and wet seasons. All six pathogenic E. coli strains were identified and Enteroaggregative E. coli (EAEC), atypical Enteropathogenic E. coli (a-EPEC) and Enterotoxigenic E. coli (ETEC) were the most prevalent E. coli strains detected (respectively, 87%, 86% and 83%). Conclusions: The study indicated that contamination in the majority of sampling sites, due to human activities such as car wash, animal grazing and farming, poses health risks to communities using the rivers for various domestic chores. It is therefore recommended that more education by the respective departments is done to avert pollution of rivers and prevent health risks to the communities in the Vhembe District. PMID:27529265

Method Analysis of Microbial-Resistant Gypsum Products

EPA Science Inventory

Method Analysis of Microbial-Resistant Gypsum ProductsD.A. Betancourt1, T.R.Dean1, A. Evans2, and G.Byfield2 1. US Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory; RTP, NC 277112. RTI International, RTP, NCSeveral...

Quantitative microbial risk assessment for Escherichia coli O157:H7, Salmonella enterica, and Listeria monocytogenes in leafy green vegetables consumed at salad bars, based on modeling supply chain logistics.

PubMed

Tromp, S O; Rijgersberg, H; Franz, E

2010-10-01

Quantitative microbial risk assessments do not usually account for the planning and ordering mechanisms (logistics) of a food supply chain. These mechanisms and consumer demand determine the storage and delay times of products. The aim of this study was to quantitatively assess the difference between simulating supply chain logistics (MOD) and assuming fixed storage times (FIX) in microbial risk estimation for the supply chain of fresh-cut leafy green vegetables destined for working-canteen salad bars. The results of the FIX model were previously published (E. Franz, S. O. Tromp, H. Rijgersberg, and H. J. van der Fels-Klerx, J. Food Prot. 73:274-285, 2010). Pathogen growth was modeled using stochastic discrete-event simulation of the applied logistics concept. The public health effects were assessed by conducting an exposure assessment and risk characterization. The relative growths of Escherichia coli O157 (17%) and Salmonella enterica (15%) were identical in the MOD and FIX models. In contrast, the relative growth of Listeria monocytogenes was considerably higher in the MOD model (1,156%) than in the FIX model (194%). The probability of L. monocytogenes infection in The Netherlands was higher in the MOD model (5.18×10(-8)) than in the FIX model (1.23×10(-8)). The risk of listeriosis-induced fetal mortality in the perinatal population increased from 1.24×10(-4) (FIX) to 1.66×10(-4) (MOD). Modeling the probabilistic nature of supply chain logistics is of additional value for microbial risk assessments regarding psychrotrophic pathogens in food products for which time and temperature are the postharvest preventive measures in guaranteeing food safety.

Microbial Keratitis in Kingdom of Bahrain: Clinical and Microbiology Study

PubMed Central

Al-Yousuf, Nada

2009-01-01

Background: Microbial keratitis is a potentially vision threatening condition worldwide. Knowing the predisposing factors and etiologic microorganism can help control and prevent this problem. This is the first study of its kind in Kingdom of Bahrain. Objective: To study the profile of microbial keratitis in Bahrain with special focus on risk factors, clinical outcome and microbilogical results. Methods: A retrospective analysis of all patients admitted in Salmaniya Medical Complex over a period of three years from January 2005 to January 2007 was performed. A total of 285 patients with keratitis were analysed. Non infectious corneal ulceration were excluded. Data collected from medical records were demographic features, predisposing factors, history of corneal trauma, associated ocular conditions, visual acuity at the time of presentation and the clinical course. Predisposing risk factors measured were contact lens use, presence of blepharitis, diabetes, lid abnormalities, dry eyes, keratoplasty and refractive surgery. For contact lens wearers any contact lens related risk factors that can lead to keratitis were measured. Pearson's chi-square test was used to carry out statistical analysis wherever required. Results: Contact lens wear, as a risk factor for microbial keratitis, formed 40% of the total study population. Other risk factors identified were dry eyes 24 cases (8%), 10 blepharitis (3%), 22 trauma (8%), abnormal lid position 14 cases (5%). 6 patients keratitis in a graft (2%), 3 had refractive surgery (1%). The most common causative organism isolated was pseudomonas aeroginosa (54%) followed by streptococcus 12%, staph 10%, other organisms 6%. 95% of contact lens wearers had pseudomonas Aeroginosa. This was statistically significant (p< 0.0001). The vast majority, 92% healed with scarring. 1% needed therapeutic keratoplasty and 7% lost to follow up. Risk factors in contact lens wearers were; 41 patients (36%) slept with the contact lenses. 12 (8%) had contact lens related trauma and 8 (7%) had poor hygiene. Sleeping with the contact lenses was statistically significant (p< 0.0001). Conclusion & Recommendation: Contact lens wear is the major risk factor for microbial keratitis in Bahrain. Pseudomonas aeroginosa was the commonest bacteria isolated. Sleeping with the contact lenses is the major risk factor among contact lens wearers. Majority of keratitis patients resulted in permanent scarring on the cornea. Educating the public, especially on contact lens care and precaution, can help reduce this visual morbidity. PMID:20142952

Consumers' behavior in quantitative microbial risk assessment for pathogens in raw milk: Incorporation of the likelihood of consumption as a function of storage time and temperature.

PubMed

Crotta, Matteo; Paterlini, Franco; Rizzi, Rita; Guitian, Javier

2016-02-01

Foodborne disease as a result of raw milk consumption is an increasing concern in Western countries. Quantitative microbial risk assessment models have been used to estimate the risk of illness due to different pathogens in raw milk. In these models, the duration and temperature of storage before consumption have a critical influence in the final outcome of the simulations and are usually described and modeled as independent distributions in the consumer phase module. We hypothesize that this assumption can result in the computation, during simulations, of extreme scenarios that ultimately lead to an overestimation of the risk. In this study, a sensorial analysis was conducted to replicate consumers' behavior. The results of the analysis were used to establish, by means of a logistic model, the relationship between time-temperature combinations and the probability that a serving of raw milk is actually consumed. To assess our hypothesis, 2 recently published quantitative microbial risk assessment models quantifying the risks of listeriosis and salmonellosis related to the consumption of raw milk were implemented. First, the default settings described in the publications were kept; second, the likelihood of consumption as a function of the length and temperature of storage was included. When results were compared, the density of computed extreme scenarios decreased significantly in the modified model; consequently, the probability of illness and the expected number of cases per year also decreased. Reductions of 11.6 and 12.7% in the proportion of computed scenarios in which a contaminated milk serving was consumed were observed for the first and the second study, respectively. Our results confirm that overlooking the time-temperature dependency may yield to an important overestimation of the risk. Furthermore, we provide estimates of this dependency that could easily be implemented in future quantitative microbial risk assessment models of raw milk pathogens. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Modeling tools for the assessment of microbiological risks during floods: a review

NASA Astrophysics Data System (ADS)

Collender, Philip; Yang, Wen; Stieglitz, Marc; Remais, Justin

2015-04-01

Floods are a major, recurring source of harm to global economies and public health. Projected increases in the frequency and intensity of heavy precipitation events under future climate change, coupled with continued urbanization in areas with high risk of floods, may exacerbate future impacts of flooding. Improved flood risk management is essential to support global development, poverty reduction and public health, and is likely to be a crucial aspect of climate change adaptation. Importantly, floods can facilitate the transmission of waterborne pathogens by changing social conditions (overcrowding among displaced populations, interruption of public health services), imposing physical challenges to infrastructure (sewerage overflow, reduced capacity to treat drinking water), and altering fate and transport of pathogens (transport into waterways from overland flow, resuspension of settled contaminants) during and after flood conditions. Hydrological and hydrodynamic models are capable of generating quantitative characterizations of microbiological risks associated with flooding, while accounting for these diverse and at times competing physical and biological processes. Despite a few applications of such models to the quantification of microbiological risks associated with floods, there exists limited guidance as to the relative capabilities, and limitations, of existing modeling platforms when used for this purpose. Here, we review 17 commonly used flood and water quality modeling tools that have demonstrated or implicit capabilities of mechanistically representing and quantifying microbial risk during flood conditions. We compare models with respect to their capabilities of generating outputs that describe physical and microbial conditions during floods, such as concentration or load of non-cohesive sediments or pathogens, and the dynamics of high flow conditions. Recommendations are presented for the application of specific modeling tools for assessing particular flood-related microbial risks, and model improvements are suggested that may better characterize key microbial risks during flood events. The state of current tools are assessed in the context of a changing climate where the frequency, intensity and duration of flooding are shifting in some areas.

Bayesian Forecasting Tool to Predict the Need for Antidote in Acute Acetaminophen Overdose.

PubMed

Desrochers, Julie; Wojciechowski, Jessica; Klein-Schwartz, Wendy; Gobburu, Jogarao V S; Gopalakrishnan, Mathangi

2017-08-01

Acetaminophen (APAP) overdose is the leading cause of acute liver injury in the United States. Patients with elevated plasma acetaminophen concentrations (PACs) require hepatoprotective treatment with N-acetylcysteine (NAC). These patients have been primarily risk-stratified using the Rumack-Matthew nomogram. Previous studies of acute APAP overdoses found that the nomogram failed to accurately predict the need for the antidote. The objectives of this study were to develop a population pharmacokinetic (PK) model for APAP following acute overdose and evaluate the utility of population PK model-based Bayesian forecasting in NAC administration decisions. Limited APAP concentrations from a retrospective cohort of acute overdosed subjects from the Maryland Poison Center were used to develop the population PK model and to investigate the effect of type of APAP products and other prognostic factors. The externally validated population PK model was used a prior for Bayesian forecasting to predict the individual PK profile when one or two observed PACs were available. The utility of Bayesian forecasted APAP concentration-time profiles inferred from one (first) or two (first and second) PAC observations were also tested in their ability to predict the observed NAC decisions. A one-compartment model with first-order absorption and elimination adequately described the data with single activated charcoal and APAP products as significant covariates on absorption and bioavailability. The Bayesian forecasted individual concentration-time profiles had acceptable bias (6.2% and 9.8%) and accuracy (40.5% and 41.9%) when either one or two PACs were considered, respectively. The sensitivity and negative predictive value of the Bayesian forecasted NAC decisions using one PAC were 84% and 92.6%, respectively. The population PK analysis provided a platform for acceptably predicting an individual's concentration-time profile following acute APAP overdose with at least one PAC, and the individual's covariate profile, and can potentially be used for making early NAC administration decisions. © 2017 Pharmacotherapy Publications, Inc.

Application of multivariate probabilistic (Bayesian) networks to substance use disorder risk stratification and cost estimation.

PubMed

Weinstein, Lawrence; Radano, Todd A; Jack, Timothy; Kalina, Philip; Eberhardt, John S

2009-09-16

This paper explores the use of machine learning and Bayesian classification models to develop broadly applicable risk stratification models to guide disease management of health plan enrollees with substance use disorder (SUD). While the high costs and morbidities associated with SUD are understood by payers, who manage it through utilization review, acute interventions, coverage and cost limitations, and disease management, the literature shows mixed results for these modalities in improving patient outcomes and controlling cost. Our objective is to evaluate the potential of data mining methods to identify novel risk factors for chronic disease and stratification of enrollee utilization, which can be used to develop new methods for targeting disease management services to maximize benefits to both enrollees and payers. For our evaluation, we used DecisionQ machine learning algorithms to build Bayesian network models of a representative sample of data licensed from Thomson-Reuters' MarketScan consisting of 185,322 enrollees with three full-year claim records. Data sets were prepared, and a stepwise learning process was used to train a series of Bayesian belief networks (BBNs). The BBNs were validated using a 10 percent holdout set. The networks were highly predictive, with the risk-stratification BBNs producing area under the curve (AUC) for SUD positive of 0.948 (95 percent confidence interval [CI], 0.944-0.951) and 0.736 (95 percent CI, 0.721-0.752), respectively, and SUD negative of 0.951 (95 percent CI, 0.947-0.954) and 0.738 (95 percent CI, 0.727-0.750), respectively. The cost estimation models produced area under the curve ranging from 0.72 (95 percent CI, 0.708-0.731) to 0.961 (95 percent CI, 0.95-0.971). We were able to successfully model a large, heterogeneous population of commercial enrollees, applying state-of-the-art machine learning technology to develop complex and accurate multivariate models that support near-real-time scoring of novel payer populations based on historic claims and diagnostic data. Initial validation results indicate that we can stratify enrollees with SUD diagnoses into different cost categories with a high degree of sensitivity and specificity, and the most challenging issue becomes one of policy. Due to the social stigma associated with the disease and ethical issues pertaining to access to care and individual versus societal benefit, a thoughtful dialogue needs to occur about the appropriate way to implement these technologies.

RISK AND RISK ASSESSMENT IN WATER-BASED RECREATION

EPA Science Inventory

The great number of individuals using recreational water resources presents a challenge with regard to protecting the health of these recreationists. Risk assessment provides a framework for characterizing the risk associated with exposure to microbial hazards and for managing r...

MICROBIOLOGICAL RISK ASSESSMENT FOR LAND APPLICATION OF MUNICIPAL SLUDGE

EPA Science Inventory

Each major option for the disposal/reuse of municipal sludges poses potential risks to human health or the environment because of the microbial contaminants in sludge. Therefore, risk assessment methodology appropriate for pathogen risk evaluation for land application and distrib...

Disposable contact lens use as a risk factor for microbial keratitis

PubMed Central

Radford, C.; Minassian, D.; Dart, J.

1998-01-01

AIMS—A case-control study was performed to evaluate soft contact lens (SCL) wear modality as a risk factor for microbial keratitis.
METHODS—Contact lens wearers presenting as new patients to Moorfields Eye Hospital accident and emergency department during a 12 month period completed a self administered questionnaire detailing demographic data and contact lens use habits. Cases were patients with a clinical diagnosis of SCL related microbial keratitis. Controls were SCL users attending with disorders unrelated to contact lens wear. Odds ratios (estimates of relative risks) and 95% confidence limits (CL) were calculated through multivariable logistic regression analysis.
RESULTS—There were 89 cases and 566 controls. A substantially increased risk with 1-4 weekly disposable SCL compared with non-disposable SCL was identified among both daily wear (DW) (odds ratio =3.51, 95% CL 1.60-7.66, p=0.002) and extended wear (odds ratio 4.76, 95% CL 1.52-14.87, p=0.007) users after adjustment for demographic, lens use and hygiene variables. Other significant factors among DW users were "occasional" overnight use, use of chlorine based (as opposed to other chemical) systems in combination with poor storage case hygiene, and irregular disinfection.
CONCLUSION—Properties of some disposable SCL may be partly responsible for these excess risks. It is also possible, however, that this finding is largely a reflection of widespread complacency among patients and practitioners with respect to disposable SCL fitting and use.

 Keywords: microbial keratitis; disposable contact lenses PMID:9924331

Estimating relative risks in multicenter studies with a small number of centers - which methods to use? A simulation study.

PubMed

Pedroza, Claudia; Truong, Van Thi Thanh

2017-11-02

Analyses of multicenter studies often need to account for center clustering to ensure valid inference. For binary outcomes, it is particularly challenging to properly adjust for center when the number of centers or total sample size is small, or when there are few events per center. Our objective was to evaluate the performance of generalized estimating equation (GEE) log-binomial and Poisson models, generalized linear mixed models (GLMMs) assuming binomial and Poisson distributions, and a Bayesian binomial GLMM to account for center effect in these scenarios. We conducted a simulation study with few centers (≤30) and 50 or fewer subjects per center, using both a randomized controlled trial and an observational study design to estimate relative risk. We compared the GEE and GLMM models with a log-binomial model without adjustment for clustering in terms of bias, root mean square error (RMSE), and coverage. For the Bayesian GLMM, we used informative neutral priors that are skeptical of large treatment effects that are almost never observed in studies of medical interventions. All frequentist methods exhibited little bias, and the RMSE was very similar across the models. The binomial GLMM had poor convergence rates, ranging from 27% to 85%, but performed well otherwise. The results show that both GEE models need to use small sample corrections for robust SEs to achieve proper coverage of 95% CIs. The Bayesian GLMM had similar convergence rates but resulted in slightly more biased estimates for the smallest sample sizes. However, it had the smallest RMSE and good coverage across all scenarios. These results were very similar for both study designs. For the analyses of multicenter studies with a binary outcome and few centers, we recommend adjustment for center with either a GEE log-binomial or Poisson model with appropriate small sample corrections or a Bayesian binomial GLMM with informative priors.

Bayesian Dose-Response Modeling in Sparse Data

NASA Astrophysics Data System (ADS)

Kim, Steven B.

This book discusses Bayesian dose-response modeling in small samples applied to two different settings. The first setting is early phase clinical trials, and the second setting is toxicology studies in cancer risk assessment. In early phase clinical trials, experimental units are humans who are actual patients. Prior to a clinical trial, opinions from multiple subject area experts are generally more informative than the opinion of a single expert, but we may face a dilemma when they have disagreeing prior opinions. In this regard, we consider compromising the disagreement and compare two different approaches for making a decision. In addition to combining multiple opinions, we also address balancing two levels of ethics in early phase clinical trials. The first level is individual-level ethics which reflects the perspective of trial participants. The second level is population-level ethics which reflects the perspective of future patients. We extensively compare two existing statistical methods which focus on each perspective and propose a new method which balances the two conflicting perspectives. In toxicology studies, experimental units are living animals. Here we focus on a potential non-monotonic dose-response relationship which is known as hormesis. Briefly, hormesis is a phenomenon which can be characterized by a beneficial effect at low doses and a harmful effect at high doses. In cancer risk assessments, the estimation of a parameter, which is known as a benchmark dose, can be highly sensitive to a class of assumptions, monotonicity or hormesis. In this regard, we propose a robust approach which considers both monotonicity and hormesis as a possibility. In addition, We discuss statistical hypothesis testing for hormesis and consider various experimental designs for detecting hormesis based on Bayesian decision theory. Past experiments have not been optimally designed for testing for hormesis, and some Bayesian optimal designs may not be optimal under a wrong parametric assumption. In this regard, we consider a robust experimental design which does not require any parametric assumption.

Wound Bioburden and Infection-Related Complications in Diabetic Foot Ulcers

PubMed Central

Gardner, Sue E.; Frantz, Rita A.

2013-01-01

The identification and diagnosis of diabetic foot ulcer (DFU) infections remains a complex problem. Because inflammatory responses to microbial invasion may be diminished in persons with diabetes, clinical signs of infection are often absent in persons with DFUs when infection is limited to localized tissue. In the absence of these clinical signs, microbial load is believed to be the best indicator of infection. Some researchers, however, believe microbial load to be insignificant and type of organism growing in the ulcer to be most important. Previous studies on the microbiology of DFUs have not provided enough evidence to determine the microbiological parameters of importance. Infection-related complications of DFUs include wound deterioration, osteomyelitis, and amputation. Risk factors for amputation include age, peripheral vascular disease, low transcutaneous oxygen, smoking, and poor glycemic control. These risk factors are best measured directly with physiological measures of arterial perfusion, glycemic control, sensory neuropathy, plantar pressures, and activity level and by controlling off-loading. DFU bioburden has not been examined as a risk factor for infection-related complications. To address the relationship between wound bioburden and the development of infection-related complications in DFUs, tightly controlled prospective studies based on clearly defined, valid measures of wound bioburden and wound outcomes are needed. This article reviews the literature and proposes a model of hypothesized relationships between wound bioburden—including microbial load, microbial diversity, and pathogenicity of organisms—and the development of infection-related complications. PMID:18647759

Order-Constrained Reference Priors with Implications for Bayesian Isotonic Regression, Analysis of Covariance and Spatial Models

NASA Astrophysics Data System (ADS)

Gong, Maozhen

Selecting an appropriate prior distribution is a fundamental issue in Bayesian Statistics. In this dissertation, under the framework provided by Berger and Bernardo, I derive the reference priors for several models which include: Analysis of Variance (ANOVA)/Analysis of Covariance (ANCOVA) models with a categorical variable under common ordering constraints, the conditionally autoregressive (CAR) models and the simultaneous autoregressive (SAR) models with a spatial autoregression parameter rho considered. The performances of reference priors for ANOVA/ANCOVA models are evaluated by simulation studies with comparisons to Jeffreys' prior and Least Squares Estimation (LSE). The priors are then illustrated in a Bayesian model of the "Risk of Type 2 Diabetes in New Mexico" data, where the relationship between the type 2 diabetes risk (through Hemoglobin A1c) and different smoking levels is investigated. In both simulation studies and real data set modeling, the reference priors that incorporate internal order information show good performances and can be used as default priors. The reference priors for the CAR and SAR models are also illustrated in the "1999 SAT State Average Verbal Scores" data with a comparison to a Uniform prior distribution. Due to the complexity of the reference priors for both CAR and SAR models, only a portion (12 states in the Midwest) of the original data set is considered. The reference priors can give a different marginal posterior distribution compared to a Uniform prior, which provides an alternative for prior specifications for areal data in Spatial statistics.

Transport and fate of microbial pathogens in agricultural settings

USDA-ARS?s Scientific Manuscript database

An understanding of the transport and survival of microbial pathogens (pathogens hereafter) in agricultural settings is needed to assess the risk of pathogen contamination to water and food resources, and to develop control strategies and treatment options. However, many knowledge gaps still remain ...

40 CFR 158.2173 - Experimental use permit microbial pesticides toxicology data requirements table.

Code of Federal Regulations, 2011 CFR

2011-07-01

... combination of inert ingredients is not likely to pose any significant human health risks. Where appropriate... pesticides toxicology data requirements table. 158.2173 Section 158.2173 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS DATA REQUIREMENTS FOR PESTICIDES Microbial...

40 CFR 158.2173 - Experimental use permit microbial pesticides toxicology data requirements table.

Code of Federal Regulations, 2013 CFR

2013-07-01

... combination of inert ingredients is not likely to pose any significant human health risks. Where appropriate... pesticides toxicology data requirements table. 158.2173 Section 158.2173 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS DATA REQUIREMENTS FOR PESTICIDES Microbial...

40 CFR 158.2173 - Experimental use permit microbial pesticides toxicology data requirements table.

Code of Federal Regulations, 2014 CFR

2014-07-01

... combination of inert ingredients is not likely to pose any significant human health risks. Where appropriate... pesticides toxicology data requirements table. 158.2173 Section 158.2173 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS DATA REQUIREMENTS FOR PESTICIDES Microbial...

40 CFR 158.2173 - Experimental use permit microbial pesticides toxicology data requirements table.

Code of Federal Regulations, 2010 CFR

2010-07-01

... combination of inert ingredients is not likely to pose any significant human health risks. Where appropriate... pesticides toxicology data requirements table. 158.2173 Section 158.2173 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS DATA REQUIREMENTS FOR PESTICIDES Microbial...

40 CFR 158.2173 - Experimental use permit microbial pesticides toxicology data requirements table.

Code of Federal Regulations, 2012 CFR

2012-07-01

... combination of inert ingredients is not likely to pose any significant human health risks. Where appropriate... pesticides toxicology data requirements table. 158.2173 Section 158.2173 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS DATA REQUIREMENTS FOR PESTICIDES Microbial...

Antibiotic effects on microbial community characteristics in soils under conservation management practices

USDA-ARS?s Scientific Manuscript database

Veterinary antibiotics (VAs) administered to livestock are introduced to agroecosystems via land application of manure, posing a potential human and environmental health risk. These Antibiotics may adversely affect soil microbial communities. The objectives of this research were to investigate poten...

Mapping child maltreatment risk: a 12-year spatio-temporal analysis of neighborhood influences.

PubMed

Gracia, Enrique; López-Quílez, Antonio; Marco, Miriam; Lila, Marisol

2017-10-18

'Place' matters in understanding prevalence variations and inequalities in child maltreatment risk. However, most studies examining ecological variations in child maltreatment risk fail to take into account the implications of the spatial and temporal dimensions of neighborhoods. In this study, we conduct a high-resolution small-area study to analyze the influence of neighborhood characteristics on the spatio-temporal epidemiology of child maltreatment risk. We conducted a 12-year (2004-2015) small-area Bayesian spatio-temporal epidemiological study with all families with child maltreatment protection measures in the city of Valencia, Spain. As neighborhood units, we used 552 census block groups. Cases were geocoded using the family address. Neighborhood-level characteristics analyzed included three indicators of neighborhood disadvantage-neighborhood economic status, neighborhood education level, and levels of policing activity-, immigrant concentration, and residential instability. Bayesian spatio-temporal modelling and disease mapping methods were used to provide area-specific risk estimations. Results from a spatio-temporal autoregressive model showed that neighborhoods with low levels of economic and educational status, with high levels of policing activity, and high immigrant concentration had higher levels of substantiated child maltreatment risk. Disease mapping methods were used to analyze areas of excess risk. Results showed chronic spatial patterns of high child maltreatment risk during the years analyzed, as well as stability over time in areas of low risk. Areas with increased or decreased child maltreatment risk over the years were also observed. A spatio-temporal epidemiological approach to study the geographical patterns, trends over time, and the contextual determinants of child maltreatment risk can provide a useful method to inform policy and action. This method can offer a more accurate description of the problem, and help to inform more localized prevention and intervention strategies. This new approach can also contribute to an improved epidemiological surveillance system to detect ecological variations in risk, and to assess the effectiveness of the initiatives to reduce this risk.

Reevaluation of health risk benchmark for sustainable water practice through risk analysis of rooftop-harvested rainwater.

PubMed

Lim, Keah-Ying; Jiang, Sunny C

2013-12-15

Health risk concerns associated with household use of rooftop-harvested rainwater (HRW) constitute one of the main impediments to exploit the benefits of rainwater harvesting in the United States. However, the benchmark based on the U.S. EPA acceptable annual infection risk level of ≤1 case per 10,000 persons per year (≤10(-4) pppy) developed to aid drinking water regulations may be unnecessarily stringent for sustainable water practice. In this study, we challenge the current risk benchmark by quantifying the potential microbial risk associated with consumption of HRW-irrigated home produce and comparing it against the current risk benchmark. Microbial pathogen data for HRW and exposure rates reported in literature are applied to assess the potential microbial risk posed to household consumers of their homegrown produce. A Quantitative Microbial Risk Assessment (QMRA) model based on worst-case scenario (e.g. overhead irrigation, no pathogen inactivation) is applied to three crops that are most popular among home gardeners (lettuce, cucumbers, and tomatoes) and commonly consumed raw. The infection risks of household consumers attributed to consumption of these home produce vary with the type of produce. The lettuce presents the highest risk, which is followed by tomato and cucumber, respectively. Results show that the 95th percentile values of infection risk per intake event of home produce are one to three orders of magnitude (10(-7) to 10(-5)) lower than U.S. EPA risk benchmark (≤10(-4) pppy). However, annual infection risks under the same scenario (multiple intake events in a year) are very likely to exceed the risk benchmark by one order of magnitude in some cases. Estimated 95th percentile values of the annual risk are in the 10(-4) to 10(-3) pppy range, which are still lower than the 10(-3) to 10(-1) pppy risk range of reclaimed water irrigated produce estimated in comparable studies. We further discuss the desirability of HRW for irrigating home produce based on the relative risk of HRW to reclaimed wastewater for irrigation of food crops. The appropriateness of the ≤10(-4) pppy risk benchmark for assessing safety level of HRW-irrigated fresh produce is questioned by considering the assumptions made for the QMRA model. Consequently, the need of an updated approach to assess appropriateness of sustainable water practice for making guidelines and policies is proposed. Copyright © 2013 Elsevier Ltd. All rights reserved.

Description of cervical cancer mortality in Belgium using Bayesian age-period-cohort models

PubMed Central

2009-01-01

Objective To correct cervical cancer mortality rates for death cause certification problems in Belgium and to describe the corrected trends (1954-1997) using Bayesian models. Method Cervical cancer (cervix uteri (CVX), corpus uteri (CRP), not otherwise specified (NOS) uterus cancer and other very rare uterus cancer (OTH) mortality data were extracted from the WHO mortality database together with population data for Belgium and the Netherlands. Different ICD (International Classification of Diseases) were used over time for death cause certification. In the Netherlands, the proportion of not-otherwise specified uterine cancer deaths was small over large periods and therefore internal reallocation could be used to estimate the corrected rates cervical cancer mortality. In Belgium, the proportion of improperly defined uterus deaths was high. Therefore, the age-specific proportions of uterus cancer deaths that are probably of cervical origin for the Netherlands was applied to Belgian uterus cancer deaths to estimate the corrected number of cervix cancer deaths (corCVX). A Bayesian loglinear Poisson-regression model was performed to disentangle the separate effects of age, period and cohort. Results The corrected age standardized mortality rate (ASMR) decreased regularly from 9.2/100 000 in the mid 1950s to 2.5/100,000 in the late 1990s. Inclusion of age, period and cohort into the models were required to obtain an adequate fit. Cervical cancer mortality increases with age, declines over calendar period and varied irregularly by cohort. Conclusion Mortality increased with ageing and declined over time in most age-groups, but varied irregularly by birth cohort. In global, with some discrete exceptions, mortality decreased for successive generations up to the cohorts born in the 1930s. This decline stopped for cohorts born in the 1940s and thereafter. For the youngest cohorts, even a tendency of increasing risk of dying from cervical cancer could be observed, reflecting increased exposure to risk factors. The fact that this increase was limited for the youngest cohorts could be explained as an effect of screening. Bayesian modeling provided similar results compared to previously used classical Poisson models. However, Bayesian models are more robust for estimating rates when data are sparse (youngest age groups, most recent cohorts) and can be used to for predicting future trends.

PEER Transportation Research Program | PEER Transportation Research Program

Science.gov Websites

methodologies, integrating fundamental knowledge, enabling technologies, and systems. We further expect that the Bayesian Framework for Performance Assessment and Risk Management of Transportation Systems subject to Earthquakes Directivity Modeling for NGA West2 Ground Motion Studies for Transportation Systems Performance

RISK ASSESSMENT FOR CRYPTOSPORIDIUM: A HIERARCHICAL BAYESIAN ANALYSIS OF HUMAN DOSE-RESPONSE DATA. (R828035)

EPA Science Inventory

Three dose_¯response studies were conducted with healthy volunteers using different Cryptosporidium parvum isolates (IOWA, TAMU, and UCP). The study data were previously analyzed for median infectious dose (ID₅₀) using a simple cumulative perce...

RISK ASSESSMENT FOR CRYPTOSPORIDIUM: A HIERARCHICAL BAYESIAN ANALYSIS OF HUMAN DOSE-RESPONSE DATA. (R829180)

EPA Science Inventory

Three dose–response studies were conducted with healthy volunteers using different Cryptosporidium parvum isolates (IOWA, TAMU, and UCP). The study data were previously analyzed for median infectious dose (ID₅₀) using a simple cumulative percent endpoi...

Empirically-based modeling and mapping to consider the co-occurrence of ecological receptors and stressors

EPA Science Inventory

Part of the ecological risk assessment process involves examining the potential for environmental stressors and ecological receptors to co-occur across a landscape. In this study, we introduce a Bayesian joint modeling framework for use in evaluating and mapping the co-occurrence...

Towards Link Characterization from Content

DTIC Science & Technology

2008-01-01

S.D. Walter and L.M. Irwig, “Estimation of Test Error Rates, Disease Prevalence , and Relative Risk from Misclas- sified Data: A Review,” Journal of...Clinical Epidemiology, vol. 41, pp. 923–937, 1988. [10] L. Joseph, T. Gyorkos, and L. Coupal, “Bayesian estimation of disease prevalence and the

Microbiological risk from minimally processed packaged salads in the Dutch food chain.

PubMed

Pielaat, Annemarie; van Leusden, Frans M; Wijnands, Lucas M

2014-03-01

The objective of this study was to evaluate the microbial hazard associated with the consumption of mixed salads produced under standard conditions. The presence of Salmonella, Campylobacter spp., and Escherichia coli O157 in the Dutch production chain of mixed salads was determined. Microbial prevalence and concentration data from a microbiological surveillance study were used as inputs for the quantitative microbial risk assessment. Chain logistics, production figures, and consumption patterns were combined with the survey data for the risk assessment chain approach. The results of the sample analysis were used to track events from contamination through human illness. Wide 95% confidence intervals around the mean were found for estimated annual numbers of illnesses resulting from the consumption of mixed salads contaminated with Salmonella Typhimurium DT104 (0 to 10,300 cases), Campylobacter spp. (0 to 92,000 cases), or E. coli (0 to 800 cases). The main sources of uncertainty are the lack of decontamination data (i.e., produce washing during processing) and an appropriate dose-response relationship.

Bayesian Estimation of Combined Accuracy for Tests with Verification Bias

PubMed Central

Broemeling, Lyle D.

2011-01-01

This presentation will emphasize the estimation of the combined accuracy of two or more tests when verification bias is present. Verification bias occurs when some of the subjects are not subject to the gold standard. The approach is Bayesian where the estimation of test accuracy is based on the posterior distribution of the relevant parameter. Accuracy of two combined binary tests is estimated employing either “believe the positive” or “believe the negative” rule, then the true and false positive fractions for each rule are computed for two tests. In order to perform the analysis, the missing at random assumption is imposed, and an interesting example is provided by estimating the combined accuracy of CT and MRI to diagnose lung cancer. The Bayesian approach is extended to two ordinal tests when verification bias is present, and the accuracy of the combined tests is based on the ROC area of the risk function. An example involving mammography with two readers with extreme verification bias illustrates the estimation of the combined test accuracy for ordinal tests. PMID:26859487

Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil.

PubMed

Li, Xuewen; Xie, Yunfeng; Li, Lianfa; Yang, Xunfeng; Wang, Ning; Wang, Jinfeng

2015-11-01

Prediction of antibiotic pollution and its consequences is difficult, due to the uncertainties and complexities associated with multiple related factors. This article employed domain knowledge and spatial data to construct a Bayesian network (BN) model to assess fluoroquinolone antibiotic (FQs) pollution in the soil of an intensive vegetable cultivation area. The results show: (1) The relationships between FQs pollution and contributory factors: Three factors (cultivation methods, crop rotations, and chicken manure types) were consistently identified as predictors in the topological structures of three FQs, indicating their importance in FQs pollution; deduced with domain knowledge, the cultivation methods are determined by the crop rotations, which require different nutrients (derived from the manure) according to different plant biomass. (2) The performance of BN model: The integrative robust Bayesian network model achieved the highest detection probability (pd) of high-risk and receiver operating characteristic (ROC) area, since it incorporates domain knowledge and model uncertainty. Our encouraging findings have implications for the use of BN as a robust approach to assessment of FQs pollution and for informing decisions on appropriate remedial measures.

Development and Evaluation of a Quantitative PCR Assay Targeting Sandhill Crane (Grus canadensis) Fecal Pollution

PubMed Central

Ryu, Hodon; Lu, Jingrang; Vogel, Jason; Elk, Michael; Chávez-Ramírez, Felipe; Ashbolt, Nicholas

2012-01-01

While the microbial water quality in the Platte River is seasonally impacted by excreta from migrating cranes, there are no methods available to study crane fecal contamination. Here we characterized microbial populations in crane feces using phylogenetic analysis of 16S rRNA gene fecal clone libraries. Using these sequences, a novel crane quantitative PCR (Crane1) assay was developed, and its applicability as a microbial source tracking (MST) assay was evaluated by determining its host specificity and detection ability in environmental waters. Bacteria from crane excreta were dominated by bacilli and proteobacteria, with a notable paucity of sequences homologous to Bacteroidetes and Clostridia. The Crane1 marker targeted a dominant clade of unclassified Lactobacillales sequences closely related to Catellicoccus marimammalium. The host distribution of the Crane1 marker was relatively high, being positive for 69% (66/96) of the crane excreta samples tested. The assay also showed high host specificity, with 95% of the nontarget fecal samples (i.e., n = 553; 20 different free-range hosts) being negative. Of the presumed crane-impacted water samples (n = 16), 88% were positive for the Crane1 assay, whereas none of the water samples not impacted by cranes were positive (n = 165). Bayesian statistical models of the Crane1 MST marker demonstrated high confidence in detecting true-positive signals and a low probability of false-negative signals from environmental water samples. Altogether, these data suggest that the newly developed marker could be used in environmental monitoring studies to study crane fecal pollution dynamics. PMID:22492437

SURVEY OF CULTURABLE AIRBORNE BACTERIA AT FOUR DIVERSE LOCATIONS IN OREGON: URBAN, RURAL, FOREST, AND COASTAL

EPA Science Inventory

To determine the risks of microbial air pollution from microorganisms used for pesticides and bioremediation, or emanating from composting, fermentation tanks, or other agricultural and urban sources, airborne microbial levels must be evaluated. This study surveyed the atmospheri...

Applications for predictive microbiology to food packaging

USDA-ARS?s Scientific Manuscript database

Predictive microbiology has been used for several years in the food industry to predict microbial growth, inactivation and survival. Predictive models provide a useful tool in risk assessment, HACCP set-up and GMP for the food industry to enhance microbial food safety. This report introduces the c...

Integrated network modelling for identifying microbial mechanisms of particulate organic carbon accumulation in coastal marine systems

NASA Astrophysics Data System (ADS)

McDonald, Karlie; Turk, Valentina; Mozetič, Patricija; Tinta, Tinkara; Malfatti, Francesca; Hannah, David; Krause, Stefan

2016-04-01

Accumulation of particulate organic carbon (POC) has the potential to change the structure and function of marine ecosystems. High abidance of POC can develop into aggregates, known as marine snow or mucus aggregates that can impair essential marine ecosystem functioning and services. Currently marine POC formation, accumulation and sedimentation processes are being explored as potential pathways to remove CO2 from the atmosphere by CO2 sequestration via fixation into biomass by phytoplankton. However, the current ability of scientists, environmental managers and regulators to analyse and predict high POC concentrations is restricted by the limited understanding of the dynamic nature of the microbial mechanisms regulating POC accumulation events in marine environments. We present a proof of concept study that applies a novel Bayesian Networks (BN) approach to integrate relevant biological and physical-chemical variables across spatial and temporal scales in order to identify the interactions of the main contributing microbial mechanisms regulating POC accumulation in the northern Adriatic Sea. Where previous models have characterised only the POC formed, the BN approach provides a probabilistic framework for predicting the occurrence of POC accumulation by linking biotic factors with prevailing environmental conditions. In this paper the BN was used to test three scenarios (diatom, nanoflagellate, and dinoflagellate blooms). The scenarios predicted diatom blooms to produce high chlorophyll a at the water surface while nanoflagellate blooms were predicted to occur at lower depths (> 6m) in the water column and produce lower chlorophyll a concentrations. A sensitivity analysis identified the variables with the greatest influence on POC accumulation being the enzymes protease and alkaline phosphatase, which highlights the importance of microbial community interactions. The developed proof of concept BN model allows for the first time to quantify the impacts of biological, chemical and physical parameters influencing microbial community interactions mechanisms that regulate POC accumulation in marine environments. The dynamic modular nature of the developed BN will allow successive updating and improvement of the model structure as new data are emerging, thus, providing a powerful interactive framework for the investigation, prediction and mitigation of future POC accumulation events.

Microbial Diversity Aboard Spacecraft: Evaluation of the International Space Station

NASA Technical Reports Server (NTRS)

Castro, Victoria A.; Thrasher, Adrianna N.; Healy, Mimi; Ott, C. Mark; Pierson, Duane L.

2003-01-01

An evaluation of the microbial flora from air, water, and surface samples provided a baseline of microbial diversity onboard the International Space Station (ISS) to gain insight into bacterial and fungal contamination during the initial stages of construction and habitation. Using 16S genetic sequencing and rep-PeR, 63 bacterial strains were isolated for identification and fingerprinted for microbial tracking. The use of these molecular tools allowed for the identification of bacteria not previously identified using automated biochemical analysis and provided a clear indication of the source of several ISS contaminants. Fungal and bacterial data acquired during monitoring do not suggest there is a current microbial hazard to the spacecraft, nor does any trend indicate a potential health risk. Previous spacecraft environmental analysis indicated that microbial contamination will increase with time and require continued surveillance.

What Does Tympanostomy Tube Placement in Children Teach Us About the Association Between Atopic Conditions and Otitis Media?

PubMed Central

Juhn, Young J.; Wi, Chung-Il

2014-01-01

Otitis media is the most common infection second only to viral upper respiratory infection in the outpatient setting. Tympanostomy tube insertion (TTI) is the most common ambulatory surgical procedure in the United States. While many risk factors for otitis media have been identified, atopic conditions have been under-recognized as risk factors for recurrent and persistent otitis media. Given that asthma and other atopic conditions are the most common chronic conditions during childhood, it is worth examining the association between atopic conditions and risk of otitis media, which can provide insight into how atopic conditions influence the risk of microbial infections. This paper focuses its discussion on otitis media, however it is important that the association between atopic conditions and risk of otitis media be interpreted in the context of the association of atopic conditions with increased risks of various microbial infections. PMID:24816652

Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models.

PubMed

Scholte, Ronaldo G C; Schur, Nadine; Bavia, Maria E; Carvalho, Edgar M; Chammartin, Frédérique; Utzinger, Jürg; Vounatsou, Penelope

2013-11-01

Soil-transmitted helminths (Ascaris lumbricoides, Trichuris trichiura and hookworm) negatively impact the health and wellbeing of hundreds of millions of people, particularly in tropical and subtropical countries, including Brazil. Reliable maps of the spatial distribution and estimates of the number of infected people are required for the control and eventual elimination of soil-transmitted helminthiasis. We used advanced Bayesian geostatistical modelling, coupled with geographical information systems and remote sensing to visualize the distribution of the three soil-transmitted helminth species in Brazil. Remotely sensed climatic and environmental data, along with socioeconomic variables from readily available databases were employed as predictors. Our models provided mean prevalence estimates for A. lumbricoides, T. trichiura and hookworm of 15.6%, 10.1% and 2.5%, respectively. By considering infection risk and population numbers at the unit of the municipality, we estimate that 29.7 million Brazilians are infected with A. lumbricoides, 19.2 million with T. trichiura and 4.7 million with hookworm. Our model-based maps identified important risk factors related to the transmission of soiltransmitted helminths and confirm that environmental variables are closely associated with indices of poverty. Our smoothed risk maps, including uncertainty, highlight areas where soil-transmitted helminthiasis control interventions are most urgently required, namely in the North and along most of the coastal areas of Brazil. We believe that our predictive risk maps are useful for disease control managers for prioritising control interventions and for providing a tool for more efficient surveillance-response mechanisms.

Natural Hazards and Supply Chain Disruptions

NASA Astrophysics Data System (ADS)

Haraguchi, M.

2016-12-01

Natural hazards distress the global economy through disruptions in supply chain networks. Moreover, despite increasing investment to infrastructure for disaster risk management, economic damages and losses caused by natural hazards are increasing. Manufacturing companies today have reduced inventories and streamlined logistics in order to maximize economic competitiveness. As a result, today's supply chains are profoundly susceptible to systemic risks, which are the risk of collapse of an entire network caused by a few node of the network. For instance, the prolonged floods in Thailand in 2011 caused supply chain disruptions in their primary industries, i.e. electronic and automotive industries, harming not only the Thai economy but also the global economy. Similar problems occurred after the Great East Japan Earthquake and Tsunami in 2011, the Mississippi River floods and droughts during 2011 - 2013, and the Earthquake in Kumamoto Japan in 2016. This study attempts to discover what kind of effective measures are available for private companies to manage supply chain disruptions caused by floods. It also proposes a method to estimate potential risks using a Bayesian network. The study uses a Bayesian network to create synthetic networks that include variables associated with the magnitude and duration of floods, major components of supply chains such as logistics, multiple layers of suppliers, warehouses, and consumer markets. Considering situations across different times, our study shows desirable data requirements for the analysis and effective measures to improve Value at Risk (VaR) for private enterprises and supply chains.

Psychosocial stress factors, including the relationship with the coach, and their influence on acute and overuse injury risk in elite female football players.

PubMed

Pensgaard, Anne Marte; Ivarsson, Andreas; Nilstad, Agnethe; Solstad, Bård Erlend; Steffen, Kathrin

2018-01-01

The relationship between specific types of stressors (eg, teammates, coach) and acute versus overuse injuries is not well understood. To examine the roles of different types of stressors as well as the effect of motivational climate on the occurrence of acute and overuse injuries. Players in the Norwegian elite female football league (n=193 players from 12 teams) participated in baseline screening tests prior to the 2009 competitive football season. As part of the screening, we included the Life Event Survey for Collegiate Athletes and the Perceived Motivational Climate in Sport Questionnaire (Norwegian short version). Acute and overuse time-loss injuries and exposure to training and matches were recorded prospectively in the football season using weekly text messaging. Data were analysed with Bayesian logistic regression analyses. Using Bayesian logistic regression analyses, we showed that perceived negative life event stress from teammates was associated with an increased risk of acute injuries (OR=1.23, 95% credibility interval (1.01 to 1.48)). There was a credible positive association between perceived negative life event stress from the coach and the risk of overuse injuries (OR=1.21, 95% credibility interval (1.01 to 1.45)). Players who report teammates as a source of stress have a greater risk of sustaining an acute injury, while players reporting the coach as a source of stress are at greater risk of sustaining an overuse injury. Motivational climate did not relate to increased injury occurrence.

Assessment of occupational safety risks in Floridian solid waste systems using Bayesian analysis.

PubMed

Bastani, Mehrad; Celik, Nurcin

2015-10-01

Safety risks embedded within solid waste management systems continue to be a significant issue and are prevalent at every step in the solid waste management process. To recognise and address these occupational hazards, it is necessary to discover the potential safety concerns that cause them, as well as their direct and/or indirect impacts on the different types of solid waste workers. In this research, our goal is to statistically assess occupational safety risks to solid waste workers in the state of Florida. Here, we first review the related standard industrial codes to major solid waste management methods including recycling, incineration, landfilling, and composting. Then, a quantitative assessment of major risks is conducted based on the data collected using a Bayesian data analysis and predictive methods. The risks estimated in this study for the period of 2005-2012 are then compared with historical statistics (1993-1997) from previous assessment studies. The results have shown that the injury rates among refuse collectors in both musculoskeletal and dermal injuries have decreased from 88 and 15 to 16 and three injuries per 1000 workers, respectively. However, a contrasting trend is observed for the injury rates among recycling workers, for whom musculoskeletal and dermal injuries have increased from 13 and four injuries to 14 and six injuries per 1000 workers, respectively. Lastly, a linear regression model has been proposed to identify major elements of the high number of musculoskeletal and dermal injuries. © The Author(s) 2015.

A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data

PubMed Central

Vehtari, Aki; Mäkinen, Ville-Petteri; Soininen, Pasi; Ingman, Petri; Mäkelä, Sanna M; Savolainen, Markku J; Hannuksela, Minna L; Kaski, Kimmo; Ala-Korpela, Mika

2007-01-01

Background A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk assessment and diagnostics. Here we focus on clinically relevant estimation of lipoprotein lipids by 1H NMR spectroscopy of serum. Results A Bayesian methodology, with a biochemical motivation, is presented for a real 1H NMR metabonomics data set of 75 serum samples. Lipoprotein lipid concentrations were independently obtained for these samples via ultracentrifugation and specific biochemical assays. The Bayesian models were constructed by Markov chain Monte Carlo (MCMC) and they showed remarkably good quantitative performance, the predictive R-values being 0.985 for the very low density lipoprotein triglycerides (VLDL-TG), 0.787 for the intermediate, 0.943 for the low, and 0.933 for the high density lipoprotein cholesterol (IDL-C, LDL-C and HDL-C, respectively). The modelling produced a kernel-based reformulation of the data, the parameters of which coincided with the well-known biochemical characteristics of the 1H NMR spectra; particularly for VLDL-TG and HDL-C the Bayesian methodology was able to clearly identify the most characteristic resonances within the heavily overlapping information in the spectra. For IDL-C and LDL-C the resulting model kernels were more complex than those for VLDL-TG and HDL-C, probably reflecting the severe overlap of the IDL and LDL resonances in the 1H NMR spectra. Conclusion The systematic use of Bayesian MCMC analysis is computationally demanding. Nevertheless, the combination of high-quality quantification and the biochemical rationale of the resulting models is expected to be useful in the field of metabonomics. PMID:17493257

Modeling environmental risk factors of autism in mice induces IBD-related gut microbial dysbiosis and hyperserotonemia.

PubMed

Lim, Joon Seo; Lim, Mi Young; Choi, Yongbin; Ko, GwangPyo

2017-04-20

Autism spectrum disorder (ASD) is a range of neurodevelopmental conditions that are sharply increasing in prevalence worldwide. Intriguingly, ASD is often accompanied by an array of systemic aberrations including (1) increased serotonin, (2) various modes of gastrointestinal disorders, and (3) inflammatory bowel disease (IBD), albeit the underlying cause for such comorbidities remains uncertain. Also, accumulating number of studies report that the gut microbial composition is significantly altered in children with ASD or patients with IBD. Surprisingly, when we analyzed the gut microbiota of poly I:C and VPA-induced mouse models of ASD, we found a distinct pattern of microbial dysbiosis that highly recapitulated those reported in clinical cases of ASD and IBD. Moreover, we report that such microbial dysbiosis led to notable perturbations in microbial metabolic pathways that are known to negatively affect the host, especially with regards to the pathogenesis of ASD and IBD. Lastly, we found that serum level of serotonin is significantly increased in both poly I:C and VPA mice, and that it correlates with increases of a bacterial genus and a metabolic pathway that are implicated in stimulation of host serotonin production. Our results using animal model identify prenatal environmental risk factors of autism as possible causative agents of IBD-related gut microbial dysbiosis in ASD, and suggest a multifaceted role of gut microbiota in the systemic pathogenesis of ASD and hyperserotonemia.

Pancreas preservation fluid microbial contamination is associated with poor islet isolation outcomes - a multi-centre cohort study.

PubMed

Meier, Raphael P H; Andrey, Diego O; Sun, Pamela; Niclauss, Nadja; Bédat, Benoît; Demuylder-Mischler, Sandrine; Borot, Sophie; Benhamou, Pierre-Yves; Wojtusciszyn, Anne; Buron, Fanny; Pernin, Nadine; Muller, Yannick D; Bosco, Domenico; van Delden, Christian; Berney, Thierry

2018-03-30

The microbiological safety of islet preparations is paramount. Preservation medium contamination is frequent, and its impact on islet yield and function remains unclear. Microbiological samples collected during islet isolations from 2006 to 2016 were analyzed and correlated to isolation and allo- and autotransplantation outcomes. Microbial contamination of preservation medium was found in 64.4% of processed donor pancreases (291/452). We identified 464 microorganisms including Staphylococcus (253/464, 54.5%), Streptococcus (31/464, 6.7%), and Candida species (25/464, 5.4%). Microbial contamination was associated with longer warm and cold ischemia times and lower numbers of postpurification islet equivalents, purity, transplant rate, and stimulation index (all P < 0.05). Six percent of the preparations accepted for transplantation showed microbial contamination after isolation (12/200); 9 of 12 were Candida species. Six patients were transplanted with a sample with late microbial growth discovered after the infusion. Insulin independence rate was not affected. This risk of transplanting a contaminated islets preparation was reduced by half following the implementation of an additional sampling after 24 h of islet culture. Pancreas preservation fluid microbial contamination is associated with lower transplant rate and poorer in vitro function, but not with changes in graft survival. Culture medium testing 1 day after isolation reduces the risk of incidental transplantation with contaminated islets. © 2018 Steunstichting ESOT.

Obesity and overweight: Impact on maternal and milk microbiome and their role for infant health and nutrition.

PubMed

Garcia-Mantrana, Izaskun; Collado, Maria Carmen

2016-08-01

Obesity, particularly in infants, is becoming a significant public health problem that has reached "epidemic" status worldwide. Obese children have an increased risk of developing obesity-related diseases, such as metabolic syndromes and diabetes, as well as increased risk of mortality and adverse health outcomes later in life. Experimental data show that maternal obesity has negative effects on the offspring's health in the short and long term. Increasing evidence suggests a key role for microbiota in host metabolism and energy harvest, providing novel tools for obesity prevention and management. The maternal environment, including nutrition and microbes, influences the likelihood of developing childhood diseases, which may persist and be exacerbated in adulthood. Maternal obesity and weight gain also influence microbiota composition and activity during pregnancy and lactation. They affect microbial diversity in the gut and breast milk. Such microbial changes may be transferred to the offspring during delivery and also during lactation, affecting infant microbial colonisation and immune system maturation. Thus, an adequate nutritional and microbial environment during the peri-natal period may provide a window of opportunity to reduce the risk of obesity and overweight in our infants using targeted strategies aimed at modulating the microbiota during early life. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Air toxics and birth defects: a Bayesian hierarchical approach to evaluate multiple pollutants and spina bifida.

PubMed

Swartz, Michael D; Cai, Yi; Chan, Wenyaw; Symanski, Elaine; Mitchell, Laura E; Danysh, Heather E; Langlois, Peter H; Lupo, Philip J

2015-02-09

While there is evidence that maternal exposure to benzene is associated with spina bifida in offspring, to our knowledge there have been no assessments to evaluate the role of multiple hazardous air pollutants (HAPs) simultaneously on the risk of this relatively common birth defect. In the current study, we evaluated the association between maternal exposure to HAPs identified by the United States Environmental Protection Agency (U.S. EPA) and spina bifida in offspring using hierarchical Bayesian modeling that includes Stochastic Search Variable Selection (SSVS). The Texas Birth Defects Registry provided data on spina bifida cases delivered between 1999 and 2004. The control group was a random sample of unaffected live births, frequency matched to cases on year of birth. Census tract-level estimates of annual HAP levels were obtained from the U.S. EPA's 1999 Assessment System for Population Exposure Nationwide. Using the distribution among controls, exposure was categorized as high exposure (>95(th) percentile), medium exposure (5(th)-95(th) percentile), and low exposure (<5(th) percentile, reference). We used hierarchical Bayesian logistic regression models with SSVS to evaluate the association between HAPs and spina bifida by computing an odds ratio (OR) for each HAP using the posterior mean, and a 95% credible interval (CI) using the 2.5(th) and 97.5(th) quantiles of the posterior samples. Based on previous assessments, any pollutant with a Bayes factor greater than 1 was selected for inclusion in a final model. Twenty-five HAPs were selected in the final analysis to represent "bins" of highly correlated HAPs (ρ > 0.80). We identified two out of 25 HAPs with a Bayes factor greater than 1: quinoline (ORhigh = 2.06, 95% CI: 1.11-3.87, Bayes factor = 1.01) and trichloroethylene (ORmedium = 2.00, 95% CI: 1.14-3.61, Bayes factor = 3.79). Overall there is evidence that quinoline and trichloroethylene may be significant contributors to the risk of spina bifida. Additionally, the use of Bayesian hierarchical models with SSVS is an alternative approach in the evaluation of multiple environmental pollutants on disease risk. This approach can be easily extended to environmental exposures, where novel approaches are needed in the context of multi-pollutant modeling.

Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients.

PubMed

Dagliati, Arianna; Malovini, Alberto; Decata, Pasquale; Cogni, Giulia; Teliti, Marsida; Sacchi, Lucia; Cerra, Carlo; Chiovato, Luca; Bellazzi, Riccardo

2016-01-01

In this work we present our efforts in building a model able to forecast patients' changes in clinical conditions when repeated measurements are available. In this case the available risk calculators are typically not applicable. We propose a Hierarchical Bayesian Logistic Regression model, which allows taking into account individual and population variability in model parameters estimate. The model is used to predict metabolic control and its variation in type 2 diabetes mellitus. In particular we have analyzed a population of more than 1000 Italian type 2 diabetic patients, collected within the European project Mosaic. The results obtained in terms of Matthews Correlation Coefficient are significantly better than the ones gathered with standard logistic regression model, based on data pooling.

Functional form and risk adjustment of hospital costs: Bayesian analysis of a Box-Cox random coefficients model.

PubMed

Hollenbeak, Christopher S

2005-10-15

While risk-adjusted outcomes are often used to compare the performance of hospitals and physicians, the most appropriate functional form for the risk adjustment process is not always obvious for continuous outcomes such as costs. Semi-log models are used most often to correct skewness in cost data, but there has been limited research to determine whether the log transformation is sufficient or whether another transformation is more appropriate. This study explores the most appropriate functional form for risk-adjusting the cost of coronary artery bypass graft (CABG) surgery. Data included patients undergoing CABG surgery at four hospitals in the midwest and were fit to a Box-Cox model with random coefficients (BCRC) using Markov chain Monte Carlo methods. Marginal likelihoods and Bayes factors were computed to perform model comparison of alternative model specifications. Rankings of hospital performance were created from the simulation output and the rankings produced by Bayesian estimates were compared to rankings produced by standard models fit using classical methods. Results suggest that, for these data, the most appropriate functional form is not logarithmic, but corresponds to a Box-Cox transformation of -1. Furthermore, Bayes factors overwhelmingly rejected the natural log transformation. However, the hospital ranking induced by the BCRC model was not different from the ranking produced by maximum likelihood estimates of either the linear or semi-log model. Copyright (c) 2005 John Wiley & Sons, Ltd.

A microbial identification framework for risk assessment.

PubMed

Bernatchez, Stéphane; Anoop, Valar; Saikali, Zeina; Breton, Marie

2018-06-01

Micro-organisms are increasingly used in a variety of products for commercial uses, including cleaning products. Such microbial-based cleaning products (MBCP) are represented as a more environmentally-friendly alternative to chemically based cleaning products. The identity of the micro-organisms formulated into these products is often considered confidential business information and is not revealed or it is only partly revealed (i.e., identification to the genus, not to the species). That paucity of information complicates the evaluation of the risk associated with their use. The accurate taxonomic identification of those micro-organisms is important so that a suitable risk assessment of the products can be conducted. To alleviate difficulties associated with adequate identification of micro-organisms in MBCP and other products containing micro-organisms, a microbial identification framework for risk assessment (MIFRA) has been elaborated. It serves to provide guidance on a polyphasic tiered approach, combining the data obtained from the use of various methods (i.e., polyphasic approach) combined with the sequential selection of the methods (i.e., tiered) to achieve a satisfactory identity of the micro-organism to an acceptable taxonomic level. The MIFRA is suitable in various risk assessment contexts for micro-organisms used in any commercial product. Copyright © 2018. Published by Elsevier Ltd.

Risk of Adverse Health Effects Due to Host-Microorganism Interactions

NASA Technical Reports Server (NTRS)

Ott, C. Mark; Oubre, Cherie; Castro, Sarah; Mehta, Satish; Pierson, Duane

2015-01-01

While preventive measures limit the presence of many medically significant microorganisms during spaceflight missions, microbial infection of crewmembers cannot be completely prevented. Spaceflight experiments over the past 50 years have demonstrated a unique microbial response to spaceflight culture, although the mechanisms behind those responses and their operational relevance were unclear. In 2007, the operational importance of these microbial responses was emphasized as the results of an experiment aboard STS-115 demonstrated that the enteric pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium) increased in virulence in a murine model of infection. The experiment was reproduced in 2008 aboard STS-123 confirming this finding. In response to these findings, the Institute of Medicine of the National Academies recommended that NASA investigate this risk and its potential impact on the health of the crew during spaceflight. NASA assigned this risk to the Human Research Program. To better understand this risk, evidence has been collected and reported from both spaceflight analog systems and actual spaceflight. Although the performance of virulence studies during spaceflight are challenging and often impractical, additional information has been and continues to be collected to better understand the risk to crew health. Still, the uncertainty concerning the extent and severity of these alterations in host-microorganism interactions is very large and requires more investigation.

Quantitative Microbial Risk Assessment Tutorial: Publishing a Microbial Density Time Series as a Txt File

EPA Science Inventory

A SARA Timeseries Utility supports analysis and management of time-varying environmental data including listing, graphing, computing statistics, computing meteorological data and saving in a WDM or text file. File formats supported include WDM, HSPF Binary (.hbn), USGS RDB, and T...

76 FR 58266 - Agency Information Collection Activities; Proposed Collection; Comment Request; Reporting...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-20

... contents of the docket, and access those documents in the public docket that are available electronically... monitor recreational water quality; assess, manage, and communicate health risks from waterborne microbial... public exposure to microbial pathogens. To qualify for a BEACH Act Grant, a state must submit information...

Awareness and Perceptions of Food Safety Risks and Risk Management in Poultry Production and Slaughter: A Qualitative Study of Direct-Market Poultry Producers in Maryland.

PubMed

Baron, Patrick; Frattaroli, Shannon

2016-01-01

The objective of this study was to document and understand the perceptions and opinions of small-scale poultry producers who market directly to consumers about microbial food safety risks in the poultry supply chain. Between January and November 2014, we conducted semi-structured, in-depth interviews with a convenience sample of 16 owner-operators of Maryland direct-market commercial poultry farms. Three overarching thematic categories emerged from these interviews that describe: 1) characteristics of Maryland direct-market poultry production and processing; 2) microbial food safety risk awareness and risk management in small-scale poultry production, slaughter and processing; and 3) motivations for prioritizing food safety in the statewide direct-market poultry supply chain. Key informants provided valuable insights on many topics relevant to evaluating microbial food safety in the Maryland direct-market poultry supply chain, including: direct-market poultry production and processing practices and models, perspectives on issues related to food safety risk management, perspectives on direct-market agriculture economics and marketing strategies, and ideas for how to enhance food safety at the direct-market level of the Maryland poultry supply chain. The findings have policy implications and provide insights into food safety in small-scale commercial poultry production, processing, distribution and retail. In addition, the findings will inform future food safety research on the small-scale US poultry supply chain.

Risk factors for moderate and severe microbial keratitis in daily wear contact lens users.

PubMed

Stapleton, Fiona; Edwards, Katie; Keay, Lisa; Naduvilath, Thomas; Dart, John K G; Brian, Garry; Holden, Brien

2012-08-01

To establish risk factors for moderate and severe microbial keratitis among daily contact lens (CL) wearers in Australia. A prospective, 12-month, population-based, case-control study. New cases of moderate and severe microbial keratitis in daily wear CL users presenting in Australia over a 12-month period were identified through surveillance of all ophthalmic practitioners. Case detection was augmented by record audits at major ophthalmic centers. Controls were users of daily wear CLs in the community identified using a national telephone survey. Cases and controls were interviewed by telephone to determine subject demographics and CL wear history. Multiple binary logistic regression was used to determine independent risk factors and univariate population attributable risk percentage (PAR%) was estimated for each risk factor. Independent risk factors, relative risk (with 95% confidence intervals [CIs]), and PAR%. There were 90 eligible moderate and severe cases related to daily wear of CLs reported during the study period. We identified 1090 community controls using daily wear CLs. Independent risk factors for moderate and severe keratitis while adjusting for age, gender, and lens material type included poor storage case hygiene 6.4× (95% CI, 1.9-21.8; PAR, 49%), infrequent storage case replacement 5.4× (95% CI, 1.5-18.9; PAR, 27%), solution type 7.2× (95% CI, 2.3-22.5; PAR, 35%), occasional overnight lens use (<1 night per week) 6.5× (95% CI, 1.3-31.7; PAR, 23%), high socioeconomic status 4.1× (95% CI, 1.2-14.4; PAR, 31%), and smoking 3.7× (95% CI, 1.1-12.8; PAR, 31%). Moderate and severe microbial keratitis associated with daily use of CLs was independently associated with factors likely to cause contamination of CL storage cases (frequency of storage case replacement, hygiene, and solution type). Other factors included occasional overnight use of CLs, smoking, and socioeconomic class. Disease load may be considerably reduced by attention to modifiable risk factors related to CL storage case practice. Copyright © 2012 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Investment appraisal using quantitative risk analysis.

PubMed

Johansson, Henrik

2002-07-01

Investment appraisal concerned with investments in fire safety systems is discussed. Particular attention is directed at evaluating, in terms of the Bayesian decision theory, the risk reduction that investment in a fire safety system involves. It is shown how the monetary value of the change from a building design without any specific fire protection system to one including such a system can be estimated by use of quantitative risk analysis, the results of which are expressed in terms of a Risk-adjusted net present value. This represents the intrinsic monetary value of investing in the fire safety system. The method suggested is exemplified by a case study performed in an Avesta Sheffield factory.

Associations between soil bacterial community structure and nutrient cycling functions in long-term organic farm soils following cover crop and organic fertilizer amendment.

PubMed

Fernandez, Adria L; Sheaffer, Craig C; Wyse, Donald L; Staley, Christopher; Gould, Trevor J; Sadowsky, Michael J

2016-10-01

Agricultural management practices can produce changes in soil microbial populations whose functions are crucial to crop production and may be detectable using high-throughput sequencing of bacterial 16S rRNA. To apply sequencing-derived bacterial community structure data to on-farm decision-making will require a better understanding of the complex associations between soil microbial community structure and soil function. Here 16S rRNA sequencing was used to profile soil bacterial communities following application of cover crops and organic fertilizer treatments in certified organic field cropping systems. Amendment treatments were hairy vetch (Vicia villosa), winter rye (Secale cereale), oilseed radish (Raphanus sativus), buckwheat (Fagopyrum esculentum), beef manure, pelleted poultry manure, Sustane(®) 8-2-4, and a no-amendment control. Enzyme activities, net N mineralization, soil respiration, and soil physicochemical properties including nutrient levels, organic matter (OM) and pH were measured. Relationships between these functional and physicochemical parameters and soil bacterial community structure were assessed using multivariate methods including redundancy analysis, discriminant analysis, and Bayesian inference. Several cover crops and fertilizers affected soil functions including N-acetyl-β-d-glucosaminidase and β-glucosidase activity. Effects, however, were not consistent across locations and sampling timepoints. Correlations were observed among functional parameters and relative abundances of individual bacterial families and phyla. Bayesian analysis inferred no directional relationships between functional activities, bacterial families, and physicochemical parameters. Soil functional profiles were more strongly predicted by location than by treatment, and differences were largely explained by soil physicochemical parameters. Composition of soil bacterial communities was predictive of soil functional profiles. Differences in soil function were better explained using both soil physicochemical test values and bacterial community structure data than using soil tests alone. Pursuing a better understanding of bacterial community composition and how it is affected by farming practices is a promising avenue for increasing our ability to predict the impact of management practices on important soil functions. Copyright © 2016. Published by Elsevier B.V.

Quantitative microbial risk assessment of microbial source tracking markers in recreational water contaminated with fresh untreated and secondary treated sewage.

PubMed

Ahmed, Warish; Hamilton, Kerry A; Lobos, Aldo; Hughes, Bridie; Staley, Christopher; Sadowsky, Michael J; Harwood, Valerie J

2018-05-14

Microbial source tracking (MST) methods have provided the means to identify sewage contamination in recreational waters, but the risk associated with elevated levels of MST targets such as sewage-associated Bacteroides HF183 and other markers is uncertain. Quantitative microbial risk assessment (QMRA) modeling allows interpretation of MST data in the context of the risk of gastrointestinal (GI) illness caused by exposure to known reference pathogens. In this study, five sewage-associated, quantitative PCR (qPCR) MST markers [Bacteroides HF183 (HF183), Methanobrevibacter smithii nifH (nifH), human adenovirus (HAdV), human polyomavirus (HPyV) and pepper mild mottle virus (PMMoV)] were evaluated to determine at what concentration these nucleic acid markers reflected a significant health risk from exposure to fresh untreated or secondary treated sewage in beach water. The QMRA models were evaluated for a target probability of illness of 36 GI illnesses/1000 swimming events (i.e., risk benchmark 0.036) for the reference pathogens norovirus (NoV) and human adenovirus 40/41 (HAdV 40/41). Sewage markers at several dilutions exceeded the risk benchmark for reference pathogens NoV and HAdV 40/41. HF183 concentrations 3.22 × 10 3 (for both NoV and HAdV 40/41) gene copies (GC)/100 mL of water contaminated with fresh untreated sewage represented risk >0.036. Similarly, HF183 concentrations 3.66 × 10 3 (for NoV and HAdV 40/41) GC/100 mL of water contaminated with secondary treated sewage represented risk >0.036. HAdV concentration as low as 4.11 × 10 1 GC/100 mL of water represented risk >0.036 when water was contaminated with secondary treated sewage. Results of this study provide a valuable context for water quality managers to evaluate human health risks associated with contamination from fresh sewage. The approach described here may also be useful in the future for evaluating health risks from contamination with aged or treated sewage or feces from other animal sources as more data are made available. Copyright © 2018 Elsevier Ltd. All rights reserved.

16S rRNA analysis of diversity of manure microbial community in dairy farm environment

PubMed Central

Miao, Max; Wang, Yi; Settles, Matthew; del Rio, Noelia Silva; Castillo, Alejandro; Souza, Alex; Pereira, Richard

2018-01-01

Dairy farms generate a considerable amount of manure, which is applied in cropland as fertilizer. While the use of manure as fertilizer reduces the application of chemical fertilizers, the main concern with regards to manure application is microbial pollution. Manure is a reservoir of a broad range of microbial populations, including pathogens, which have potential to cause contamination and pose risks to public and animal health. Despite the widespread use of manure fertilizer, the change in microbial diversity of manure under various treatment processes is still not well-understood. We hypothesize that the microbial population of animal waste changes with manure handling used in a farm environment. Consequential microbial risk caused by animal manure may depend on manure handling. In this study, a reconnaissance effort for sampling dairy manure in California Central Valley followed by 16S rRNA analysis of content and diversity was undertaken to understand the microbiome of manure after various handling processes. The microbial community analysis of manure revealed that the population in liquid manure differs from that in solid manure. For instance, the bacteria of genus Sulfuriomonas were unique in liquid samples, while the bacteria of genus Thermos were observed only in solid samples. Bacteria of genus Clostridium were present in both solid and liquid samples. The population among liquid samples was comparable, as was the population among solid samples. These findings suggest that the mode of manure application (i.e., liquid versus solid) could have a potential impact on the microbiome of cropland receiving manure as fertilizers. PMID:29304047

Performance of Two Quantitative PCR Methods for Microbial Source Tracking of Human Sewage and Implications for Microbial Risk Assessment in Recreational Waters

PubMed Central

Staley, Christopher; Gordon, Katrina V.; Schoen, Mary E.

2012-01-01

Before new, rapid quantitative PCR (qPCR) methods for assessment of recreational water quality and microbial source tracking (MST) can be useful in a regulatory context, an understanding of the ability of the method to detect a DNA target (marker) when the contaminant source has been diluted in environmental waters is needed. This study determined the limits of detection and quantification of the human-associated Bacteroides sp. (HF183) and human polyomavirus (HPyV) qPCR methods for sewage diluted in buffer and in five ambient, Florida water types (estuarine, marine, tannic, lake, and river). HF183 was quantifiable in sewage diluted up to 10−6 in 500-ml ambient-water samples, but HPyVs were not quantifiable in dilutions of >10−4. Specificity, which was assessed using fecal composites from dogs, birds, and cattle, was 100% for HPyVs and 81% for HF183. Quantitative microbial risk assessment (QMRA) estimated the possible norovirus levels in sewage and the human health risk at various sewage dilutions. When juxtaposed with the MST marker detection limits, the QMRA analysis revealed that HF183 was detectable when the modeled risk of gastrointestinal (GI) illness was at or below the benchmark of 10 illnesses per 1,000 exposures, but the HPyV method was generally not sensitive enough to detect potential health risks at the 0.01 threshold for frequency of illness. The tradeoff between sensitivity and specificity in the MST methods indicates that HF183 data should be interpreted judiciously, preferably in conjunction with a more host-specific marker, and that better methods of concentrating HPyVs from environmental waters are needed if this method is to be useful in a watershed management or monitoring context. PMID:22885746

Bayesian Evaluation of Dynamical Soil Carbon Models Using Soil Carbon Flux Data

NASA Astrophysics Data System (ADS)

Xie, H. W.; Romero-Olivares, A.; Guindani, M.; Allison, S. D.

2017-12-01

2016 was Earth's hottest year in the modern temperature record and the third consecutive record-breaking year. As the planet continues to warm, temperature-induced changes in respiration rates of soil microbes could reduce the amount of carbon sequestered in the soil organic carbon (SOC) pool, one of the largest terrestrial stores of carbon. This would accelerate temperature increases. In order to predict the future size of the SOC pool, mathematical soil carbon models (SCMs) describing interactions between the biosphere and atmosphere are needed. SCMs must be validated before they can be chosen for predictive use. In this study, we check two SCMs called CON and AWB for consistency with observed data using Bayesian goodness of fit testing that can be used in the future to compare other models. We compare the fit of the models to longitudinal soil respiration data from a meta-analysis of soil heating experiments using a family of Bayesian goodness of fit metrics called information criteria (IC), including the Widely Applicable Information Criterion (WAIC), the Leave-One-Out Information Criterion (LOOIC), and the Log Pseudo Marginal Likelihood (LPML). These IC's take the entire posterior distribution into account, rather than just one outputted model fit line. A lower WAIC and LOOIC and larger LPML indicate a better fit. We compare AWB and CON with fixed steady state model pool sizes. At equivalent SOC, dissolved organic carbon, and microbial pool sizes, CON always outperforms AWB quantitatively by all three IC's used. AWB monotonically improves in fit as we reduce the SOC steady state pool size while fixing all other pool sizes, and the same is almost true for CON. The AWB model with the lowest SOC is the best performing AWB model, while the CON model with the second lowest SOC is the best performing model. We observe that AWB displays more changes in slope sign and qualitatively displays more adaptive dynamics, which prevents AWB from being fully ruled out for predictive use, but based on IC's, CON is clearly the superior model for fitting the data. Hence, we demonstrate that Bayesian goodness of fit testing with information criteria helps us rigorously determine the consistency of models with data. Models that demonstrate their consistency to multiple data sets with our approach can then be selected for further refinement.

Assessing the safety of Pseudomonas putida introduction in the environment: an overview of ecotoxicological tests.

PubMed

de Castro, Vera Lúcia S S; Jonsson, Cláudio Martin; Silva, Célia Maria M; de Holanda Nunes Maia, Aline

2010-04-01

Risk assessment guidelines for the environmental release of microbial agents are performed in a tiered sequence which includes evaluation of exposure effects on non-target organisms. However, it becomes important to verify whether environmental risk assessment from temperate studies is applicable to tropical countries, as Brazil. Pseudomonas putida is a bacteria showing potential to be used for environmental applications as bioremediation and plant disease control. This study investigates the effects of this bacteria exposure on rodents and aquatic organisms (Daphnia similis) that are recommended to be used as non-target organism in environmental risk assessments. Also, the microbial activity in three different soils under P. putida exposure was evaluated. Rats did not show clinical alterations, although the agent was recovered 16h after the exposure in lung homogenates. The bacteria did not reduce significantly the reproduction and survival of D. similis. The soil enzymatic activities presented fluctuating values after inoculation with bacteria. The measurement of perturbations in soil biochemical characteristics is presented as an alternative way of monitoring the overall effects of the microbial agent to be introduced even in first stage (Tier I) of the risk assessment in tropical ecosystems. Copyright 2009 Elsevier Inc. All rights reserved.

Microbial growth and physiology in space - A review

NASA Technical Reports Server (NTRS)

Cioletti, Louis A.; Mishra, S. K.; Pierson, Duane L.

1991-01-01

An overview of microbial behavior in closed environments is given with attention to data related to simulated microgravity and actual space flight. Microbes are described in terms of antibiotic sensitivity, subcellular structure, and physiology, and the combined effects are considered of weightlessness and cosmic radiation on human immunity to such microorganisms. Space flight results report such effects as increased phage induction, accelerated microbial growth rates, and the increased risk of disease communication and microbial exchange aboard confining spacecraft. Ultrastructural changes are also noted in the nuclei, cell membranes, and cytoplasmic streaming, and it appears that antibiotic sensitivity is reduced under both actual and simulated conditions of spaceflight.

Bayesian mapping of HIV infection among women of reproductive age in Rwanda.

PubMed

Niragire, François; Achia, Thomas N O; Lyambabaje, Alexandre; Ntaganira, Joseph

2015-01-01

HIV prevalence is rising and has been consistently higher among women in Rwanda whereas a decreasing national HIV prevalence rate in the adult population has stabilised since 2005. Factors explaining the increased vulnerability of women to HIV infection are not currently well understood. A statistical mapping at smaller geographic units and the identification of key HIV risk factors are crucial for pragmatic and more efficient interventions. The data used in this study were extracted from the 2010 Rwanda Demographic and Health Survey data for 6952 women. A full Bayesian geo-additive logistic regression model was fitted to data in order to assess the effect of key risk factors and map district-level spatial effects on the risk of HIV infection. The results showed that women who had STIs, concurrent sexual partners in the 12 months prior to the survey, a sex debut at earlier age than 19 years, were living in a woman-headed or high-economic status household were significantly associated with a higher risk of HIV infection. There was a protective effect of high HIV knowledge and perception. Women occupied in agriculture, and those residing in rural areas were also associated with lower risk of being infected. This study provides district-level maps of the variation of HIV infection among women of child-bearing age in Rwanda. The maps highlight areas where women are at a higher risk of infection; the aspect that proximate and distal factors alone could not uncover. There are distinctive geographic patterns, although statistically insignificant, of the risk of HIV infection suggesting potential effectiveness of district specific interventions. The results also suggest that changes in sexual behaviour can yield significant results in controlling HIV infection in Rwanda.

Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks.

PubMed

Aussem, Alex; de Morais, Sérgio Rodrigues; Corbex, Marilys

2012-01-01

We propose a new graphical framework for extracting the relevant dietary, social and environmental risk factors that are associated with an increased risk of nasopharyngeal carcinoma (NPC) on a case-control epidemiologic study that consists of 1289 subjects and 150 risk factors. This framework builds on the use of Bayesian networks (BNs) for representing statistical dependencies between the random variables. We discuss a novel constraint-based procedure, called Hybrid Parents and Children (HPC), that builds recursively a local graph that includes all the relevant features statistically associated to the NPC, without having to find the whole BN first. The local graph is afterwards directed by the domain expert according to his knowledge. It provides a statistical profile of the recruited population, and meanwhile helps identify the risk factors associated to NPC. Extensive experiments on synthetic data sampled from known BNs show that the HPC outperforms state-of-the-art algorithms that appeared in the recent literature. From a biological perspective, the present study confirms that chemical products, pesticides and domestic fume intake from incomplete combustion of coal and wood are significantly associated with NPC risk. These results suggest that industrial workers are often exposed to noxious chemicals and poisonous substances that are used in the course of manufacturing. This study also supports previous findings that the consumption of a number of preserved food items, like house made proteins and sheep fat, are a major risk factor for NPC. BNs are valuable data mining tools for the analysis of epidemiologic data. They can explicitly combine both expert knowledge from the field and information inferred from the data. These techniques therefore merit consideration as valuable alternatives to traditional multivariate regression techniques in epidemiologic studies. Copyright © 2011 Elsevier B.V. All rights reserved.

Bayesian Mapping of HIV Infection among Women of Reproductive Age in Rwanda

PubMed Central

Niragire, François; Achia, Thomas N. O.; Lyambabaje, Alexandre; Ntaganira, Joseph

2015-01-01

HIV prevalence is rising and has been consistently higher among women in Rwanda whereas a decreasing national HIV prevalence rate in the adult population has stabilised since 2005. Factors explaining the increased vulnerability of women to HIV infection are not currently well understood. A statistical mapping at smaller geographic units and the identification of key HIV risk factors are crucial for pragmatic and more efficient interventions. The data used in this study were extracted from the 2010 Rwanda Demographic and Health Survey data for 6952 women. A full Bayesian geo-additive logistic regression model was fitted to data in order to assess the effect of key risk factors and map district-level spatial effects on the risk of HIV infection. The results showed that women who had STIs, concurrent sexual partners in the 12 months prior to the survey, a sex debut at earlier age than 19 years, were living in a woman-headed or high-economic status household were significantly associated with a higher risk of HIV infection. There was a protective effect of high HIV knowledge and perception. Women occupied in agriculture, and those residing in rural areas were also associated with lower risk of being infected. This study provides district-level maps of the variation of HIV infection among women of child-bearing age in Rwanda. The maps highlight areas where women are at a higher risk of infection; the aspect that proximate and distal factors alone could not uncover. There are distinctive geographic patterns, although statistically insignificant, of the risk of HIV infection suggesting potential effectiveness of district specific interventions. The results also suggest that changes in sexual behaviour can yield significant results in controlling HIV infection in Rwanda. PMID:25811462

DART Employees at Work

NASA Image and Video Library

2014-10-31

The Dust Atmospheric Recovery Technology, or DART, spacecraft is being assembled in a laboratory inside the Space Life Sciences Lab at NASA’s Kennedy Space Center in Florida. DART will characterize the dust loading and microbial diversity in the atmosphere over Florida during summer months with a special emphasis on their interactions during an African dust storm. DART will be used to collect atmospheric aerosols and suspended microbial cells over Florida and Kennedy. Results will help predict the risks of excessive microbial contamination adhering to spacecraft surfaces.

DART Employees at Work

NASA Image and Video Library

2014-10-31

A researcher at NASA’s Kennedy Space Center in Florida checks a reading on the Dust Atmospheric Recovery Technology, or DART, spacecraft inside a laboratory at the Space Life Sciences Lab. DART will characterize the dust loading and microbial diversity in the atmosphere over Florida during summer months with a special emphasis on their interactions during an African dust storm. DART will be used to collect atmospheric aerosols and suspended microbial cells over Florida and Kennedy. Results will help predict the risks of excessive microbial contamination adhering to spacecraft surfaces.

DART Employees at Work

NASA Image and Video Library

2014-10-31

Researchers at NASA’s Kennedy Space Center in Florida check readings on the Dust Atmospheric Recovery Technology, or DART, spacecraft inside a laboratory at the Space Life Sciences Lab. DART will characterize the dust loading and microbial diversity in the atmosphere over Florida during summer months with a special emphasis on their interactions during an African dust storm. DART will be used to collect atmospheric aerosols and suspended microbial cells over Florida and Kennedy. Results will help predict the risks of excessive microbial contamination adhering to spacecraft surfaces.

Microbial signature profiles of periodontally healthy and diseased patients.

PubMed

Lourenço, Talita Gomes Baêta; Heller, Débora; Silva-Boghossian, Carina Maciel; Cotton, Sean L; Paster, Bruce J; Colombo, Ana Paula Vieira

2014-11-01

To determine microbial profiles that discriminate periodontal health from different forms of periodontal diseases. Subgingival biofilm was obtained from patients with periodontal health (27), gingivitis (11), chronic periodontitis (35) and aggressive periodontitis (24), and analysed for the presence of >250 species/phylotypes using HOMIM. Microbial differences among groups were examined by Mann-Whitney U-test. Regression analyses were performed to determine microbial risk indicators of disease. Putative and potential new periodontal pathogens were more prevalent in subjects with periodontal diseases than periodontal health. Detection of Porphyromonas endodontalis/Porphyromonas spp. (OR 9.5 [1.2-73.1]) and Tannerella forsythia (OR 38.2 [3.2-450.6]), and absence of Neisseria polysaccharea (OR 0.004 [0-0.15]) and Prevotella denticola (OR 0.014 [0-0.49], p < 0.05) were risk indicators of periodontal disease. Presence of Aggregatibacter actinomycetemcomitans (OR 29.4 [3.4-176.5]), Cardiobacterium hominis (OR 14.9 [2.3-98.7]), Peptostreptococcaceae sp. (OR 35.9 [2.7-483.9]), P. alactolyticus (OR 31.3 [2.1-477.2]), and absence of Fretibacterium spp. (OR 0.024 [0.002-0.357]), Fusobacterium naviforme/Fusobacterium nucleatum ss vincentii (OR 0.015 [0.001-0.223]), Granulicatella adiacens/Granulicatella elegans (OR 0.013 [0.001-0.233], p < 0.05) were associated with aggressive periodontitis. There were specific microbial signatures of the subgingival biofilm that were able to distinguish between microbiomes of periodontal health and diseases. Such profiles may be used to establish risk of disease. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Microbial Signature Profiles of Periodontally Healthy and Diseased Patients

PubMed Central

Lourenço, Talita Gomes Baêta; Heller, Débora; da Silva-Boghossian, Carina Maciel; Cotton, Sean L.; Paster, Bruce J.; Colombo, Ana Paula Vieira

2014-01-01

Aim To determine microbial profiles that discriminate periodontal health from different forms of periodontal diseases. Methods Subgingival biofilm was obtained from patients with periodontal health (27), gingivitis (11), chronic periodontitis (35) and aggressive periodontitis (24), and analyzed for the presence of >250 species/phylotypes using HOMIM. Microbial differences among groups were examined by Mann-Whitney. Regression analyses were performed to determine microbial risk indicators of disease. Results Putative and potential new periodontal pathogens were more prevalent in subjects with periodontal diseases than periodontal health. Detection of Porphyromonas endodontalis/Porphyromonas spp. (OR 9.5 [1.2–73.1]) and Tannerella forsythia (OR 38.2 [3.2–450.6]), and absence of Neisseria polysaccharea (OR 0.004 [0–0.15]) and Prevotella denticola (OR 0.014 [0–0.49], p<0.05) were risk indicators of periodontal disease. Presence of Aggregatibacter actinomycetemcomitans (OR 29.4 [3.4–176.5]), Cardiobacterium hominis (OR 14.9 [2.3–98.7]), Peptostreptococcaceae sp. (OR 35.9 [2.7–483.9]), P. alactolyticus (OR 31.3 [2.1–477.2]), and absence of Fretibacterium spp. (OR 0.024 [0.002–0.357]), Fusobacterium naviforme/Fusobacterium nucleatum ss vincentii (OR 0.015 [0.001–0.223]), Granulicatella adiacens/Granulicatella elegans (OR 0.013 [0.001–0.233], p<0.05) were associated with aggressive periodontitis. Conclusion There were specific microbial signatures of the subgingival biofilm that were able to distinguish between microbiomes of periodontal health and diseases. Such profiles may be used to establish risk of disease. PMID:25139407

Information content of incubation experiments for inverse estimation of pools in the Rothamsted carbon model: a Bayesian approach

NASA Astrophysics Data System (ADS)

Scharnagl, Benedikt; Vrugt, Jasper A.; Vereecken, Harry; Herbst, Michael

2010-05-01

Turnover of soil organic matter is usually described with multi-compartment models. However, a major drawback of these models is that the conceptually defined compartments (or pools) do not necessarily correspond to measurable soil organic carbon (SOC) fractions in real practice. This not only impairs our ability to rigorously evaluate SOC models but also makes it difficult to derive accurate initial states. In this study, we tested the usefulness and applicability of inverse modeling to derive the various carbon pool sizes in the Rothamsted carbon model (ROTHC) using a synthetic time series of mineralization rates from laboratory incubation. To appropriately account for data and model uncertainty we considered a Bayesian approach using the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. This Markov chain Monte Carlo scheme derives the posterior probability density distribution of the initial pool sizes at the start of incubation from observed mineralization rates. We used the Kullback-Leibler divergence to quantify the information contained in the data and to illustrate the effect of increasing incubation times on the reliability of the pool size estimates. Our results show that measured mineralization rates generally provide sufficient information to reliably estimate the sizes of all active pools in the ROTHC model. However, with about 900 days of incubation, these experiments are excessively long. The use of prior information on microbial biomass provided a way forward to significantly reduce uncertainty and required duration of incubation to about 600 days. Explicit consideration of model parameter uncertainty in the estimation process further impaired the identifiability of initial pools, especially for the more slowly decomposing pools. Our illustrative case studies show how Bayesian inverse modeling can be used to provide important insights into the information content of incubation experiments. Moreover, the outcome of this virtual experiment helps to explain the results of related real-world studies on SOC dynamics.

Airborne bacterial assemblage in a zero carbon building: A case study.

PubMed

Leung, M H Y; Tong, X; Tong, J C K; Lee, P K H

2018-01-01

Currently, there is little information pertaining to the airborne bacterial communities of green buildings. In this case study, the air bacterial community of a zero carbon building (ZCB) in Hong Kong was characterized by targeting the bacterial 16S rRNA gene. Bacteria associated with the outdoor environment dominated the indoor airborne bacterial assemblage, with a modest contribution from bacteria associated with human skin. Differences in overall community diversity, membership, and composition associated with short (day-to-day) and long-term temporal properties were detected, which may have been driven by specific environmental genera and taxa. Furthermore, time-decay relationships in community membership (based on unweighted UniFrac distances) and composition (based on weighted UniFrac distances) differed depending on the season and sampling location. A Bayesian source-tracking approach further supported the importance of adjacent outdoor air bacterial assemblage in sourcing the ZCB indoor bioaerosol. Despite the unique building attributes, the ZCB microbial assemblage detected and its temporal characteristics were not dissimilar to that of conventional built environments investigated previously. Future controlled experiments and microbial assemblage investigations of other ZCBs will undoubtedly uncover additional knowledge related to how airborne bacteria in green buildings may be influenced by their distinctive architectural attributes. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

APPLICATION OF BAYESIAN AND GEOSTATISTICAL MODELING TO THE ENVIRONMENTAL MONITORING OF CS-137 AT THE IDAHO NATIONAL LABORATORY

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

Kara G. Eby

2010-08-01

At the Idaho National Laboratory (INL) Cs-137 concentrations above the U.S. Environmental Protection Agency risk-based threshold of 0.23 pCi/g may increase the risk of human mortality due to cancer. As a leader in nuclear research, the INL has been conducting nuclear activities for decades. Elevated anthropogenic radionuclide levels including Cs-137 are a result of atmospheric weapons testing, the Chernobyl accident, and nuclear activities occurring at the INL site. Therefore environmental monitoring and long-term surveillance of Cs-137 is required to evaluate risk. However, due to the large land area involved, frequent and comprehensive monitoring is limited. Developing a spatial model thatmore » predicts Cs-137 concentrations at unsampled locations will enhance the spatial characterization of Cs-137 in surface soils, provide guidance for an efficient monitoring program, and pinpoint areas requiring mitigation strategies. The predictive model presented herein is based on applied geostatistics using a Bayesian analysis of environmental characteristics across the INL site, which provides kriging spatial maps of both Cs-137 estimates and prediction errors. Comparisons are presented of two different kriging methods, showing that the use of secondary information (i.e., environmental characteristics) can provide improved prediction performance in some areas of the INL site.« less

From reading numbers to seeing ratios: a benefit of icons for risk comprehension.

PubMed

Tubau, Elisabet; Rodríguez-Ferreiro, Javier; Barberia, Itxaso; Colomé, Àngels

2018-06-21

Promoting a better understanding of statistical data is becoming increasingly important for improving risk comprehension and decision-making. In this regard, previous studies on Bayesian problem solving have shown that iconic representations help infer frequencies in sets and subsets. Nevertheless, the mechanisms by which icons enhance performance remain unclear. Here, we tested the hypothesis that the benefit offered by icon arrays lies in a better alignment between presented and requested relationships, which should facilitate the comprehension of the requested ratio beyond the represented quantities. To this end, we analyzed individual risk estimates based on data presented either in standard verbal presentations (percentages and natural frequency formats) or as icon arrays. Compared to the other formats, icons led to estimates that were more accurate, and importantly, promoted the use of equivalent expressions for the requested probability. Furthermore, whereas the accuracy of the estimates based on verbal formats depended on their alignment with the text, all the estimates based on icons were equally accurate. Therefore, these results support the proposal that icons enhance the comprehension of the ratio and its mapping onto the requested probability and point to relational misalignment as potential interference for text-based Bayesian reasoning. The present findings also argue against an intrinsic difficulty with understanding single-event probabilities.

Hazard Screening Methods for Nanomaterials: A Comparative Study

PubMed Central

Murphy, Finbarr; Mullins, Martin; Furxhi, Irini; Costa, Anna L.; Simeone, Felice C.

2018-01-01

Hazard identification is the key step in risk assessment and management of manufactured nanomaterials (NM). However, the rapid commercialisation of nano-enabled products continues to out-pace the development of a prudent risk management mechanism that is widely accepted by the scientific community and enforced by regulators. However, a growing body of academic literature is developing promising quantitative methods. Two approaches have gained significant currency. Bayesian networks (BN) are a probabilistic, machine learning approach while the weight of evidence (WoE) statistical framework is based on expert elicitation. This comparative study investigates the efficacy of quantitative WoE and Bayesian methodologies in ranking the potential hazard of metal and metal-oxide NMs—TiO2, Ag, and ZnO. This research finds that hazard ranking is consistent for both risk assessment approaches. The BN and WoE models both utilize physico-chemical, toxicological, and study type data to infer the hazard potential. The BN exhibits more stability when the models are perturbed with new data. The BN has the significant advantage of self-learning with new data; however, this assumes all input data is equally valid. This research finds that a combination of WoE that would rank input data along with the BN is the optimal hazard assessment framework. PMID:29495342

Cancer incidence and mortality projections up to 2020 in Catalonia by means of Bayesian models.

PubMed

Ribes, J; Esteban, L; Clèries, R; Galceran, J; Marcos-Gragera, R; Gispert, R; Ameijide, A; Vilardell, M L; Borras, J; Puigdefabregas, A; Buxó, M; Freitas, A; Izquierdo, A; Borras, J M

2014-08-01

To predict the burden of cancer in Catalonia by 2020 assessing changes in demography and cancer risk during 2010-2020. Data were obtained from Tarragona and Girona cancer registries and Catalan mortality registry. Population age distribution was obtained from the Catalan Institute of Statistics. Predicted cases in Catalonia were estimated through autoregressive Bayesian age-period-cohort models. There will be diagnosed 26,455 incident cases among men and 18,345 among women during 2020, which means an increase of 22.5 and 24.5 % comparing with the cancer incidence figures of 2010. In men, the increase of cases (22.5 %) can be partitioned in three components: 12 % due to ageing, 8 % due to increase in population size and 2 % due to cancer risk. In women, the role of each component was 9, 8 and 8 %, respectively. The increased risk is mainly expected to be observed in tobacco-related tumours among women and in colorectal and liver cancers among men. During 2010-2020 a mortality decline is expected in both sexes. The expected increase of cancer incidence, mainly due to tobacco-related tumours in women and colorectal in men, reinforces the need to strengthen smoking prevention and the expansion of early detection of colorectal cancer in Catalonia.

What is the relative health risk to swimmers from California Seagull feces compared to bather shedders?

EPA Science Inventory

Estimated infection risks to swimmers from California seagull and bather sources of fecal contamination at a beach in Southern California were compared using quantitative microbial risk assessment (QMRA). The risk to swimmers of gastro-intestinal infections was estimated from Ca...

Finding Groups Using Model-Based Cluster Analysis: Heterogeneous Emotional Self-Regulatory Processes and Heavy Alcohol Use Risk

ERIC Educational Resources Information Center

Mun, Eun Young; von Eye, Alexander; Bates, Marsha E.; Vaschillo, Evgeny G.

2008-01-01

Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of nonnested models using the Bayesian information criterion to compare multiple models and identify the…

A Biologically Informed Framework for the Analysis of the PPAR Signaling Pathway using a Bayesian Network

EPA Science Inventory

The US EPA’s ToxCastTM program seeks to combine advances in high-throughput screening technology with methodologies from statistics and computer science to develop high-throughput decision support tools for assessing chemical hazard and risk. To develop new methods of analysis of...

Population viability assessment of salmonids by using probabilistic networks

Treesearch

Danny C. Lee; Bruce E. Rieman

1997-01-01

Public agencies are being asked to quantitatively assess the impact of land management activities on sensitive populations of salmonids. To aid in these assessments, we developed a Bayesian viability assessment procedure (BayVAM) to help characterize land use risks to salmonids in the Pacific Northwest. This procedure incorporates a hybrid approach to viability...

Bayesian averaging over Decision Tree models for trauma severity scoring.

PubMed

Schetinin, V; Jakaite, L; Krzanowski, W

2018-01-01

Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the "gold" standard of screening a patient's conditions for predicting survival probability, based on logistic regression modelling, which is used in trauma care for clinical purposes and quality audit. This methodology is based on theoretical assumptions about data and uncertainties. Models induced within such an approach have exposed a number of problems, providing unexplained fluctuation of predicted survival and low accuracy of estimating uncertainty intervals within which predictions are made. Bayesian method, which in theory is capable of providing accurate predictions and uncertainty estimates, has been adopted in our study using Decision Tree models. Our approach has been tested on a large set of patients registered in the US National Trauma Data Bank and has outperformed the standard method in terms of prediction accuracy, thereby providing practitioners with accurate estimates of the predictive posterior densities of interest that are required for making risk-aware decisions. Copyright © 2017 Elsevier B.V. All rights reserved.

Profiles of lead in urban dust and the effect of the distance to multi-industry in an old heavy industry city in China.

PubMed

Yu, Yang; Li, Yingxia; Li, Ben; Shen, Zhenyao; Stenstrom, Michael K

2017-03-01

Lead (Pb) concentration in urban dust is often higher than background concentrations and can result in a wide range of health risks to local communities. To understand Pb distribution in urban dust and how multi-industrial activity affects Pb concentration, 21 sampling sites within the heavy industry city of Jilin, China, were analyzed for Pb concentration. Pb concentrations of all 21 urban dust samples from the Jilin City Center were higher than the background concentration for soil in Jilin Province. The analyses show that distance to industry is an important parameter determining health risks associated with Pb in urban dust. The Pb concentration showed an exponential decrease, with increasing distance from industry. Both maximum likelihood estimation and Bayesian analysis were used to estimate the exponential relationship between Pb concentration and distance to multi-industry areas. We found that Bayesian analysis was a better method with less uncertainty for estimating Pb dust concentrations based on their distance to multi-industry, and this approach is recommended for further study. Copyright © 2016. Published by Elsevier Inc.

Impact of the reusing of food manufacturing wastewater for irrigation in a closed system on the microbiological quality of the food crops.

PubMed

Beneduce, Luciano; Gatta, Giuseppe; Bevilacqua, Antonio; Libutti, Angela; Tarantino, Emanuele; Bellucci, Micol; Troiano, Eleonora; Spano, Giuseppe

2017-11-02

In order to evaluate if the reuse of food industry treated wastewater is compatible for irrigation of food crops, without increased health risk, in the present study a cropping system, in which ground water and treated wastewater were used for irrigation of tomato and broccoli, during consecutive crop seasons was monitored. Water, crop environment and final products were monitored for microbial indicators and pathogenic bacteria, by conventional and molecular methods. The microbial quality of the irrigation waters influenced sporadically the presence of microbial indicators in soil. No water sample was found positive for pathogenic bacteria, independently from the source. Salmonella spp. and Listeria monocytogenes were detected in soil samples, independently from the irrigation water source. No pathogen was found to contaminate tomato plants, while Listeria monocytogenes and E. coli O157:H7 were detected on broccoli plant, but when final produce were harvested, no pathogen was detected on edible part. The level of microbial indicators and detection of pathogenic bacteria in field and plant was not dependent upon wastewater used. Our results, suggest that reuse of food industry wastewater for irrigation of agricultural crop can be applied without significant increase of potential health risk related to microbial quality. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantitative Microbial Risk Assessment and Infectious Disease Transmission Modeling of Waterborne Enteric Pathogens.

PubMed

Brouwer, Andrew F; Masters, Nina B; Eisenberg, Joseph N S

2018-04-20

Waterborne enteric pathogens remain a global health threat. Increasingly, quantitative microbial risk assessment (QMRA) and infectious disease transmission modeling (IDTM) are used to assess waterborne pathogen risks and evaluate mitigation. These modeling efforts, however, have largely been conducted independently for different purposes and in different settings. In this review, we examine the settings where each modeling strategy is employed. QMRA research has focused on food contamination and recreational water in high-income countries (HICs) and drinking water and wastewater in low- and middle-income countries (LMICs). IDTM research has focused on large outbreaks (predominately LMICs) and vaccine-preventable diseases (LMICs and HICs). Human ecology determines the niches that pathogens exploit, leading researchers to focus on different risk assessment research strategies in different settings. To enhance risk modeling, QMRA and IDTM approaches should be integrated to include dynamics of pathogens in the environment and pathogen transmission through populations.

A two-stage model in a Bayesian framework to estimate a survival endpoint in the presence of confounding by indication.

PubMed

Bellera, Carine; Proust-Lima, Cécile; Joseph, Lawrence; Richaud, Pierre; Taylor, Jeremy; Sandler, Howard; Hanley, James; Mathoulin-Pélissier, Simone

2018-04-01

Background Biomarker series can indicate disease progression and predict clinical endpoints. When a treatment is prescribed depending on the biomarker, confounding by indication might be introduced if the treatment modifies the marker profile and risk of failure. Objective Our aim was to highlight the flexibility of a two-stage model fitted within a Bayesian Markov Chain Monte Carlo framework. For this purpose, we monitored the prostate-specific antigens in prostate cancer patients treated with external beam radiation therapy. In the presence of rising prostate-specific antigens after external beam radiation therapy, salvage hormone therapy can be prescribed to reduce both the prostate-specific antigens concentration and the risk of clinical failure, an illustration of confounding by indication. We focused on the assessment of the prognostic value of hormone therapy and prostate-specific antigens trajectory on the risk of failure. Methods We used a two-stage model within a Bayesian framework to assess the role of the prostate-specific antigens profile on clinical failure while accounting for a secondary treatment prescribed by indication. We modeled prostate-specific antigens using a hierarchical piecewise linear trajectory with a random changepoint. Residual prostate-specific antigens variability was expressed as a function of prostate-specific antigens concentration. Covariates in the survival model included hormone therapy, baseline characteristics, and individual predictions of the prostate-specific antigens nadir and timing and prostate-specific antigens slopes before and after the nadir as provided by the longitudinal process. Results We showed positive associations between an increased prostate-specific antigens nadir, an earlier changepoint and a steeper post-nadir slope with an increased risk of failure. Importantly, we highlighted a significant benefit of hormone therapy, an effect that was not observed when the prostate-specific antigens trajectory was not accounted for in the survival model. Conclusion Our modeling strategy was particularly flexible and accounted for multiple complex features of longitudinal and survival data, including the presence of a random changepoint and a time-dependent covariate.

Prevalence and risk factors of Coxiella burnetii seropositivity in Danish beef and dairy cattle at slaughter adjusted for test uncertainty.

PubMed

Paul, Suman; Agger, Jens F; Agerholm, Jørgen S; Markussen, Bo

2014-03-01

Antibodies to Coxiella burnetii have been found in the Danish dairy cattle population with high levels of herd and within herd seroprevalences. However, the prevalence of antibodies to C. burnetii in Danish beef cattle remains unknown. The objectives of this study were to (1) estimate the prevalence and (2) identify risk factors associated with C. burnetii seropositivity in Danish beef and dairy cattle based on sampling at slaughter. Eight hundred blood samples from slaughtered cattle were collected from six Danish slaughter houses from August to October 2012 following a random sampling procedure. Blood samples were tested by a commercially available C. burnetii antibody ELISA kit. A sample was defined positive if the sample-to-positive ratio was greater than or equal to 40. Animal and herd information were extracted from the Danish Cattle Database. Apparent (AP) and true prevalences (TPs) specific for breed, breed groups, gender and herd type; and breed-specific true prevalences with a random effect of breed was estimated in a Bayesian framework. A Bayesian logistic regression model was used to identify risk factors of C. burnetii seropositivity. Test sensitivity and specificity estimates from a previous study involving Danish dairy cattle were used to generate prior information. The prevalence was significantly higher in dairy breeds (AP=9.11%; TP=9.45%) than in beef breeds (AP=4.32%; TP=3.54%), in females (AP=9.10%; TP=9.40%) than in males (AP=3.62%; TP=2.61%) and in dairy herds (AP=15.10%; TP=16.67%) compared to beef herds (AP=4.54%; TP=3.66%). The Bayesian logistic regression model identified breed group along with age, and number of movements as contributors for C. burnetii seropositivity. The risk of seropositivity increased with age and increasing number of movements between herds. Results indicate that seroprevalence of C. burnetii is lower in cattle sent for slaughter than in Danish dairy cows in production units. A greater proportion of this prevalence is attributed to slaughtered cattle of dairy breeds or cattle raised in dairy herds rather than beef breeds. Copyright © 2014 Elsevier B.V. All rights reserved.

Overview of Microbial Monitoring Technologies Considered for Use Inside Long Duration Spaceflights and Planetary Habitats

NASA Technical Reports Server (NTRS)

Roman, Monsi C.; Ott, C. Mark

2015-01-01

The purpose of this presentation is to start a conversation including the Crew Health, ECLSS, and Planetary Protection communities about the best approach for inflight microbial monitoring as part of a risk mitigation strategy to prevent forward and back contamination while protecting the crew and vehicle.

Stereoselective Microbial Transformation of Triadimefon to Triadimenol in Soils: Varying Production Rates of Triadimenol Stereoisomers Could Impact Risk Assessment

EPA Science Inventory

The microbial transformation of triadimefon, an agricultural fungicide of the 1,2,4-triazole class, was followed over several months under aerobic conditions in 3 different soil types to observe rates and products of transformation as well as enantiomer fractions of parent and pr...

77 FR 45350 - Notice of Availability of Microbial Risk Assessment Guideline: Pathogenic Microorganisms With...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-31

... assessment. Although the focus of this guideline is microbial contamination of water and food, it will also...: Pathogenic Microorganisms With Focus on Food and Water AGENCY: Environmental Protection Agency (EPA). ACTION: Notice of availability. SUMMARY: The U.S. Environmental Protection Agency (EPA) and the Food Safety and...

Canopy gaps decrease microbial densities and disease risk for a shade-intolerant tree species

Treesearch

Kurt O. Reinhart; Alejandro A. Royo; Stacie A. Kageyama; Keith. Clay

2010-01-01

Canopy disturbances such as windthrowevents have obvious impacts on forest structure and composition aboveground, but changes in soil microbial communities and the consequences of these changes are less understood.We characterized the densities of a soil-borne pathogenic oomycete (Pythium) and a common saprotrophic zygomycete (Mortierella...

77 FR 3263 - Agency Information Collection Activities; Submission to OMB for Review and Approval; Comment...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-23

... index listing of the contents of the docket, and to access those documents in the docket that are... recreational water quality; assess, manage, and communicate health risks from waterborne microbial... public exposure to microbial pathogens. To qualify for a BEACH Act Grant, a state must submit information...

Exploitation of microbial forensics and nanotechnology for the monitoring of emerging pathogens.

PubMed

Bokhari, Habib

2018-03-07

Emerging infectious diseases remain among the leading causes of global mortality. Traditional laboratory diagnostic approaches designed to detect and track infectious disease agents provide a framework for surveillance of bio threats. However, surveillance and outbreak investigations using such time-consuming approaches for early detection of pathogens remain the major pitfall. Hence, reasonable real-time surveillance systems to anticipate threats to public health and environment are critical for identifying specific aetiologies and preventing the global spread of infectious disease. The current review discusses the growing need for monitoring and surveillance of pathogens with the same zeal and approach as adopted by microbial forensics laboratories, and further strengthening it by integrating with the innovative nanotechnology for rapid detection of microbial pathogens. Such innovative diagnostics platforms will help to track pathogens from high risk areas and environment by pre-emptive approach that will minimize damages. The various scenarios with the examples are discussed where the high risk associated human pathogens in particular were successfully detected using various nanotechnology approaches with potential future prospects in the field of microbial forensics.

A Bayesian evidence synthesis approach to estimate disease prevalence in hard-to-reach populations: hepatitis C in New York City.

PubMed

Tan, Sarah; Makela, Susanna; Heller, Daliah; Konty, Kevin; Balter, Sharon; Zheng, Tian; Stark, James H

2018-06-01

Existing methods to estimate the prevalence of chronic hepatitis C (HCV) in New York City (NYC) are limited in scope and fail to assess hard-to-reach subpopulations with highest risk such as injecting drug users (IDUs). To address these limitations, we employ a Bayesian multi-parameter evidence synthesis model to systematically combine multiple sources of data, account for bias in certain data sources, and provide unbiased HCV prevalence estimates with associated uncertainty. Our approach improves on previous estimates by explicitly accounting for injecting drug use and including data from high-risk subpopulations such as the incarcerated, and is more inclusive, utilizing ten NYC data sources. In addition, we derive two new equations to allow age at first injecting drug use data for former and current IDUs to be incorporated into the Bayesian evidence synthesis, a first for this type of model. Our estimated overall HCV prevalence as of 2012 among NYC adults aged 20-59 years is 2.78% (95% CI 2.61-2.94%), which represents between 124,900 and 140,000 chronic HCV cases. These estimates suggest that HCV prevalence in NYC is higher than previously indicated from household surveys (2.2%) and the surveillance system (2.37%), and that HCV transmission is increasing among young injecting adults in NYC. An ancillary benefit from our results is an estimate of current IDUs aged 20-59 in NYC: 0.58% or 27,600 individuals. Copyright © 2018 Elsevier B.V. All rights reserved.

Bayesian Networks to Compare Pest Control Interventions on Commodities Along Agricultural Production Chains.

PubMed

Holt, J; Leach, A W; Johnson, S; Tu, D M; Nhu, D T; Anh, N T; Quinlan, M M; Whittle, P J L; Mengersen, K; Mumford, J D

2018-02-01

The production of an agricultural commodity involves a sequence of processes: planting/growing, harvesting, sorting/grading, postharvest treatment, packing, and exporting. A Bayesian network has been developed to represent the level of potential infestation of an agricultural commodity by a specified pest along an agricultural production chain. It reflects the dependency of this infestation on the predicted level of pest challenge, the anticipated susceptibility of the commodity to the pest, the level of impact from pest control measures as designed, and any variation from that due to uncertainty in measure efficacy. The objective of this Bayesian network is to facilitate agreement between national governments of the exporters and importers on a set of phytosanitary measures to meet specific phytosanitary measure requirements to achieve target levels of protection against regulated pests. The model can be used to compare the performance of different combinations of measures under different scenarios of pest challenge, making use of available measure performance data. A case study is presented using a model developed for a fruit fly pest on dragon fruit in Vietnam; the model parameters and results are illustrative and do not imply a particular level of fruit fly infestation of these exports; rather, they provide the most likely, alternative, or worst-case scenarios of the impact of measures. As a means to facilitate agreement for trade, the model provides a framework to support communication between exporters and importers about any differences in perceptions of the risk reduction achieved by pest control measures deployed during the commodity production chain. © 2017 Society for Risk Analysis.

Should Perioperative Supplemental Oxygen Be Routinely Recommended for Surgical Patients? A Bayesian Meta-analysis

PubMed Central

Kao, Lillian S.; Millas, Stefanos G.; Pedroza, Claudia; Tyson, Jon E.; Lally, Kevin P.

2012-01-01

Objective The purpose of this study is to use updated data and Bayesian methods to evaluate the effectiveness of hyperoxia to reduce surgical site infections (SSIs) and/or mortality in both colorectal and all surgical patients. Because few trials assessed potential harms of hyperoxia, hazards were not included. Background Use of hyperoxia to reduce SSIs is controversial. Three recent meta-analyses have had conflicting conclusions. Methods A systematic literature search and review were performed. Traditional fixed-effect and random-effects meta-analyses and Bayesian meta-analysis were performed to evaluate SSIs and mortality. Results Traditional meta-analysis yielded a relative risk of an SSI with hyperoxia among all surgery patients of 0.84 (95% confidence interval, CI, 0.73–0.97) and 0.84 (95% CI 0.61–1.16) for the fixed-effect and random effects models respectively. The probabilities of any risk reduction in SSIs among all surgery patients were 77%, 81%, and 83% for skeptical, neutral, and enthusiastic priors. Subset analysis of colorectal surgery patients increased the probabilities to 86%, 89%, and 92%. The probabilities of at least a 10% reduction were 57%, 62%, and 68% for all surgical patients and 71%, 75%, and 80% among the colorectal surgery subset. Conclusions There is a moderately high probability of a benefit to hyperoxia in reducing SSIs in colorectal surgery patients; however, the magnitude of benefit is relatively small and might not exceed treatment hazards. Further studies should focus on generalizability to other patient populations or on treatment hazards and other outcomes. PMID:23160100

Clearing Unexploded Ordnance: Bayesian Methodology for Assessing Success

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

Anderson, K K.

2005-10-30

The Department of Defense has many Formerly Used Defense Sites (FUDS) that are slated for transfer for public use. Some sites have unexploded ordnance (UXO) that must be cleared prior to any land transfers. Sites are characterized using geophysical sensing devices and locations are identified where possible UXO may be located. In practice, based on the analysis of the geophysical surveys, a dig list of N suspect locations is created for a site that is possibly contaminated with UXO. The suspect locations on the dig list are often assigned into K bins ranging from ``most likely to contain UXO" tomore » ``least likely to be UXO" based on signal discrimination techniques and expert judgment. Usually all dig list locations are sampled to determine if UXO is present before the site is determined to be free of UXO. While this method is 100% certain to insure no UXO remains in the locations identified by the signal discrimination and expert judgment, it is very costly. This paper proposes a statistical Bayesian methodology that may result in digging less than 100% of the suspect locations to reach a pre-defined tolerable risk, where risk is defined in terms of a low probability that any UXO remains in the unsampled dig list locations. Two important features of a Bayesian approach are that it can account for uncertainties in model parameters and that it can handle data that becomes available in stages. The results from each stage of data can be used to direct the subsequent digs.« less

Predicting forest insect flight activity: A Bayesian network approach

PubMed Central

Pawson, Stephen M.; Marcot, Bruce G.; Woodberry, Owen G.

2017-01-01

Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model’s predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways. PMID:28953904

Comparison of recreational health risks associated with surfing and swimming in dry weather and post-storm conditions at Southern California beaches using quantitative microbial risk assessment (QMRA).

PubMed

Tseng, Linda Y; Jiang, Sunny C

2012-05-01

Southern California is an increasingly urbanized hotspot for surfing, thus it is of great interest to assess the human illness risks associated with this popular ocean recreational water sport from exposure to fecal bacteria contaminated coastal waters. Quantitative microbial risk assessments were applied to eight popular Southern California beaches using readily available enterococcus and fecal coliform data and dose-response models to compare health risks associated with surfing during dry weather and storm conditions. The results showed that the level of gastrointestinal illness risks from surfing post-storm events was elevated, with the probability of exceeding the US EPA health risk guideline up to 28% of the time. The surfing risk was also elevated in comparison with swimming at the same beach due to ingestion of greater volume of water. The study suggests that refinement of dose-response model, improving monitoring practice and better surfer behavior surveillance will improve the risk estimation. Copyright © 2012 Elsevier Ltd. All rights reserved.

KF-finder: identification of key factors from host-microbial networks in cervical cancer.

PubMed

Hu, Jialu; Gao, Yiqun; Zheng, Yan; Shang, Xuequn

2018-04-24

The human body is colonized by a vast number of microbes. Microbiota can benefit many normal life processes, but can also cause many diseases by interfering the regular metabolism and immune system. Recent studies have demonstrated that the microbial community is closely associated with various types of cell carcinoma. The search for key factors, which also refer to cancer causing agents, can provide an important clue in understanding the regulatory mechanism of microbiota in uterine cervix cancer. In this paper, we investigated microbiota composition and gene expression data for 58 squamous and adenosquamous cell carcinoma. A host-microbial covariance network was constructed based on the 16s rRNA and gene expression data of the samples, which consists of 259 abundant microbes and 738 differentially expressed genes (DEGs). To search for risk factors from host-microbial networks, the method of bi-partite betweenness centrality (BpBC) was used to measure the risk of a given node to a certain biological process in hosts. A web-based tool KF-finder was developed, which can efficiently query and visualize the knowledge of microbiota and differentially expressed genes (DEGs) in the network. Our results suggest that prevotellaceade, tissierellaceae and fusobacteriaceae are the most abundant microbes in cervical carcinoma, and the microbial community in cervical cancer is less diverse than that of any other boy sites in health. A set of key risk factors anaerococcus, hydrogenophilaceae, eubacterium, PSMB10, KCNIP1 and KRT13 have been identified, which are thought to be involved in the regulation of viral response, cell cycle and epithelial cell differentiation in cervical cancer. It can be concluded that permanent changes of microbiota composition could be a major force for chromosomal instability, which subsequently enables the effect of key risk factors in cancer. All our results described in this paper can be freely accessed from our website at http://www.nwpu-bioinformatics.com/KF-finder/ .

Accounting for shared and unshared dosimetric uncertainties in the dose response for ultrasound-detected thyroid nodules after exposure to radioactive fallout.

PubMed

Land, Charles E; Kwon, Deukwoo; Hoffman, F Owen; Moroz, Brian; Drozdovitch, Vladimir; Bouville, André; Beck, Harold; Luckyanov, Nicholas; Weinstock, Robert M; Simon, Steven L

2015-02-01

Dosimetic uncertainties, particularly those that are shared among subgroups of a study population, can bias, distort or reduce the slope or significance of a dose response. Exposure estimates in studies of health risks from environmental radiation exposures are generally highly uncertain and thus, susceptible to these methodological limitations. An analysis was published in 2008 concerning radiation-related thyroid nodule prevalence in a study population of 2,994 villagers under the age of 21 years old between August 1949 and September 1962 and who lived downwind from the Semipalatinsk Nuclear Test Site in Kazakhstan. This dose-response analysis identified a statistically significant association between thyroid nodule prevalence and reconstructed doses of fallout-related internal and external radiation to the thyroid gland; however, the effects of dosimetric uncertainty were not evaluated since the doses were simple point "best estimates". In this work, we revised the 2008 study by a comprehensive treatment of dosimetric uncertainties. Our present analysis improves upon the previous study, specifically by accounting for shared and unshared uncertainties in dose estimation and risk analysis, and differs from the 2008 analysis in the following ways: 1. The study population size was reduced from 2,994 to 2,376 subjects, removing 618 persons with uncertain residence histories; 2. Simulation of multiple population dose sets (vectors) was performed using a two-dimensional Monte Carlo dose estimation method; and 3. A Bayesian model averaging approach was employed for evaluating the dose response, explicitly accounting for large and complex uncertainty in dose estimation. The results were compared against conventional regression techniques. The Bayesian approach utilizes 5,000 independent realizations of population dose vectors, each of which corresponds to a set of conditional individual median internal and external doses for the 2,376 subjects. These 5,000 population dose vectors reflect uncertainties in dosimetric parameters, partly shared and partly independent, among individual members of the study population. Risk estimates for thyroid nodules from internal irradiation were higher than those published in 2008, which results, to the best of our knowledge, from explicitly accounting for dose uncertainty. In contrast to earlier findings, the use of Bayesian methods led to the conclusion that the biological effectiveness for internal and external dose was similar. Estimates of excess relative risk per unit dose (ERR/Gy) for males (177 thyroid nodule cases) were almost 30 times those for females (571 cases) and were similar to those reported for thyroid cancers related to childhood exposures to external and internal sources in other studies. For confirmed cases of papillary thyroid cancers (3 in males, 18 in females), the ERR/Gy was also comparable to risk estimates from other studies, but not significantly different from zero. These findings represent the first reported dose response for a radiation epidemiologic study considering all known sources of shared and unshared errors in dose estimation and using a Bayesian model averaging (BMA) method for analysis of the dose response.

Probabilistic risk assessment for a loss of coolant accident in McMaster Nuclear Reactor and application of reliability physics model for modeling human reliability

NASA Astrophysics Data System (ADS)

Ha, Taesung

A probabilistic risk assessment (PRA) was conducted for a loss of coolant accident, (LOCA) in the McMaster Nuclear Reactor (MNR). A level 1 PRA was completed including event sequence modeling, system modeling, and quantification. To support the quantification of the accident sequence identified, data analysis using the Bayesian method and human reliability analysis (HRA) using the accident sequence evaluation procedure (ASEP) approach were performed. Since human performance in research reactors is significantly different from that in power reactors, a time-oriented HRA model (reliability physics model) was applied for the human error probability (HEP) estimation of the core relocation. This model is based on two competing random variables: phenomenological time and performance time. The response surface and direct Monte Carlo simulation with Latin Hypercube sampling were applied for estimating the phenomenological time, whereas the performance time was obtained from interviews with operators. An appropriate probability distribution for the phenomenological time was assigned by statistical goodness-of-fit tests. The human error probability (HEP) for the core relocation was estimated from these two competing quantities: phenomenological time and operators' performance time. The sensitivity of each probability distribution in human reliability estimation was investigated. In order to quantify the uncertainty in the predicted HEPs, a Bayesian approach was selected due to its capability of incorporating uncertainties in model itself and the parameters in that model. The HEP from the current time-oriented model was compared with that from the ASEP approach. Both results were used to evaluate the sensitivity of alternative huinan reliability modeling for the manual core relocation in the LOCA risk model. This exercise demonstrated the applicability of a reliability physics model supplemented with a. Bayesian approach for modeling human reliability and its potential usefulness of quantifying model uncertainty as sensitivity analysis in the PRA model.

Predicting the microbial exposure risks in urban floods using GIS, building simulation, and microbial models.

PubMed

Taylor, Jonathon; Biddulph, Phillip; Davies, Michael; Lai, Ka man

2013-01-01

London is expected to experience more frequent periods of intense rainfall and tidal surges, leading to an increase in the risk of flooding. Damp and flooded dwellings can support microbial growth, including mould, bacteria, and protozoa, as well as persistence of flood-borne microorganisms. The amount of time flooded dwellings remain damp will depend on the duration and height of the flood, the contents of the flood water, the drying conditions, and the building construction, leading to particular properties and property types being prone to lingering damp and human pathogen growth or persistence. The impact of flooding on buildings can be simulated using Heat Air and Moisture (HAM) models of varying complexity in order to understand how water can be absorbed and dry out of the building structure. This paper describes the simulation of the drying of building archetypes representative of the English building stock using the EnergyPlus based tool 'UCL-HAMT' in order to determine the drying rates of different abandoned structures flooded to different heights and during different seasons. The results are mapped out using GIS in order to estimate the spatial risk across London in terms of comparative flood vulnerability, as well as for specific flood events. Areas of South and East London were found to be particularly vulnerable to long-term microbial exposure following major flood events. Copyright © 2012 Elsevier Ltd. All rights reserved.

Bayesian data analysis for newcomers.

PubMed

Kruschke, John K; Liddell, Torrin M

2018-02-01

This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.

Quantification of microbial risks to human health caused by waterborne viruses and bacteria in an urban slum.

PubMed

Katukiza, A Y; Ronteltap, M; van der Steen, P; Foppen, J W A; Lens, P N L

2014-02-01

To determine the magnitude of microbial risks from waterborne viruses and bacteria in Bwaise III in Kampala (Uganda), a typical slum in Sub-Saharan Africa. A quantitative microbial risk assessment (QMRA) was carried out to determine the magnitude of microbial risks from waterborne pathogens through various exposure pathways in Bwaise III in Kampala (Uganda). This was based on the concentration of Escherichia coli O157:H7, Salmonella spp., rotavirus (RV) and human adenoviruses F and G (HAdV) in spring water, tap water, surface water, grey water and contaminated soil samples. The total disease burden was 680 disability-adjusted life years (DALYs) per 1000 persons per year. The highest disease burden contribution was caused by exposure to surface water open drainage channels (39%) followed by exposure to grey water in tertiary drains (24%), storage containers (22%), unprotected springs (8%), contaminated soil (7%) and tap water (0.02%). The highest percentage of the mean estimated infections was caused by E. coli O157:H7 (41%) followed by HAdV (32%), RV (20%) and Salmonella spp. (7%). In addition, the highest infection risk was 1 caused by HAdV in surface water at the slum outlet, while the lowest infection risk was 2.71 × 10(-6) caused by E. coli O157:H7 in tap water. The results show that the slum environment is polluted, and the disease burden from each of the exposure routes in Bwaise III slum, with the exception of tap water, was much higher than the WHO reference level of tolerable risk of 1 × 10(-6) DALYs per person per year. The findings of this study provide guidance to governments, local authorities and nongovernment organizations in making decisions on measures to reduce infection risk and the disease burden by 10(2) to 10(5) depending on the source of exposure to achieve the desired health impacts. The infection risk may be reduced by sustainable management of human excreta and grey water, coupled with risk communication during hygiene awareness campaigns at household and community level. The data also provide a basis to make strategic investments to improve sanitary conditions in urban slums. © 2013 The Society for Applied Microbiology.

Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk

PubMed Central

Calle, M. Luz; Rothman, Nathaniel; Urrea, Víctor; Kogevinas, Manolis; Petrus, Sandra; Chanock, Stephen J.; Tardón, Adonina; García-Closas, Montserrat; González-Neira, Anna; Vellalta, Gemma; Carrato, Alfredo; Navarro, Arcadi; Lorente-Galdós, Belén; Silverman, Debra T.; Real, Francisco X.; Wu, Xifeng; Malats, Núria

2013-01-01

The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk. PMID:24391818

Psychosocial stress factors, including the relationship with the coach, and their influence on acute and overuse injury risk in elite female football players

PubMed Central

Pensgaard, Anne Marte; Ivarsson, Andreas; Nilstad, Agnethe; Solstad, Bård Erlend; Steffen, Kathrin

2018-01-01

Background The relationship between specific types of stressors (eg, teammates, coach) and acute versus overuse injuries is not well understood. Objective To examine the roles of different types of stressors as well as the effect of motivational climate on the occurrence of acute and overuse injuries. Methods Players in the Norwegian elite female football league (n=193 players from 12 teams) participated in baseline screening tests prior to the 2009 competitive football season. As part of the screening, we included the Life Event Survey for Collegiate Athletes and the Perceived Motivational Climate in Sport Questionnaire (Norwegian short version). Acute and overuse time-loss injuries and exposure to training and matches were recorded prospectively in the football season using weekly text messaging. Data were analysed with Bayesian logistic regression analyses. Results Using Bayesian logistic regression analyses, we showed that perceived negative life event stress from teammates was associated with an increased risk of acute injuries (OR=1.23, 95% credibility interval (1.01 to 1.48)). There was a credible positive association between perceived negative life event stress from the coach and the risk of overuse injuries (OR=1.21, 95% credibility interval (1.01 to 1.45)). Conclusions Players who report teammates as a source of stress have a greater risk of sustaining an acute injury, while players reporting the coach as a source of stress are at greater risk of sustaining an overuse injury. Motivational climate did not relate to increased injury occurrence. PMID:29629182

Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk.

PubMed

Fuster-Parra, P; Tauler, P; Bennasar-Veny, M; Ligęza, A; López-González, A A; Aguiló, A

2016-04-01

An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial importance in the research of cardiovascular disease (CVD) in order to prevent (or reduce) the chance of developing or dying from CVD. The main focus of data analysis is on the use of models able to discover and understand the relationships between different CVRF. In this paper a report on applying Bayesian network (BN) modeling to discover the relationships among thirteen relevant epidemiological features of heart age domain in order to analyze cardiovascular lost years (CVLY), cardiovascular risk score (CVRS), and metabolic syndrome (MetS) is presented. Furthermore, the induced BN was used to make inference taking into account three reasoning patterns: causal reasoning, evidential reasoning, and intercausal reasoning. Application of BN tools has led to discovery of several direct and indirect relationships between different CVRF. The BN analysis showed several interesting results, among them: CVLY was highly influenced by smoking being the group of men the one with highest risk in CVLY; MetS was highly influence by physical activity (PA) being again the group of men the one with highest risk in MetS, and smoking did not show any influence. BNs produce an intuitive, transparent, graphical representation of the relationships between different CVRF. The ability of BNs to predict new scenarios when hypothetical information is introduced makes BN modeling an Artificial Intelligence (AI) tool of special interest in epidemiological studies. As CVD is multifactorial the use of BNs seems to be an adequate modeling tool. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Awareness and Perceptions of Food Safety Risks and Risk Management in Poultry Production and Slaughter: A Qualitative Study of Direct-Market Poultry Producers in Maryland

PubMed Central

Baron, Patrick; Frattaroli, Shannon

2016-01-01

The objective of this study was to document and understand the perceptions and opinions of small-scale poultry producers who market directly to consumers about microbial food safety risks in the poultry supply chain. Between January and November 2014, we conducted semi-structured, in-depth interviews with a convenience sample of 16 owner-operators of Maryland direct-market commercial poultry farms. Three overarching thematic categories emerged from these interviews that describe: 1) characteristics of Maryland direct-market poultry production and processing; 2) microbial food safety risk awareness and risk management in small-scale poultry production, slaughter and processing; and 3) motivations for prioritizing food safety in the statewide direct-market poultry supply chain. Key informants provided valuable insights on many topics relevant to evaluating microbial food safety in the Maryland direct-market poultry supply chain, including: direct-market poultry production and processing practices and models, perspectives on issues related to food safety risk management, perspectives on direct-market agriculture economics and marketing strategies, and ideas for how to enhance food safety at the direct-market level of the Maryland poultry supply chain. The findings have policy implications and provide insights into food safety in small-scale commercial poultry production, processing, distribution and retail. In addition, the findings will inform future food safety research on the small-scale US poultry supply chain. PMID:27341034

Italian multicentre study on microbial environmental contamination in dental clinics: a pilot study.

PubMed

Pasquarella, Cesira; Veronesi, Licia; Castiglia, Paolo; Liguori, Giorgio; Montagna, Maria Teresa; Napoli, Christian; Rizzetto, Rolando; Torre, Ida; Masia, Maria Dolores; Di Onofrio, Valeria; Colucci, Maria Eugenia; Tinteri, Carola; Tanzi, Marialuisa

2010-09-01

The dental practice is associated with a high risk of infections, both for patients and healthcare operators, and the environment may play an important role in the transmission of infectious diseases. A microbiological environmental investigation was carried out in six dental clinics as a pilot study for a larger multicentre study that will be performed by the Italian SItI (Society of Hygiene, Preventive Medicine and Public Health) working group "Hygiene in Dentistry". Microbial contamination of water, air and surfaces was assessed in each clinic during the five working days of the week, before and during treatments. Air and surfaces were also examined at the end of the daily activity. A wide variation was found in microbial environmental contamination, both within the participating clinics and relative to the different sampling times. Microbial water contamination in Dental Unit Water Systems (DUWS) reached values of up to 26x10(4)cfu/mL (colony forming units per millilitre). P. aeruginosa was found in 33% of the sampled DUWS and Legionella spp. in 50%. A significant decrease in the Total Viable Count (TVC) was recorded during the activity. Microbial air contamination showed the highest levels during dental treatments and tended to decrease at the end of the working activity (p<0.05). Microbial buildup on surfaces increased significantly during the working hours. As these findings point out, research on microbial environmental contamination and the related risk factors in dental clinics should be expanded and should also be based on larger collections of data, in order to provide the essential knowledge aimed at targeted preventive interventions. Copyright 2010 Elsevier B.V. All rights reserved.

Modelling the impact of future socio-economic and climate change scenarios on river microbial water quality.

PubMed

Islam, M M Majedul; Iqbal, Muhammad Shahid; Leemans, Rik; Hofstra, Nynke

2018-03-01

Microbial surface water quality is important, as it is related to health risk when the population is exposed through drinking, recreation or consumption of irrigated vegetables. The microbial surface water quality is expected to change with socio-economic development and climate change. This study explores the combined impacts of future socio-economic and climate change scenarios on microbial water quality using a coupled hydrodynamic and water quality model (MIKE21FM-ECOLab). The model was applied to simulate the baseline (2014-2015) and future (2040s and 2090s) faecal indicator bacteria (FIB: E. coli and enterococci) concentrations in the Betna river in Bangladesh. The scenarios comprise changes in socio-economic variables (e.g. population, urbanization, land use, sanitation and sewage treatment) and climate variables (temperature, precipitation and sea-level rise). Scenarios have been developed building on the most recent Shared Socio-economic Pathways: SSP1 and SSP3 and Representative Concentration Pathways: RCP4.5 and RCP8.5 in a matrix. An uncontrolled future results in a deterioration of the microbial water quality (+75% by the 2090s) due to socio-economic changes, such as higher population growth, and changes in rainfall patterns. However, microbial water quality improves under a sustainable scenario with improved sewage treatment (-98% by the 2090s). Contaminant loads were more influenced by changes in socio-economic factors than by climatic change. To our knowledge, this is the first study that combines climate change and socio-economic development scenarios to simulate the future microbial water quality of a river. This approach can also be used to assess future consequences for health risks. Copyright © 2017 The Authors. Published by Elsevier GmbH.. All rights reserved.

THE IMPORTANCE OF RISK COMMUNICATION

EPA Science Inventory

The goal of environmental and public health is to reduce the health risks associated with microbial and toxic agents in the environment, and also to agents of injury. There have generally been three approaches to managing these risks: first, control releases of the agent to the e...

A SAFE consortium position paper: Update on microbial safety of fresh produce

USDA-ARS?s Scientific Manuscript database

Surveys of fresh produce demonstrate potential to become contaminated with pathogenic microorganisms. The analysis of microbiological risk is generally divided into three categories: Risk Assessment identifies the factors that contribute to a problem; Risk Management identifies ways to solve a probl...

Evidence Report: Risk of Adverse Health Effects Due to Host-Microorganism Interactions

NASA Technical Reports Server (NTRS)

Ott, C. Mark; Oubre, Cherie; Wallace, Sarah; Mehta, Satish; Pierson, Duane

2016-01-01

While preventive measures limit the presence of many medically significant microorganisms during spaceflight missions, microbial infection of crewmembers cannot be completely prevented. Spaceflight experiments over the past 50 years have demonstrated a unique microbial response to spaceflight culture, although the mechanisms behind those responses and their operational relevance were unclear. In 2007, the operational importance of these microbial responses was emphasized as the results of an experiment aboard STS-115 demonstrated that the enteric pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium) increased in virulence in a murine model of infection. The experiment was reproduced in 2008 aboard STS-123 confirming this finding. In response to these findings, the Institute of Medicine of the National Academies recommended that NASA investigate this risk and its potential impact on the health of the crew during spaceflight. NASA assigned this risk to the Human Research Program. To better understand this risk, evidence has been collected and reported from both spaceflight analog systems and actual spaceflight including Mir, Space Shuttle, and ISS missions. Although the performance of virulence studies during spaceflight are challenging and often impractical, additional information has been and continues to be collected to better understand the risk to crew health. Still, the uncertainty concerning the extent and severity of these alterations in host-microorganism interactions is very large and requires more investigation as the focus of human spaceflight shifts to longer-duration exploration class missions.

A regional-scale ecological risk framework for environmental flow evaluations

NASA Astrophysics Data System (ADS)

O'Brien, Gordon C.; Dickens, Chris; Hines, Eleanor; Wepener, Victor; Stassen, Retha; Quayle, Leo; Fouchy, Kelly; MacKenzie, James; Graham, P. Mark; Landis, Wayne G.

2018-02-01

Environmental flow (E-flow) frameworks advocate holistic, regional-scale, probabilistic E-flow assessments that consider flow and non-flow drivers of change in a socio-ecological context as best practice. Regional-scale ecological risk assessments of multiple stressors to social and ecological endpoints, which address ecosystem dynamism, have been undertaken internationally at different spatial scales using the relative-risk model since the mid-1990s. With the recent incorporation of Bayesian belief networks into the relative-risk model, a robust regional-scale ecological risk assessment approach is available that can contribute to achieving the best practice recommendations of E-flow frameworks. PROBFLO is a holistic E-flow assessment method that incorporates the relative-risk model and Bayesian belief networks (BN-RRM) into a transparent probabilistic modelling tool that addresses uncertainty explicitly. PROBFLO has been developed to evaluate the socio-ecological consequences of historical, current and future water resource use scenarios and generate E-flow requirements on regional spatial scales. The approach has been implemented in two regional-scale case studies in Africa where its flexibility and functionality has been demonstrated. In both case studies the evidence-based outcomes facilitated informed environmental management decision making, with trade-off considerations in the context of social and ecological aspirations. This paper presents the PROBFLO approach as applied to the Senqu River catchment in Lesotho and further developments and application in the Mara River catchment in Kenya and Tanzania. The 10 BN-RRM procedural steps incorporated in PROBFLO are demonstrated with examples from both case studies. PROBFLO can contribute to the adaptive management of water resources and contribute to the allocation of resources for sustainable use of resources and address protection requirements.

A Bayesian Model and Stochastic Exposure (Dose) Estimation for Relative Exposure Risk Comparison Involving Asbestos-Containing Dropped Ceiling Panel Installation and Maintenance Tasks.

PubMed

Boelter, Fred W; Xia, Yulin; Persky, Jacob D

2017-09-01

Assessing exposures to hazards in order to characterize risk is at the core of occupational hygiene. Our study examined dropped ceiling systems commonly used in schools and commercial buildings and lay-in ceiling panels that may have contained asbestos prior to the mid to late 1970s. However, most ceiling panels and tiles do not contain asbestos. Since asbestos risk relates to dose, we estimated the distribution of eight-hour TWA concentrations and one-year exposures (a one-year dose equivalent) to asbestos fibers (asbestos f/cc-years) for five groups of workers who may encounter dropped ceilings: specialists, generalists, maintenance workers, nonprofessional do-it-yourself (DIY) persons, and other tradespersons who are bystanders to ceiling work. Concentration data (asbestos f/cc) were obtained through two exposure assessment studies in the field and one chamber study. Bayesian and stochastic models were applied to estimate distributions of eight-hour TWAs and annual exposures (dose). The eight-hour TWAs for all work categories were below current and historic occupational exposure limits (OELs). Exposures to asbestos fibers from dropped ceiling work would be categorized as "highly controlled" for maintenance workers and "well controlled" for remaining work categories, according to the American Industrial Hygiene Association exposure control rating system. Annual exposures (dose) were found to be greatest for specialists, followed by maintenance workers, generalists, bystanders, and DIY. On a comparative basis, modeled dose and thus risk from dropped ceilings for all work categories were orders of magnitude lower than published exposures for other sources of banned friable asbestos-containing building material commonly encountered in construction trades. © 2016 The Authors Risk Analysis published by Wiley Periodicals, Inc. on behalf of Society for Risk Analysis.

A Bayesian framework for early risk prediction in traumatic brain injury

NASA Astrophysics Data System (ADS)

Chaganti, Shikha; Plassard, Andrew J.; Wilson, Laura; Smith, Miya A.; Patel, Mayur B.; Landman, Bennett A.

2016-03-01

Early detection of risk is critical in determining the course of treatment in traumatic brain injury (TBI). Computed tomography (CT) acquired at admission has shown latent prognostic value in prior studies; however, no robust clinical risk predictions have been achieved based on the imaging data in large-scale TBI analysis. The major challenge lies in the lack of consistent and complete medical records for patients, and an inherent bias associated with the limited number of patients samples with high-risk outcomes in available TBI datasets. Herein, we propose a Bayesian framework with mutual information-based forward feature selection to handle this type of data. Using multi-atlas segmentation, 154 image-based features (capturing intensity, volume and texture) were computed over 22 ROIs in 1791 CT scans. These features were combined with 14 clinical parameters and converted into risk likelihood scores using Bayes modeling. We explore the prediction power of the image features versus the clinical measures for various risk outcomes. The imaging data alone were more predictive of outcomes than the clinical data (including Marshall CT classification) for discharge disposition with an area under the curve of 0.81 vs. 0.67, but less predictive than clinical data for discharge Glasgow Coma Scale (GCS) score with an area under the curve of 0.65 vs. 0.85. However, in both cases, combining imaging and clinical data increased the combined area under the curve with 0.86 for discharge disposition and 0.88 for discharge GCS score. In conclusion, CT data have meaningful prognostic value for TBI patients beyond what is captured in clinical measures and the Marshall CT classification.

A methodology for risk analysis based on hybrid Bayesian networks: application to the regasification system of liquefied natural gas onboard a floating storage and regasification unit.

PubMed

Martins, Marcelo Ramos; Schleder, Adriana Miralles; Droguett, Enrique López

2014-12-01

This article presents an iterative six-step risk analysis methodology based on hybrid Bayesian networks (BNs). In typical risk analysis, systems are usually modeled as discrete and Boolean variables with constant failure rates via fault trees. Nevertheless, in many cases, it is not possible to perform an efficient analysis using only discrete and Boolean variables. The approach put forward by the proposed methodology makes use of BNs and incorporates recent developments that facilitate the use of continuous variables whose values may have any probability distributions. Thus, this approach makes the methodology particularly useful in cases where the available data for quantification of hazardous events probabilities are scarce or nonexistent, there is dependence among events, or when nonbinary events are involved. The methodology is applied to the risk analysis of a regasification system of liquefied natural gas (LNG) on board an FSRU (floating, storage, and regasification unit). LNG is becoming an important energy source option and the world's capacity to produce LNG is surging. Large reserves of natural gas exist worldwide, particularly in areas where the resources exceed the demand. Thus, this natural gas is liquefied for shipping and the storage and regasification process usually occurs at onshore plants. However, a new option for LNG storage and regasification has been proposed: the FSRU. As very few FSRUs have been put into operation, relevant failure data on FSRU systems are scarce. The results show the usefulness of the proposed methodology for cases where the risk analysis must be performed under considerable uncertainty. © 2014 Society for Risk Analysis.

The interplay of stressful life events and coping skills on risk for suicidal behavior among youth students in contemporary China: a large scale cross-sectional study.

PubMed

Tang, Fang; Xue, Fuzhong; Qin, Ping

2015-07-31

Stressful life events are common among youth students and may induce psychological problems and even suicidal behaviors in those with poor coping skills. This study aims to assess the influence of stressful life events and coping skills on risk for suicidal behavior and to elucidate the underlying mechanism using a large sample of university students in China. 5972 students, randomly selected from 6 universities, completed the questionnaire survey. Logistic regression analysis was performed to estimate the effect of stressful life events and coping skills on risk for suicidal behavior. Bayesian network was further adopted to probe their probabilistic relationships. Of the 5972 students, 7.64% reported the presence of suicidal behavior (attempt or ideation) within the past one year period. Stressful life events such as strong conflicts with classmates and a failure in study exam constituted strong risk factors for suicidal behavior. The influence of coping skills varied according to the strategies adapted toward problems with a high score of approach coping skills significantly associated with a reduced risk of suicidal behavior. The Bayesian network indicated that the probability of suicidal behavior associated with specific life events was to a large extent conditional on coping skills. For instance, a stressful experience of having strong conflicts with classmates could result in a probability of suicidal behavior of 21.25% and 15.36% respectively, for female and male students with the score of approach coping skills under the average. Stressful life events and deficient coping skills are strong risk factors for suicidal behavior among youth students. The results underscore the importance of prevention efforts to improve coping skills towards stressful life events.

Modeling the risk of phosphorus runoff following single and split phosphorus fertilizer applications in two contrasting catchments.

PubMed

Burkitt, Lucy L; Dougherty, Warwick J; Corkrey, Ross; Broad, Shane T

2011-01-01

The potential loss of P in runoff is a function of the combined effects of fertilizer-soil interactions and climatic characteristics. In this study, we applied a Bayesian approach to experimental data to model the annualized long-term risk of P runoff following single and split P fertilizer applications using two example catchments with contrasting rainfall/runoff patterns. Split P fertilizer strategies are commonly used in intensive pasture production in Australia and our results showed that three applications of 13.3 kg P ha(-1) resulted in a greater risk of P runoff compared with a single application of 40 kg P ha(-1) when long-term surface runoff data were incorporated into a Bayesian P risk model. Splitting P fertilizer applications increased the likelihood of a coincidence of fertilizer application and runoff occurring. We found that the overall risk of P runoff is also increased in catchments where the rainfall/runoff pattern is less predictable, compared with catchments where rainfall/runoff is winter dominant. The findings of our study also question the effectiveness of current recommendations to avoid applying fertilizer if runoff is likely to occur in the next few days, as we found that total P concentrations at the half-life were still very high (18.2 and 8.2 mg P L(-1)) following single and split P treatments, respectively. Data from the current study also highlight that omitting P fertilizer on soils that already have adequate soil test P concentrations is an effective method of reducing P loss in surface runoff. If P fertilizer must be applied, we recommend less frequent applications and only during periods of the year when the risk of surface P runoff is low.

Adversarial risk analysis with incomplete information: a level-k approach.

PubMed

Rothschild, Casey; McLay, Laura; Guikema, Seth

2012-07-01

This article proposes, develops, and illustrates the application of level-k game theory to adversarial risk analysis. Level-k reasoning, which assumes that players play strategically but have bounded rationality, is useful for operationalizing a Bayesian approach to adversarial risk analysis. It can be applied in a broad class of settings, including settings with asynchronous play and partial but incomplete revelation of early moves. Its computational and elicitation requirements are modest. We illustrate the approach with an application to a simple defend-attack model in which the defender's countermeasures are revealed with a probability less than one to the attacker before he decides on how or whether to attack. © 2011 Society for Risk Analysis.

Application of quantitative microbial risk assessments for estimation of risk management metrics: Clostridium perfringens in ready-to-eat and partially cooked meat and poultry products as an example.

PubMed

Crouch, Edmund A; Labarre, David; Golden, Neal J; Kause, Janell R; Dearfield, Kerry L

2009-10-01

The U.S. Department of Agriculture, Food Safety and Inspection Service is exploring quantitative risk assessment methodologies to incorporate the use of the Codex Alimentarius' newly adopted risk management metrics (e.g., food safety objectives and performance objectives). It is suggested that use of these metrics would more closely tie the results of quantitative microbial risk assessments (QMRAs) to public health outcomes. By estimating the food safety objective (the maximum frequency and/or concentration of a hazard in a food at the time of consumption) and the performance objective (the maximum frequency and/or concentration of a hazard in a food at a specified step in the food chain before the time of consumption), risk managers will have a better understanding of the appropriate level of protection (ALOP) from microbial hazards for public health protection. We here demonstrate a general methodology that allows identification of an ALOP and evaluation of corresponding metrics at appropriate points in the food chain. It requires a two-dimensional probabilistic risk assessment, the example used being the Monte Carlo QMRA for Clostridium perfringens in ready-to eat and partially cooked meat and poultry products, with minor modifications to evaluate and abstract required measures. For demonstration purposes, the QMRA model was applied specifically to hot dogs produced and consumed in the United States. Evaluation of the cumulative uncertainty distribution for illness rate allows a specification of an ALOP that, with defined confidence, corresponds to current industry practices.

Hierarchical Bayesian Spatio–Temporal Analysis of Climatic and Socio–Economic Determinants of Rocky Mountain Spotted Fever

PubMed Central

Raghavan, Ram K.; Goodin, Douglas G.; Neises, Daniel; Anderson, Gary A.; Ganta, Roman R.

2016-01-01

This study aims to examine the spatio-temporal dynamics of Rocky Mountain spotted fever (RMSF) prevalence in four contiguous states of Midwestern United States, and to determine the impact of environmental and socio–economic factors associated with this disease. Bayesian hierarchical models were used to quantify space and time only trends and spatio–temporal interaction effect in the case reports submitted to the state health departments in the region. Various socio–economic, environmental and climatic covariates screened a priori in a bivariate procedure were added to a main–effects Bayesian model in progressive steps to evaluate important drivers of RMSF space-time patterns in the region. Our results show a steady increase in RMSF incidence over the study period to newer geographic areas, and the posterior probabilities of county-specific trends indicate clustering of high risk counties in the central and southern parts of the study region. At the spatial scale of a county, the prevalence levels of RMSF is influenced by poverty status, average relative humidity, and average land surface temperature (>35°C) in the region, and the relevance of these factors in the context of climate–change impacts on tick–borne diseases are discussed. PMID:26942604

Hierarchical Bayesian Spatio-Temporal Analysis of Climatic and Socio-Economic Determinants of Rocky Mountain Spotted Fever.

PubMed

Raghavan, Ram K; Goodin, Douglas G; Neises, Daniel; Anderson, Gary A; Ganta, Roman R

2016-01-01

This study aims to examine the spatio-temporal dynamics of Rocky Mountain spotted fever (RMSF) prevalence in four contiguous states of Midwestern United States, and to determine the impact of environmental and socio-economic factors associated with this disease. Bayesian hierarchical models were used to quantify space and time only trends and spatio-temporal interaction effect in the case reports submitted to the state health departments in the region. Various socio-economic, environmental and climatic covariates screened a priori in a bivariate procedure were added to a main-effects Bayesian model in progressive steps to evaluate important drivers of RMSF space-time patterns in the region. Our results show a steady increase in RMSF incidence over the study period to newer geographic areas, and the posterior probabilities of county-specific trends indicate clustering of high risk counties in the central and southern parts of the study region. At the spatial scale of a county, the prevalence levels of RMSF is influenced by poverty status, average relative humidity, and average land surface temperature (>35°C) in the region, and the relevance of these factors in the context of climate-change impacts on tick-borne diseases are discussed.

Bayesian analysis of physiologically based toxicokinetic and toxicodynamic models.

PubMed

Hack, C Eric

2006-04-17

Physiologically based toxicokinetic (PBTK) and toxicodynamic (TD) models of bromate in animals and humans would improve our ability to accurately estimate the toxic doses in humans based on available animal studies. These mathematical models are often highly parameterized and must be calibrated in order for the model predictions of internal dose to adequately fit the experimentally measured doses. Highly parameterized models are difficult to calibrate and it is difficult to obtain accurate estimates of uncertainty or variability in model parameters with commonly used frequentist calibration methods, such as maximum likelihood estimation (MLE) or least squared error approaches. The Bayesian approach called Markov chain Monte Carlo (MCMC) analysis can be used to successfully calibrate these complex models. Prior knowledge about the biological system and associated model parameters is easily incorporated in this approach in the form of prior parameter distributions, and the distributions are refined or updated using experimental data to generate posterior distributions of parameter estimates. The goal of this paper is to give the non-mathematician a brief description of the Bayesian approach and Markov chain Monte Carlo analysis, how this technique is used in risk assessment, and the issues associated with this approach.

Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics

NASA Astrophysics Data System (ADS)

Michelioudakis, Dimitrios G.; Hobbs, Richard W.; Caiado, Camila C. S.

2018-03-01

Estimating the depths of target horizons from seismic reflection data is an important task in exploration geophysics. To constrain these depths we need a reliable and accurate velocity model. Here, we build an optimum 2D seismic reflection data processing flow focused on pre - stack deghosting filters and velocity model building and apply Bayesian methods, including Gaussian process emulation and Bayesian History Matching (BHM), to estimate the uncertainties of the depths of key horizons near the borehole DSDP-258 located in the Mentelle Basin, south west of Australia, and compare the results with the drilled core from that well. Following this strategy, the tie between the modelled and observed depths from DSDP-258 core was in accordance with the ± 2σ posterior credibility intervals and predictions for depths to key horizons were made for the two new drill sites, adjacent the existing borehole of the area. The probabilistic analysis allowed us to generate multiple realizations of pre-stack depth migrated images, these can be directly used to better constrain interpretation and identify potential risk at drill sites. The method will be applied to constrain the drilling targets for the upcoming International Ocean Discovery Program (IODP), leg 369.

Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics

NASA Astrophysics Data System (ADS)

Michelioudakis, Dimitrios G.; Hobbs, Richard W.; Caiado, Camila C. S.

2018-06-01

Estimating the depths of target horizons from seismic reflection data is an important task in exploration geophysics. To constrain these depths we need a reliable and accurate velocity model. Here, we build an optimum 2-D seismic reflection data processing flow focused on pre-stack deghosting filters and velocity model building and apply Bayesian methods, including Gaussian process emulation and Bayesian History Matching, to estimate the uncertainties of the depths of key horizons near the Deep Sea Drilling Project (DSDP) borehole 258 (DSDP-258) located in the Mentelle Basin, southwest of Australia, and compare the results with the drilled core from that well. Following this strategy, the tie between the modelled and observed depths from DSDP-258 core was in accordance with the ±2σ posterior credibility intervals and predictions for depths to key horizons were made for the two new drill sites, adjacent to the existing borehole of the area. The probabilistic analysis allowed us to generate multiple realizations of pre-stack depth migrated images, these can be directly used to better constrain interpretation and identify potential risk at drill sites. The method will be applied to constrain the drilling targets for the upcoming International Ocean Discovery Program, leg 369.

Inferring Alcoholism SNPs and Regulatory Chemical Compounds Based on Ensemble Bayesian Network.

PubMed

Chen, Huan; Sun, Jiatong; Jiang, Hong; Wang, Xianyue; Wu, Lingxiang; Wu, Wei; Wang, Qh

2017-01-01

The disturbance of consciousness is one of the most common symptoms of those have alcoholism and may cause disability and mortality. Previous studies indicated that several single nucleotide polymorphisms (SNP) increase the susceptibility of alcoholism. In this study, we utilized the Ensemble Bayesian Network (EBN) method to identify causal SNPs of alcoholism based on the verified GAW14 data. We built a Bayesian network combining random process and greedy search by using Genetic Analysis Workshop 14 (GAW14) dataset to establish EBN of SNPs. Then we predicted the association between SNPs and alcoholism by determining Bayes' prior probability. Thirteen out of eighteen SNPs directly connected with alcoholism were found concordance with potential risk regions of alcoholism in OMIM database. As many SNPs were found contributing to alteration on gene expression, known as expression quantitative trait loci (eQTLs), we further sought to identify chemical compounds acting as regulators of alcoholism genes captured by causal SNPs. Chloroprene and valproic acid were identified as the expression regulators for genes C11orf66 and SALL3 which were captured by alcoholism SNPs, respectively. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Statistical surrogate models for prediction of high-consequence climate change.

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

Constantine, Paul; Field, Richard V., Jr.; Boslough, Mark Bruce Elrick

2011-09-01

In safety engineering, performance metrics are defined using probabilistic risk assessments focused on the low-probability, high-consequence tail of the distribution of possible events, as opposed to best estimates based on central tendencies. We frame the climate change problem and its associated risks in a similar manner. To properly explore the tails of the distribution requires extensive sampling, which is not possible with existing coupled atmospheric models due to the high computational cost of each simulation. We therefore propose the use of specialized statistical surrogate models (SSMs) for the purpose of exploring the probability law of various climate variables of interest.more » A SSM is different than a deterministic surrogate model in that it represents each climate variable of interest as a space/time random field. The SSM can be calibrated to available spatial and temporal data from existing climate databases, e.g., the Program for Climate Model Diagnosis and Intercomparison (PCMDI), or to a collection of outputs from a General Circulation Model (GCM), e.g., the Community Earth System Model (CESM) and its predecessors. Because of its reduced size and complexity, the realization of a large number of independent model outputs from a SSM becomes computationally straightforward, so that quantifying the risk associated with low-probability, high-consequence climate events becomes feasible. A Bayesian framework is developed to provide quantitative measures of confidence, via Bayesian credible intervals, in the use of the proposed approach to assess these risks.« less

Risk based adaptation of infrastructures to floods and storm surges induced by climate change.

NASA Astrophysics Data System (ADS)

Luna, Byron Quan; Garrè, Luca; Hansen, Peter Friis

2014-05-01

Coastal natural hazards are changing in frequency and intensity associated to climate change. These extreme events combined with an increase in the extent of vulnerable societies will lead to an increase of substantial monetary losses. For this reason, adaptive measures are required to identify the effective and adequate measures to withstand the impacts of climate change. Decision strategies are needed for the timing of investments and for the allocation of resources to safeguard the future in a sustainable manner. Adapting structures to climate change requires decision making under uncertainties. Therefore, it is vital that risk assessments are generated on a reliable and appropriate evaluation of the involved uncertainties. Linking a Bayesian network (BN) to a Geographic Information System (GIS) for a risk assessment enables to model all the relevant parameters, their causal relations and the involved uncertainties. The integration of the probabilistic approach into a GIS allows quantifying and visualizing uncertainties in a spatial manner. By addressing these uncertainties, the Bayesian Network approach allows quantifying their effects; and facilitates the identification of future model improvements and where other efforts should be concentrated. The final results can be applied as a supportive tool for presenting reliable risk assessments to decision-makers. Based on this premises, a case study was performed to assess how the storm surge magnitude and flooding extent of an event with similar characteristics to the Sandy Super storm will occur in 2050 and 2090.

KSC-2014-4900

NASA Image and Video Library

2014-10-31

CAPE CANAVERAL, Fla. – A researcher at NASA’s Kennedy Space Center in Florida checks a reading on the Dust Atmospheric Recovery Technology, or DART, spacecraft inside a laboratory at the Space Life Sciences Lab. DART will characterize the dust loading and microbial diversity in the atmosphere over Florida during summer months with a special emphasis on their interactions during an African dust storm. DART will be used to collect atmospheric aerosols and suspended microbial cells over Florida and Kennedy. Results will help predict the risks of excessive microbial contamination adhering to spacecraft surfaces. Photo credit: NASA/Dimitri Gerondidakis

KSC-2014-4901

NASA Image and Video Library

2014-10-31

CAPE CANAVERAL, Fla. – A researcher at NASA’s Kennedy Space Center in Florida checks a reading on the Dust Atmospheric Recovery Technology, or DART, spacecraft inside a laboratory at the Space Life Sciences Lab. DART will characterize the dust loading and microbial diversity in the atmosphere over Florida during summer months with a special emphasis on their interactions during an African dust storm. DART will be used to collect atmospheric aerosols and suspended microbial cells over Florida and Kennedy. Results will help predict the risks of excessive microbial contamination adhering to spacecraft surfaces. Photo credit: NASA/Dimitri Gerondidakis

KSC-2014-4903

NASA Image and Video Library

2014-10-31

CAPE CANAVERAL, Fla. – Researchers at NASA’s Kennedy Space Center in Florida check readings on the Dust Atmospheric Recovery Technology, or DART, spacecraft inside a laboratory at the Space Life Sciences Lab. DART will characterize the dust loading and microbial diversity in the atmosphere over Florida during summer months with a special emphasis on their interactions during an African dust storm. DART will be used to collect atmospheric aerosols and suspended microbial cells over Florida and Kennedy. Results will help predict the risks of excessive microbial contamination adhering to spacecraft surfaces. Photo credit: NASA/Dimitri Gerondidakis

KSC-2014-4902

NASA Image and Video Library

2014-10-31

CAPE CANAVERAL, Fla. – Researchers at NASA’s Kennedy Space Center in Florida check readings on the Dust Atmospheric Recovery Technology, or DART, spacecraft inside a laboratory at the Space Life Sciences Lab. DART will characterize the dust loading and microbial diversity in the atmosphere over Florida during summer months with a special emphasis on their interactions during an African dust storm. DART will be used to collect atmospheric aerosols and suspended microbial cells over Florida and Kennedy. Results will help predict the risks of excessive microbial contamination adhering to spacecraft surfaces. Photo credit: NASA/Dimitri Gerondidakis

KSC-2014-4899

NASA Image and Video Library

2014-10-31

CAPE CANAVERAL, Fla. – The Dust Atmospheric Recovery Technology, or DART, spacecraft is being assembled in a laboratory inside the Space Life Sciences Lab at NASA’s Kennedy Space Center in Florida. DART will characterize the dust loading and microbial diversity in the atmosphere over Florida during summer months with a special emphasis on their interactions during an African dust storm. DART will be used to collect atmospheric aerosols and suspended microbial cells over Florida and Kennedy. Results will help predict the risks of excessive microbial contamination adhering to spacecraft surfaces. Photo credit: NASA/Dimitri Gerondidakis

KSC-2014-4898

NASA Image and Video Library

2014-10-31

CAPE CANAVERAL, Fla. – The Dust Atmospheric Recovery Technology, or DART, spacecraft is being assembled in a laboratory inside the Space Life Sciences Lab at NASA’s Kennedy Space Center in Florida. DART will characterize the dust loading and microbial diversity in the atmosphere over Florida during summer months with a special emphasis on their interactions during an African dust storm. DART will be used to collect atmospheric aerosols and suspended microbial cells over Florida and Kennedy. Results will help predict the risks of excessive microbial contamination adhering to spacecraft surfaces. Photo credit: NASA/Dimitri Gerondidakis

DART Employees at Work

NASA Image and Video Library

2014-10-31

A researcher from the University of Florida in Gainesville, checks the Dust Atmospheric Recovery Technology, or DART, spacecraft in a laboratory inside the Space Life Sciences Lab at NASA’s Kennedy Space Center in Florida. DART will characterize the dust loading and microbial diversity in the atmosphere over Florida during summer months with a special emphasis on their interactions during an African dust storm. DART will be used to collect atmospheric aerosols and suspended microbial cells over Florida and Kennedy. Results will help predict the risks of excessive microbial contamination adhering to spacecraft surfaces.

Fire risk in San Diego County, California: A weighted Bayesian model approach

USGS Publications Warehouse

Kolden, Crystal A.; Weigel, Timothy J.

2007-01-01

Fire risk models are widely utilized to mitigate wildfire hazards, but models are often based on expert opinions of less understood fire-ignition and spread processes. In this study, we used an empirically derived weights-of-evidence model to assess what factors produce fire ignitions east of San Diego, California. We created and validated a dynamic model of fire-ignition risk based on land characteristics and existing fire-ignition history data, and predicted ignition risk for a future urbanization scenario. We then combined our empirical ignition-risk model with a fuzzy fire behavior-risk model developed by wildfire experts to create a hybrid model of overall fire risk. We found that roads influence fire ignitions and that future growth will increase risk in new rural development areas. We conclude that empirically derived risk models and hybrid models offer an alternative method to assess current and future fire risk based on management actions.

Microbial risk assessment in heterogeneous aquifers: 1. Pathogen transport

NASA Astrophysics Data System (ADS)

Molin, S.; Cvetkovic, V.

2010-05-01

Pathogen transport in heterogeneous aquifers is investigated for microbial risk assessment. A point source with time-dependent input of pathogens is assumed, exemplified as a simple on-site sanitation installation, intermingled with water supply wells. Any pathogen transmission pathway (realization) to the receptor from a postulated infection hazard is viewed as a random event, with the hydraulic conductivity varying spatially. For aquifers where VAR[lnK] < 1 and the integral scale is finite, we provide relatively simple semianalytical expressions for pathogen transport that incorporate the colloid filtration theory. We test a wide range of Damkohler numbers in order to assess the significance of rate limitations on the aquifer barrier function. Even slow immobile inactivation may notably affect the retention of pathogens. Analytical estimators for microbial peak discharge are evaluated and are shown to be applicable using parameters representative of rotavirus and Hepatitis A with input of 10-20 days duration.

The Influence of Microbiology on Spacecraft Design and Controls: A Historical Perspective of the Shuttle and International Space Station Programs

NASA Technical Reports Server (NTRS)

Castro, Victoria A.; Bruce, Rebekah J.; Ott, C. Mark; Pierson, D. L.

2006-01-01

For over 40 years, NASA has been putting humans safely into space in part by minimizing microbial risks to crew members. Success of the program to minimize such risks has resulted from a combination of engineering and design controls as well as active monitoring of the crew, food, water, hardware, and spacecraft interior. The evolution of engineering and design controls is exemplified by the implementation of HEPA filters for air treatment, antimicrobial surface materials, and the disinfection regimen currently used on board the International Space Station. Data from spaceflight missions confirm the effectiveness of current measures; however, fluctuations in microbial concentrations and trends in contamination events suggest the need for continued diligence in monitoring and evaluation as well as further improvements in engineering systems. The knowledge of microbial controls and monitoring from assessments of past missions will be critical in driving the design of future spacecraft.

Modelling the fate of PAH added with composts in amended soil according to the origin of the exogenous organic matter.

PubMed

Brimo, Khaled; Ouvrard, Stéphanie; Houot, Sabine; Lafolie, François; Garnier, Patricia

2018-03-01

A new model that was able to simulate the behaviours of polycyclic aromatic hydrocarbons (PAH) during composting and after the addition of the composts to agricultural soil is presented here. This model associates modules that describe the physical, biological and biochemical processes involved in PAH dynamics in soils, along with a module describing the compost degradation resulting in PAH release. The model was calibrated from laboratory incubations using three 14 C-PAHs, phenanthrene, fluoranthene and benzo(a)pyrene, and three different composts consisting of two mature and one non-mature composts. First, the labelled PAHs were added to the compost over 28days, and spiked composts were then added to the soil over 55days. The model calculates the proportion of biogenic and physically bound residues in the non-extractable compartment of PAHs at the end of the compost incubation to feed the initial conditions of the model for soil amended with composts. For most of the treatments, a single parameter set enabled to simulate the observed dynamics of PAHs adequately for all the amended soil treatments using a Bayesian approach. However, for fluoranthene, different parameters that were able to simulate the growth of a specific microbial biomass had to be considered for mature compost. Processes that occurred before the compost application to the soil strongly influenced the fate of PAHs in the soil. Our results showed that the PAH dissipation during compost incubation was higher in mature composts because of the higher specific microbial activity, while the PAH dissipation in amended soil was higher in the non-mature compost because of the higher availability of PAHs and the higher co-metabolic microbial activity. Copyright © 2017 Elsevier B.V. All rights reserved.

Bacterial Contamination of Hands Increases Risk of Cross-Contamination among Low-Income Puerto Rican Meal Preparers

ERIC Educational Resources Information Center

Dharod, Jigna Morarji; Paciello, Stefania; Bermudez-Millan, Angela; Venkitanarayanan, Kumar; Damio, Grace; Perez-Escamilla, Rafael

2009-01-01

Objective: To examine the association of microbial contamination of the meal preparer's hands with microbial status of food and kitchen/utensil surfaces during home preparation of a "Chicken and Salad" meal. Design and Setting: Observational home food safety assessment. Before starting meal preparation, participants' hands were tested to…

Quantitative Microbial Risk Assessment Tutorial: Navigate the SDMPB and Identify an 8-digit HUC of Interest

EPA Science Inventory

This tutorial reviews some of the screens, icons, and basic functions of the SDMProjectBuilder (SDMPB) that allow a user to identify a watershed of interest that can be used to choose a pour point or 12-digit HUC (HUC-12) for a microbial assessment. It demonstrates how to identif...

Microbial Transformation of Triadimefon to Triadimenol in Soils: Selective Production Rates of Triadimenol Stereoisomers Affect Exposure and Risk

EPA Science Inventory

The microbial transformation of triadimefon, an agricultural fungicide of the 1,2,4-triazole class, was followed at a nominal concentration of 50 μg/mL over 4 months under aerobic conditions in three different soil types. Rates and products of transformation were measured, as wel...

Climate risks on potato yield in Europe

NASA Astrophysics Data System (ADS)

Sun, Xun; Lall, Upmanu

2016-04-01

The yield of potatoes is affected by water and temperature during the growing season. We study the impact of a suite of climate variables on potato yield at country level. More than ten climate variables related to the growth of potato are considered, including the seasonal rainfall and temperature, but also extreme conditions at different averaging periods from daily to monthly. A Bayesian hierarchical model is developed to jointly consider the risk of heat stress, cold stress, wet and drought. Future climate risks are investigated through the projection of future climate data. This study contributes to assess the risks of present and future climate risks on potatoes yield, especially the risks of extreme events, which could be used to guide better sourcing strategy and ensure food security in the future.

Using Machine-Learned Bayesian Belief Networks to Predict Perioperative Risk of Clostridium Difficile Infection Following Colon Surgery

DTIC Science & Technology

2012-01-01

resections without C-Diff (n = 111,368) General characteristics 8500 (7.5).06116 (6.3)8384 (7.5)CM_HYPOTHY ( Hypothyroidism ) 1772 (1.6).04839 (2.1)1733 (1.6...CM_COAG), hypothyroidism (CM_HYPOTHY), liver disease (CM_LIVER), neurological disorders (CM_NEURO), paralysis (CM_PARA), peripheral vascular disorders

Bayesian Power Prior Analysis and Its Application to Operational Risk and Rasch Model

ERIC Educational Resources Information Center

Zhang, Honglian

2010-01-01

When sample size is small, informative priors can be valuable in increasing the precision of estimates. Pooling historical data and current data with equal weights under the assumption that both of them are from the same population may be misleading when heterogeneity exists between historical data and current data. This is particularly true when…

Mapping marine habitat suitability and uncertainty of Bayesian networks: a case study using Pacific benthic macrofauna

Treesearch

Andrea Havron; Chris Goldfinger; Sarah Henkel; Bruce G. Marcot; Chris Romsos; Lisa Gilbane

2017-01-01

Resource managers increasingly use habitat suitability map products to inform risk management and policy decisions. Modeling habitat suitability of data-poor species over large areas requires careful attention to assumptions and limitations. Resulting habitat suitability maps can harbor uncertainties from data collection and modeling processes; yet these limitations...

Simulation of enteric pathogen concentrations in locally-collected greywater and wastewater for microbial risk assessments

EPA Science Inventory

As decentralized water reuse continues to gain popularity, risk-based treatment guidance is increasingly sought for the protection of public health. However, efforts to evaluate pathogen risks and log-reduction requirements have been hindered by an incomplete understanding of pat...

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

PubMed

Danyluk, Michelle D; Schaffner, Donald W

2011-05-01

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

Gut Microbiome Associates With Lifetime Cardiovascular Disease Risk Profile Among Bogalusa Heart Study Participants

PubMed Central

Kelly, Tanika N.; Bazzano, Lydia A.; Ajami, Nadim J.; He, Hua; Zhao, Jinying; Petrosino, Joseph F.; Correa, Adolfo; He, Jiang

2016-01-01

Rationale Few studies have systematically assessed the influence of gut microbiota on cardiovascular disease (CVD) risk. Objective To examine the association between gut microbiota and lifetime CVD risk profile among 55 Bogalusa Heart Study (BHS) participants with the highest and 57 with the lowest lifetime burdens of CVD risk factors. Methods and Results 16S rRNA sequencing was conducted on microbial DNA extracted from stool samples of the BHS participants. Alpha diversity, including measures of richness and evenness, and individual genera were tested for associations with lifetime CVD risk profile. Multivariable regression techniques were employed to adjust for age, gender, and race (Model 1), along with body mass index (BMI) (Model 2) and both BMI and diet (Model 3). In Model 1, odds ratios (95% confidence intervals) for each standard deviation increase in richness, measured by the number of observed operational taxonomic units, Chao 1 index, and abundance-based coverage estimator, were 0.62 (0.39, 0.99), 0.61 (0.38, 0.98), and 0.63 (0.39, 0.99), respectively. Associations were consistent in Models 2 and 3. Four genera were enriched among those with high versus low CVD risk profile in all models. Model 1 p-values were: 2.12×10−3, 7.95×10−5, 4.39×10−4, and 1.51×10−4 for Prevotella 2, Prevotella 7, Tyzzerella and Tyzzerella 4, respectively. Two genera were depleted among those with high versus low CVD risk profile in all models. Model 1 P-values were: 2.96×10−6 and 1.82×10−4 for Alloprevotella and Catenibacterium, respectively. Conclusions The current study identified associations of overall microbial richness and six microbial genera with lifetime CVD risk. PMID:27507222

Gut Microbiome Associates With Lifetime Cardiovascular Disease Risk Profile Among Bogalusa Heart Study Participants.

PubMed

Kelly, Tanika N; Bazzano, Lydia A; Ajami, Nadim J; He, Hua; Zhao, Jinying; Petrosino, Joseph F; Correa, Adolfo; He, Jiang

2016-09-30

Few studies have systematically assessed the influence of gut microbiota on cardiovascular disease (CVD) risk. To examine the association between gut microbiota and lifetime CVD risk profile among 55 Bogalusa Heart Study participants with the highest and 57 with the lowest lifetime burdens of CVD risk factors. 16S ribosomal RNA sequencing was conducted on microbial DNA extracted from stool samples of the Bogalusa Heart Study participants. α Diversity, including measures of richness and evenness, and individual genera were tested for associations with lifetime CVD risk profile. Multivariable regression techniques were used to adjust for age, sex, and race (model 1), along with body mass index (model 2) and both body mass index and diet (model 3). In model 1, odds ratios (95% confidence intervals) for each SD increase in richness, measured by the number of observed operational taxonomic units, Chao 1 index, and abundance-based coverage estimator, were 0.62 (0.39-0.99), 0.61 (0.38-0.98), and 0.63 (0.39-0.99), respectively. Associations were consistent in models 2 and 3. Four genera were enriched among those with high versus low CVD risk profile in all models. Model 1 P values were 2.12×10(-3), 7.95×10(-5), 4.39×10(-4), and 1.51×10(-4) for Prevotella 2, Prevotella 7, Tyzzerella, and Tyzzerella 4, respectively. Two genera were depleted among those with high versus low CVD risk profile in all models. Model 1 P values were 2.96×10(-6) and 1.82×10(-4) for Alloprevotella and Catenibacterium, respectively. The current study identified associations of overall microbial richness and 6 microbial genera with lifetime CVD risk. © 2016 American Heart Association, Inc.

The Development of Bayesian Theory and Its Applications in Business and Bioinformatics

NASA Astrophysics Data System (ADS)

Zhang, Yifei

2018-03-01

Bayesian Theory originated from an Essay of a British mathematician named Thomas Bayes in 1763, and after its development in 20th century, Bayesian Statistics has been taking a significant part in statistical study of all fields. Due to the recent breakthrough of high-dimensional integral, Bayesian Statistics has been improved and perfected, and now it can be used to solve problems that Classical Statistics failed to solve. This paper summarizes Bayesian Statistics’ history, concepts and applications, which are illustrated in five parts: the history of Bayesian Statistics, the weakness of Classical Statistics, Bayesian Theory and its development and applications. The first two parts make a comparison between Bayesian Statistics and Classical Statistics in a macroscopic aspect. And the last three parts focus on Bayesian Theory in specific -- from introducing some particular Bayesian Statistics’ concepts to listing their development and finally their applications.

Bayesian demography 250 years after Bayes

PubMed Central

Bijak, Jakub; Bryant, John

2016-01-01

Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms. PMID:26902889

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

Wainwright, Haruko M.; Flores Orozco, Adrian; Bucker, Matthias

In floodplain environments, a naturally reduced zone (NRZ) is considered to be a common biogeochemical hot spot, having distinct microbial and geochemical characteristics. Although important for understanding their role in mediating floodplain biogeochemical processes, mapping the subsurface distribution of NRZs over the dimensions of a floodplain is challenging, as conventional wellbore data are typically spatially limited and the distribution of NRZs is heterogeneous. In this work, we present an innovative methodology for the probabilistic mapping of NRZs within a three-dimensional (3-D) subsurface domain using induced polarization imaging, which is a noninvasive geophysical technique. Measurements consist of surface geophysical surveys andmore » drilling-recovered sediments at the U.S. Department of Energy field site near Rifle, CO (USA). Inversion of surface time domain-induced polarization (TDIP) data yielded 3-D images of the complex electrical resistivity, in terms of magnitude and phase, which are associated with mineral precipitation and other lithological properties. By extracting the TDIP data values colocated with wellbore lithological logs, we found that the NRZs have a different distribution of resistivity and polarization from the other aquifer sediments. To estimate the spatial distribution of NRZs, we developed a Bayesian hierarchical model to integrate the geophysical and wellbore data. In addition, the resistivity images were used to estimate hydrostratigraphic interfaces under the floodplain. Validation results showed that the integration of electrical imaging and wellbore data using a Bayesian hierarchical model was capable of mapping spatially heterogeneous interfaces and NRZ distributions thereby providing a minimally invasive means to parameterize a hydrobiogeochemical model of the floodplain.« less

Diversity and composition of vaginal microbiota of pregnant women at risk for transmitting Group B Streptococcus treated with intrapartum penicillin.

PubMed

Roesch, Luiz Fernando Wurdig; Silveira, Rita C; Corso, Andréa L; Dobbler, Priscila Thiago; Mai, Volker; Rojas, Bruna S; Laureano, Álvaro M; Procianoy, Renato S

2017-01-01

Administering intravenous antibiotics during labor to women at risk for transmitting Group B Streptococcus (GBS) can prevent infections in newborns. However, the impact of intrapartum antibiotic prophylaxis on mothers' microbial community composition is largely unknown. We compared vaginal microbial composition in pregnant women experiencing preterm birth at ≤ 32 weeks gestation that received intrapartum antibiotic prophylaxis with that in controls. Microbiota in vaginal swabs collected shortly before delivery from GBS positive women that received penicillin intravenously during labor or after premature rupture of membranes was compared to controls. Microbiota was analyzed by 16S rRNA sequencing using the PGM Ion Torrent to determine the effects of penicillin use during hospitalization and GBS status on its composition. Penicillin administration was associated with an altered vaginal microbial community composition characterized by increased microbial diversity. Lactobacillus sp. contributed only 13.1% of the total community in the women that received penicillin compared to 88.1% in the controls. Streptococcus sp. were present in higher abundance in GBS positive woman compared to controls, with 60% of the total vaginal microbiota in severe cases identified as Streptococcus sp. Vaginal communities of healthy pregnant women were dominated by Lactobacillus sp. and contained low diversity, while Group B Streptococcus positive women receiving intrapartum antibiotic prophylaxis had a modified vaginal microbiota composition with low abundance of Lactobacillus but higher microbial diversity.

Towards a rapid and comprehensive microbial detection and identification system for life support and planetary protection applications

NASA Astrophysics Data System (ADS)

Lasseur, Christophe

Long term manned missions of our Russian colleagues have demonstrated the risks associated with microbial contamination. These risks concern both crew health via the metabolic consumables contamination (water, air,.) but and also the hardware degradation. In parallel to these life support issues, planetary protection experts have agreed to place clear specifications of the microbial quality of future hardware landing on extraterrestrial planets as well as elaborate the requirements of contamination for manned missions on surface. For these activities, it is necessary to have a better understanding of microbial activity, to create culture collections and to develop on-line detection tools. . In this respect, over the last 6 years , ESA has supported active scientific research on the choice of critical genes and functions, including those linked to horizontal gene pool of bacteria and its dissemination. In parallel, ESA and European industries have been developing an automated instrument for rapid microbial detection on air and surface samples. Within this paper, we first present the life support and planetary protection requirements, and the state of the art of the instrument development. Preliminary results at breadboard level, including a mock-up view of the final instrument are also presented. Finally, the remaining steps required to reach a functional instrument for planetary hardware integration and life support flight hardware are also presented.

A geographical information system (GIS) as a tool for microbial risk assessment in catchment areas of drinking water reservoirs.

PubMed

Kistemann, T; Dangendorf, F; Exner, M

2001-03-01

The main tributaries of three drinking water reservoirs of Northrhine-Westfalia (Germany) were monitored within a 14-month period mainly for bacterial and parasitic contamination. In this context a detailed geo-ecological characterisation within the differing catchment areas was carried out to reveal a reliable informational basis for tracing back the origin of microbial loads present in the watercourses. To realise a microbial risk assessing geo-ecological information system (MRA-GIS), a Geographical Information System (GIS) has been implemented for the study areas. The results of the microbiological investigations of the watercourses showed an input of pathogens into all three of the tributaries. It could be demonstrated that the use of MRA-GIS database and some GIS-techniques substantially support the spatial analysis of the microbial contamination patterns. From the hygienic point of view, it is of the utmost importance to protect catchment areas of surface water reservoirs from microbial contamination stemming from human activities and animal sources. This constitutes essential part of the multi-barrier concept which stresses the importance of reducing diffuse and point pollution in catchment areas of water resources intended for human consumption. MRA-GIS proves to be helpful to manage multi-barrier water protection in catchment areas and ideally assists the application of the HACCP concept on drinking water production.

Can QMRA be used to Discount Pathogen Risk to Swimmers from Animal Fecal Contamination? Doheny Beach, CA Case Study

EPA Science Inventory

Estimated health risks to swimmers from seagull and bather sources of fecal contamination at Doheny Beach, California were compared using quantitative microbial risk assessment (QMRA) with a view to aiding beach closure decisions. Surfzone pathogens from seagulls were thought to...

Microbial agents in macroscopically healthy mammary gland tissues of small ruminants.

PubMed

Spuria, Liliana; Biasibetti, Elena; Bisanzio, Donal; Biasato, Ilaria; De Meneghi, Daniele; Nebbia, Patrizia; Robino, Patrizia; Bianco, Paolo; Lamberti, Michele; Caruso, Claudio; Di Blasio, Alessia; Peletto, Simone; Masoero, Loretta; Dondo, Alessandro; Capucchio, Maria Teresa

2017-01-01

Health of mammary glands is fundamental for milk and dairy products hygiene and quality, with huge impacts on consumers welfare. This study aims to investigate the microbial agents (bacteria, fungi and lentiviruses) isolated from 89 macroscopically healthy udders of regularly slaughtered small ruminants (41 sheep, 48 goats), also correlating their presence with the histological findings. Multinomial logistic regression was applied to evaluate the association between lesions and positivity for different microbial isolates, animal age and bacteria. Twenty-five samples were microbiologically negative; 138 different bacteria were isolated in 64 positive udders. Coagulase-negative staphylococci were the most prevalent bacteria isolated (46.42%), followed by environmental opportunists (34.76%), others (10.14%) and pathogens (8.68%). Most mammary glands showed coinfections (75%). Lentiviruses were detected in 39.3% of samples. Histologically, chronic non-suppurative mastitis was observed in 45/89 glands, followed by chronic mixed mastitis (12/89) and acute suppurative mastitis (4/89). Only 28 udders were normal. Histological lesions were significantly associated with the animal species and lentiviruses and coagulase-negative staphylococci infections. Goats had significantly higher risk to show chronic mixed mastitis compared to sheep. Goats showed a significantly lower risk (OR = 0.26; 95% CI [0.06-0.71]) of being infected by environmental opportunists compared to sheep, but higher risk (OR = 10.87; 95% CI [3.69-37.77]) of being infected with lentiviruses. The results of the present study suggest that macroscopically healthy glands of small ruminants could act as a reservoir of microbial agents for susceptible animals, representing a potential risk factor for the widespread of acute or chronic infection in the flock.

Severe community-acquired pneumonia. Risk factors and follow-up epidemiology.

PubMed

Ruiz, M; Ewig, S; Torres, A; Arancibia, F; Marco, F; Mensa, J; Sanchez, M; Martinez, J A

1999-09-01

The aim of the study was to determine risk factors for severe community-acquired pneumonia (CAP) as well as to compare microbial patterns of severe CAP to a previous study from our respiratory intensive care unit (ICU) originating from 1984 to 1987. Patients admitted to the ICU according to clinical judgment were defined as having severe CAP. For the study of risk factors, a hospital-based case-control design was used, matching each patient with severe CAP to a patient hospitalized with CAP but not requiring ICU admission. Microbial investigation included noninvasive and invasive techniques. Overall, 89 patients with severe CAP were successfully matched to a control patient. The presence of an alcohol ingestion of >/= 80 g/d (odds ratio [OR] 3.9, 95% confidence interval [CI] 1.4 to 10.6, p = 0.008) was found to be an independent risk factor for severe CAP and prior ambulatory antimicrobial treatment (OR 0.37, 95% CI 0.17 to 0.79, p = 0.009) to be protective. Streptococcus pneumoniae (24%) continued to be the most frequent pathogen; however, 48% of strains were drug-resistant. "Atypical" bacterial pathogens were significantly more common (17% versus 6%, p = 0.006) and Legionella spp. less common (2% versus 14%, p = 0.004) than in our previous study, whereas gram-negative enteric bacilli (GNEB) and Pseudomonas aeruginosa continued to represent important pathogens (6% and 5%, respectively). Our findings provide additional evidence for the importance of the initiation of early empiric antimicrobial treatment for a favorable outcome of CAP. Variations of microbial patterns are only in part due to different epidemiological settings. Therefore, initial empiric antimicrobial treatment will also have to take into account local trends of changing microbial patterns.

Alveolar ridge preservation after tooth extraction: a Bayesian Network meta-analysis of grafting materials efficacy on prevention of bone height and width reduction.

PubMed

Iocca, Oreste; Farcomeni, Alessio; Pardiñas Lopez, Simon; Talib, Huzefa S

2017-01-01

To conduct a traditional meta-analysis and a Bayesian Network meta-analysis to synthesize the information coming from randomized controlled trials on different socket grafting materials and combine the resulting indirect evidence in order to make inferences on treatments that have not been compared directly. RCTs were identified for inclusion in the systematic review and subsequent statistical analysis. Bone height and width remodelling were selected as the chosen summary measures for comparison. First, a series of pairwise meta-analyses were performed and overall mean difference (MD) in mm with 95% CI was calculated between grafted versus non-grafted sockets. Then, a Bayesian Network meta-analysis was performed to draw indirect conclusions on which grafting materials can be considered most likely the best compared to the others. From the six included studies, seven comparisons were obtained. Traditional meta-analysis showed statistically significant results in favour of grafting the socket compared to no-graft both for height (MD 1.02, 95% CI 0.44-1.59, p value < 0.001) than for width (MD 1.52 95% CI 1.18-1.86, p value <0.000001) remodelling. Bayesian Network meta-analysis allowed to obtain a rank of intervention efficacy. On the basis of the results of the present analysis, socket grafting seems to be more favourable than unassisted socket healing. Moreover, Bayesian Network meta-analysis indicates that freeze-dried bone graft plus membrane is the most likely effective in the reduction of bone height remodelling. Autologous bone marrow resulted the most likely effective when width remodelling was considered. Studies with larger samples and less risk of bias should be conducted in the future in order to further strengthen the results of this analysis. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

[New approach for managing microbial risks in food].

PubMed

Augustin, Jean-Christophe

2015-01-01

The aim of the food legislation is to ensure the protection of human health. Traditionally, the food legislation requires food business operators to apply good hygiene practices and specific procedures to control foodborne pathogens. These regulations allowed reaching a high level of health protection. The improvement of the system will require risk-based approaches. Firstly, risk assessment should allow the identification of high-risk situations where resources should be allocated for a better targeting of risk management. Then, management measures should be adapted to the health objective. In this approach, the appropriate level of protection is converted intofood safety and performance objectives on the food chain, i.e., maximum microbial contamination to fulfil the acceptable risk level. When objectives are defined, the food business operators and competent authorities can identify control options to comply with the objectives and establish microbiological criteria to verify compliance with these objectives. This approach, described for approximately 10 years, operative thanks to the development of quantitative risk assessment techniques, is still difficult to use in practical terms since it requires a commitment of competent authorities to define the acceptable risk and needs also the implementation of sometimes complex risk models.