Tracing the Origin of Food-borne Disease Outbreaks: A Network Model Approach.

Background: Food-borne disease outbreaks constitute a large health burden on society. One of the challenges when investigating such outbreaks is to trace the origin of the outbreak. In this study, we consider a network model to determine the spatial origin of the contaminated food product that caused the outbreak.

A generalized Bayesian nonlinear mixed-effects regression model for zero-inflated longitudinal count data in tuberculosis trials.

In this paper, we investigate Bayesian generalized nonlinear mixed-effects (NLME) regression models for zero-inflated longitudinal count data. The methodology is motivated by and applied to colony forming unit (CFU) counts in extended bactericidal activity tuberculosis (TB) trials. Furthermore, for model comparisons, we present a generalized method for calculating the marginal likelihoods required to determine Bayes factors.

Outbreak of Bovismorbificans associated with the consumption of uncooked ham products, the Netherlands, 2016 to 2017.

In January 2017, an increase in reported serotype Bovismorbificans cases in the Netherlands was observed since October 2016. We implemented a case-control study to identify the source, including all cases after December 2016. Adjusted odds ratios were calculated using logistic regression analysis.

Identifying the source of food-borne disease outbreaks: An application of Bayesian variable selection.

Early identification of contaminated food products is crucial in reducing health burdens of food-borne disease outbreaks. Analytic case-control studies are primarily used in this identification stage by comparing exposures in cases and controls using logistic regression. Standard epidemiological analysis practice is not formally defined and the combination of currently applied methods is subject to issues such as response misclassification, missing values, multiple testing problems and small sample estimation problems resulting in biased and possibly misleading results.

A probabilistic approach to assess antibiotic resistance development risks in environmental compartments and its application to an intensive aquaculture production scenario.

Estimating antibiotic pollution and antibiotic resistance development risks in environmental compartments is important to design management strategies that advance our stewardship of antibiotics. In this study we propose a modelling approach to estimate the risk of antibiotic resistance development in environmental compartments and demonstrate its application in aquaculture production systems. We modelled exposure concentrations for 12 antibiotics used in Vietnamese Pangasius catfish production using the ERA-AQUA model.

Combining exposure and effect modeling into an integrated probabilistic environmental risk assessment for nanoparticles.

There is a growing need for good environmental risk assessment of engineered nanoparticles (ENPs). Environmental risk assessment of ENPs has been hampered by lack of data and knowledge about ENPs, their environmental fate, and their toxicity. This leads to uncertainty in the risk assessment.

Parametric estimation of P(X > Y) for normal distributions in the context of probabilistic environmental risk assessment.

Estimating the risk, P(X > Y), in probabilistic environmental risk assessment of nanoparticles is a problem when confronted by potentially small risks and small sample sizes of the exposure concentration X and/or the effect concentration Y. This is illustrated in the motivating case study of aquatic risk assessment of nano-Ag. A non-parametric estimator based on data alone is not sufficient as it is limited by sample size.

Integrated probabilistic risk assessment for nanoparticles: the case of nanosilica in food.

Insight into risks of nanotechnology and the use of nanoparticles is an essential condition for the social acceptance and safe use of nanotechnology. One of the problems with which the risk assessment of nanoparticles is faced is the lack of data, resulting in uncertainty in the risk assessment. We attempt to quantify some of this uncertainty by expanding a previous deterministic study on nanosilica (5-200 nm) in food into a fully integrated probabilistic risk assessment.