SIMULATION MODELING FOR MICROBIAL RISK ASSESSMENT

Quantitative microbial risk assessment implies an estimation of the pr obability and impact of adverse health outcomes due to microbial hazar ds. 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 sol utions. In some cases, a risk assessment may be commissioned to serve a larger purpose than simply the estimation of risk. A Monte Carlo sim ulation can provide insights into complex processes that are invaluabl e, and otherwise unavailable, to those charged with the task of risk m anagement. Using examples from a farm-to-fork model of the fate of Esc herichia coli O157:H7 in ground beef hamburgers, this paper describes specifically how such goals as research prioritization, risk-based cha racterization of control points, and risk-based comparison of interven tion strategies can be objectively achieved using Monte Carlo simulati on.