title: Uncertainty distribution associated with estimating a proportion in microbial risk assessment.
abstract: The uncertainty associated with estimates should be taken into account in quantitative risk assessment. Each input's uncertainty can be characterized through a probabilistic distribution for use under Monte Carlo simulations. In this study, the sampling uncertainty associated with estimating a low proportion on the basis of a small sample size was considered. A common application in microbial risk assessment is the estimation of a prevalence, proportion of contaminated food products, on the basis of few tested units. Three Bayesian approaches (based on beta(0, 0), beta(1/2, 1/2), and beta(l, 1)) and one frequentist approach (based on the frequentist confidence distribution) were compared and evaluated on the basis of simulations. For small samples, we demonstrated some differences between the four tested methods. We concluded that the better method depends on the true proportion of contaminated products, which is by definition unknown in common practice. When no prior information is available, we recommend the beta (1/2, 1/2) prior or the confidence distribution. To illustrate the importance of these differences, the four methods were used in an applied example. We performed two-dimensional Monte Carlo simulations to estimate the proportion of cold smoked salmon packs contaminated by Listeria monocytogenes, one dimension representing within-factory uncertainty, modeled by each of the four studied methods, and the other dimension representing variability between companies.
authors.affiliation: Agence française de Sécurile Sanitaire des aliments, Microbiologie quantitative et estimation de risques, 94706 Maisons-Alfort, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Miconnet
authors.forename: Marie
authors.initials: M
authors.lastname: Cornu
authors.forename: Annie
authors.initials: A
authors.lastname: Beaufort
authors.forename: Laurent
authors.initials: L
authors.lastname: Rosso
authors.forename: Jean-Baptiste
authors.initials: JB
authors.lastname: Denis
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D003257:Consumer Product Safety; D005513:Food Inspection; D005516:Food Microbiology; D005525:Food-Processing Industry; D005517:Foodborne Diseases; D008089:Listeria monocytogenes / Q000378:metabolism*; D008088:Listeriosis / Q000453:epidemiology*; D015233:Models, Statistical; D009010:Monte Carlo Method; D011336:Probability; D012306:Risk; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012474:Salmon; D012680:Sensitivity and Specificity