Journal
INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY
Volume 139, Issue -, Pages S57-S63Publisher
ELSEVIER
DOI: 10.1016/j.ijfoodmicro.2009.12.015
Keywords
Microbial risk assessment; Bayesian belief network; Monte Carlo analysis; Food safety
Categories
Funding
- BBSRC [BBS/E/F/00042314] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BBS/E/F/00042314] Funding Source: researchfish
- Biotechnology and Biological Sciences Research Council [BBS/E/F/00042314] Funding Source: Medline
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We discuss different aspects of farm-to-fork risk assessment from a modelling perspective. Stochastic simulation models as they are presented today represent a mathematical representation of nature. In food safety risk assessment, a common modelling approach consists of a logic chain beginning at the source of the hazard and ending with the unwanted consequences of interest. This 'farm-to-fork' approach usually begins with the hazard on the farm, sometimes with different compartments presenting different parts of the production chain, and ends with the 'dose' received by the consumer or in case a dose response model is available the number of cases of illness. These models are typically implemented as Monte Carlo simulations, which are unidirectional in nature, and the link between statistics and simulation model is not interactive. A possible solution could be the use of Bayesian belief networks (BBNs) and this paper tries to discuss in an intuitive way the possibilities of using BBNs as an alternative for Monte Carlo modelling. An inventory is made of the strengths and weaknesses of both approaches, and an example is given showing an additional use of BBNs in biotracing problems. (C) 2009 Elsevier B.V. All rights reserved.
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