Case studies in Bayesian microbial risk assessments.

ABSTRACT:

Background

The quantification of uncertainty and variability is a key component of quantitative risk analysis. Recent advances in Bayesian statistics make it ideal for integrating multiple sources of information, of different types and quality, and providing a realistic estimate of the combined uncertainty in the final risk estimates.

Methods

We present two case studies related to foodborne microbial risks. In the first, we combine models to describe the sequence of events resulting in illness from consumption of milk contaminated with VTEC O157. We used Monte Carlo simulation to propagate uncertainty in some of the inputs to computer models describing the farm and pasteurisation process. Resulting simulated contamination levels were then assigned to consumption events from

SUBMITTER: Kennedy MC 

PROVIDER: S-EPMC2796497 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature