Comparison of six dose-response models for use with food-borne pathogens

Food-related illness in the United States is estimated to affect over six million people per year and cost the economy several billion dollars. These illnesses and costs could be reduced if minimum infectious doses were established and used as the basis of regulations and monitoring. However, standard methodologies for dose-response assessment are not yet formulated for microbial risk assessment. The objective of this study was to compare dose-response models for food-borne pathogens and determine which models were most appropriate for a range of pathogens. The statistical models proposed in the literature and chosen for comparison purposes were log-normal,((19)) log-logistic,((17)) exponential,((7,9,17)) beta-Poisson((7,9,18)) and Weibull-Gamma.((3)) These were fit to four data sets also taken from published literature, Shigella flexneri,((9,12,13)) Shigella dysenteriae,((9,11)) Campylobacter jejuni,((15,16)) and Salmonella typhosa,((7,14)) using the method of maximum likelihood. The Weibull-gamma, the only model with three parameters, was also the only model capable of fitting all the data sets examined using the maximum likelihood estimation for comparisons. Infectious doses were also calculated using each model. Within any given data set, the infectious dose estimated to affect one percent of the population ranged from one order of magnitude to as much as nine orders of magnitude, illustrating the differences in extrapolation of the dose response models. More data are needed to compare models and examine extrapolation from high to low doses for food-borne pathogens.