Simulation modeling for microbial risk assessment.

Abstract

Quantitative microbial risk assessment implies an estimation of the probability and impact of adverse health outcomes due to microbial hazards. In the case of food safety, the probability of human illness is a complex function of the variability of many parameters that influence the microbial environment, from the production to the consumption of a food. The analytical integration required to estimate the probability of foodborne illness is intractable in all but the simplest of models. Monte Carlo simulation is an alternative to computing analytical solutions. In some cases, a risk assessment may be commissioned to serve a larger purpose than simply the estimation of risk. A Monte Carlo simulation can provide insights into complex processes that are invaluable, and otherwise unavailable, to those charged with the task of risk management. Using examples from a farm-to-fork model of the fate of Escherichia coli O157:H7 in ground beef hamburgers, this paper describes specifically how such goals as research prioritization, risk-based characterization of control points, and risk-based comparison of intervention strategies can be objectively achieved using Monte Carlo simulation.