Bayesian Analysis for Food-Safety Risk Assessment: Evaluation of Dose-Response Functions within WinBUGS

Bayesian methods are becoming increasingly popular in the field of food-safety risk assessment. Risk assessment models often require the integration of a dose-response function over the distribution of all possible doses of a pathogen ingested with a specific food. This requires the evaluation of an integral for every sample for a Markov chain Monte Carlo analysis of a model. While many statistical software packages have functions that allow for the evaluation of the integral, this functionality is lacking in WinBUGS. A probabilistic model, that incorporates a novel numerical integration technique, is presented to facilitate the use of WinBUGS for food-safety risk assessments. The numerical integration technique is described in the context of a typical food-saftey risk assesment, some theoretical results are given, and a snippet of WinBUGS code is provided

Anticipating the prevalence of avian influenza subtypes H9 and H5 in live-bird markets.

An ability to forecast the prevalence of specific subtypes of avian influenza viruses (AIV) in live-bird markets would facilitate greatly the implementation of preventative measures designed to minimize poultry losses and human exposure. The minimum requirement for developing predictive quantitative tools is surveillance data of AIV prevalence sampled frequently over several years. Recently, a 4-year time series of monthly sampling of hemagglutinin subtypes 1–13 in ducks, chickens and quail in live-bird markets in southern China has become available. We used these data to investigate whether a simple statistical model, based solely on historical data (variables such as the number of positive samples in host X of subtype Y time t months ago), could accurately predict prevalence of H5 and H9 subtypes in chickens. We also examined the role of ducks and quail in predicting prevalence in chickens within the market setting because between-species transmission is thought to occur within markets but has not been measured. Our best statistical models performed remarkably well at predicting future prevalence (pseudo-R2 = 0.57 for H9 and 0.49 for H5), especially considering the multi-host, multi-subtype nature of AIVs. We did not find prevalence of H5/H9 in ducks or quail to be predictors of prevalence in chickens within the Chinese markets. Our results suggest surveillance protocols that could enable more accurate and timely predictive statistical models. We also discuss which data should be collected to allow the development of mechanistic models.published_or_final_versio