Topics in Microbial Risk Assessment: Dynamic Flow Tree Process

Abstract

Microbial risk assessment is emerging as a new discipline in risk assessment. A systematic approach to microbial risk assessment is presented that employs data analysis for developing parsimonious models and accounts formally for the variability and uncertainty of model inputs using analysis of variance and Monte Carlo simulation. The purpose of the paper is to raise and examine issues in conducting microbial risk assessments. The enteric pathogen Escherichia coli O157:H7 was selected as an example for this study due to its significance to public health. The framework for our work is consistent with the risk assessment components described by the National Research Council in 1983 (hazard identification; exposure assessment; dose-response assessment; and risk characterization). Exposure assessment focuses on hamburgers, cooked a range of temperatures from rare to well done, the latter typical for fast food restaurants. Features of the model include predictive microbiology components that account for random stochastic growth and death of organisms in hamburger. For dose-response modeling, Shigella data from human feeding studies were used as a surrogate for E. coli O157:H7. Risks were calculated using a threshold model and an alternative nonthreshold model. The 95% probability intervals for risk of illness for product cooked to a given internal temperature spanned five orders of magnitude for these models. The existence of even a small threshold has a dramatic impact on the estimated risk.

Monte Carlo Simulation of Pathogen Behavior during the Sprout Production Process

“…Bacterial death as determined by this distribution was described using the binomial equation (26). The probability of a single cell surviving disinfection (P) was 10 The number of surviving cells was assumed to be normally distributed, where the mean number of cells to survive disinfection was P times n, and the standard deviation was the square root of n times P times the result of 1 minus P. For example, if the reduction caused by disinfection is 2.5 log 10 CFU, then P was 10 Ϫ2.5 or 0.003 and the population size (n) was 10 4 (or 10,000); the mean number of cells to survive would be 30, and standard deviation was 5.4 cells.…”

Critical notes on microbiological risk assessment of food

“…The literature on Microbiological Risk Assessment presents various frameworks (10,13,16,20,21,26). In all of them, the risk assessor first identifies the hazard(s) of concern and then describes their impact on the population and finalises with a risk characterization.…”

“…1 at 3). This is called a Dynamic Flow Tree model (16), Process Risk Model (7), Pathogen-Product pathway or Farm-to-Fork model. It allows to estimate the various levels of the hazard in various situations / circumstances and the probability that the population is exposed to them.…”

Estimation of uncertainty and variability in bacterial growth using Bayesian inference. Application to Listeria monocytogenes

“…Quantitative risk assessment is generally based on a mathematical and statistical model of risk agent behaviour through a considered chain of processes (Marks et al, 1998). Risk assessment associated with a microbial hazard is somewhat complicated by the potential growth or decrease in the bacterial population according to the microenvironment, from food production to ingestion.…”