Harnessing the theoretical foundations of the exponential and beta-Poisson dose-response models to quantify parameter uncertainty using Markov Chain Monte Carlo.

Dose-response models are the essential link between exposure assessment and computed risk values in quantitative microbial risk assessment, yet the uncertainty that is inherent to computed risks because the dose-response model parameters are estimated using limited epidemiological data is rarely quantified. Second-order risk characterization approaches incorporating uncertainty in dose-response model parameters can provide more complete information to decisionmakers by separating variability and uncertainty to quantify the uncertainty in computed risks. Therefore, the objective of this work is to develop procedures to sample from posterior distributions describing uncertainty in the parameters of exponential and beta-Poisson dose-response models using Bayes's theorem and Markov Chain Monte Carlo (in OpenBUGS). The theoretical origins of the beta-Poisson dose-response model are used to identify a decomposed version of the model that enables Bayesian analysis without the need to evaluate Kummer confluent hypergeometric functions. Herein, it is also established that the beta distribution in the beta-Poisson dose-response model cannot address variation among individual pathogens, criteria to validate use of the conventional approximation to the beta-Poisson model are proposed, and simple algorithms to evaluate actual beta-Poisson probabilities of infection are investigated. The developed MCMC procedures are applied to analysis of a case study data set, and it is demonstrated that an important region of the posterior distribution of the beta-Poisson dose-response model parameters is attributable to the absence of low-dose data. This region includes beta-Poisson models for which the conventional approximation is especially invalid and in which many beta distributions have an extreme shape with questionable plausibility.

Probabilistic risk model for staphylococcal intoxication from pork-based food dishes prepared in food service establishments in Korea.

Foodborne illness contracted at food service operations is an important public health issue in Korea. In this study, the probabilities for growth of, and enterotoxin production by, Staphylococcus aureus in pork meat-based foods prepared in food service operations were estimated by the Monte Carlo simulation. Data on the prevalence and concentration of S. aureus as well as compliance to guidelines for time and temperature controls during food service operations were collected. The growth of S. aureus was initially estimated by using the U.S. Department of Agriculture's Pathogen Modeling Program. A second model based on raw pork meat was derived to compare cell number predictions. The correlation between toxin level and cell number as well as minimum toxin dose obtained from published data was adopted to quantify the probability of staphylococcal intoxication. When data gaps were found, assumptions were made based on guidelines for food service practices. Baseline risk model and scenario analyses were performed to indicate possible outcomes of staphylococcal intoxication under the scenarios generated based on these data gaps. Staphylococcal growth was predicted during holding before and after cooking, and the highest estimated concentration (4.59 log CFU/g for the 99.9th percentile value) of S. aureus was observed in raw pork initially contaminated with S. aureus and held before cooking. The estimated probability for staphylococcal intoxication was very low, using currently available data. However, scenario analyses revealed an increased possibility of staphylococcal intoxication when increased levels of initial contamination in the raw meat, andlonger holding time both before and after cooking the meat occurred.

A probabilistic analysis of Clostridium perfringens growth during food service operations.

The purpose of this study was threefold: first, the study was designed to illustrate the use of data and information collected in food safety surveys in a quantitative risk assessment. In this case, the focus was on the food service industry; however, similar data from other parts of the food chain could be similarly incorporated. The second objective was to quantitatively describe and better understand the role that the food service industry plays in the safety of food. The third objective was to illustrate the additional decision-making information that is available when uncertainty and variability are incorporated into the modelling of systems.