The Bayesian Microbial Subtyping Attribution Model: Robustness to Prior Information and a Proposition.

J. M. David 1 D. Guillemot N. Bemrah A. Thébault 2 A. Brisabois M. Chemaly 3 Fx Weill Pascal Sanders 1 Laurence Watier *
* Auteur correspondant
1 Unité de Toxicologie des Contaminants
2 EPFL - Ecole Polytechnique Fédérale de Lausanne
3 Laboratoire de Ploufragan - Plouzané
Abstract : Attributing foodborne illnesses to food sources is essential to conceive, prioritize, and assess the impact of public health policy measures. The Bayesian microbial subtyping attribution model by Hald et al. is one of the most advanced approaches to attribute sporadic cases; it namely allows taking into account the level of exposure to the sources and the differences between bacterial types and between sources. This step forward requires introducing type and source-dependent parameters, and generates overparameterization, which was addressed in Hald's paper by setting some parameters to constant values. We question the impact of the choices made for the parameterization (parameters set and values used) on model robustness and propose an alternative parameterization for the Hald model. We illustrate this analysis with the 2005 French data set of non-typhi Salmonella. Mullner's modified Hald model and a simple deterministic model were used to compare the results and assess the accuracy of the estimates. Setting the parameters for bacterial types specific to a unique source instead of the most frequent one and using data-based values instead of arbitrary values enhanced the convergence and adequacy of the estimates and led to attribution estimates consistent with the other models' results. The type and source parameters estimates were also coherent with Mullner's model estimates. The model appeared to be highly sensitive to parameterization. The proposed solution based on specific types and data-based values improved the robustness of estimates and enabled the use of this highly valuable tool successfully with the French data set.
Keywords : risk analysis food safety statistic microbiology bayesian model
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Article dans une revue
Risk Analysis, Wiley, 2012, epub ahead of print. 〈10.1111/j.1539-6924.2012.01877.x〉
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https://hal-anses.archives-ouvertes.fr/hal-00729049
Contributeur : Emmanuelle Chiffoleau <>
Soumis le : vendredi 7 septembre 2012 - 12:29:40
Dernière modification le : vendredi 3 août 2018 - 16:10:02

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J. M. David, D. Guillemot, N. Bemrah, A. Thébault, A. Brisabois, et al.. The Bayesian Microbial Subtyping Attribution Model: Robustness to Prior Information and a Proposition.. Risk Analysis, Wiley, 2012, epub ahead of print. 〈10.1111/j.1539-6924.2012.01877.x〉. 〈hal-00729049〉

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