title: Estimation of uncertainty and variability in bacterial growth using Bayesian inference. Application to Listeria monocytogenes.
abstract: The usefulness of risk assessment is limited by its ability or inability to model and evaluate risk uncertainty and variability separately. A key factor of variability and uncertainty in microbial risk assessment could be growth variability between strains and growth model parameter uncertainty. In this paper, we propose a Bayesian procedure for growth parameter estimation which makes it possible to separate these two components by means of hyperparameters. This model incorporates in a single step the logistic equation with delay as a primary growth model and the cardinal temperature equation as a secondary growth model. The estimation of Listeria monocytogenes growth parameters in milk using literature data is proposed as a detailed application. While this model should be applied on genuine data, it is highlighted that the proposed approach may be convenient for estimating the variability and uncertainty of growth parameters separately, using a complete predictive microbiology model.
authors.affiliation: Agence Française de Sécurité Sanitaire des Aliments, 27-31 avenue du Général Leclerc, BP19, 94701 Maisons-Alfort cedex, France. r.pouillot@afssa.fr
authors.forename: Régis
authors.initials: R
authors.lastname: Pouillot
authors.forename: Isabelle
authors.initials: I
authors.lastname: Albert
authors.forename: Marie
authors.initials: M
authors.lastname: Cornu
authors.forename: Jean Baptiste
authors.initials: JB
authors.lastname: Denis
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D005516:Food Microbiology; D007700:Kinetics; D008089:Listeria monocytogenes / Q000254:growth & development*; D008892:Milk / Q000382:microbiology*; D008954:Models, Biological; D018570:Risk Assessment