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date: 03 August 2020

Abstract and Keywords

This article discusses the use of Bayesian methods for performing uncertainty analysis in complex computer models, focusing on a mechanistic model that has been applied in a risk assessment of contamination of farm-pasteurized milk with the bacterium Vero-cytotoxigenic E. coli (VTEC) O157. The VTEC model has uncertain input parameters, which makes outputs from the model used to inform the risk assessment also uncertain. The question that arises is how to reduce output uncertainty in the most efficient manner possible. The article first provides an overview of microbial risk assessment before analysing the frequency and consequences of food-borne outbreaks associated with VTEC O157. It then introduces the risk assessment model, along with model input distributions. Finally, it presents the results of a variance-based sensitivity analysis that was conducted to identify the most important uncertain model inputs.

Keywords: Bayesian methods, complex computer models, E. coli, microbial risk assessment, food-borne outbreaks, VTEC O157, variance-based sensitivity analysis, uncertainty analysis

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