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

机译：贝叶斯微生物亚型归因模型：先验信息的稳健性和命题

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摘要

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.
机译：将食源性疾病归因于食物来源对于构想，确定优先次序和评估公共卫生政策措施的影响至关重要。 Hald等人的贝叶斯微生物亚型归因模型。是将零星案件归类的最先进方法之一；也就是说，它可以考虑到源的暴露水平以及细菌类型之间以及源之间的差异。向前迈出的一步需要引入类型和与源相关的参数，并产生过参数化，这在Hald的论文中已解决，方法是将一些参数设置为常数。我们质疑为参数化做出的选择（参数集和所使用的值）对模型鲁棒性的影响，并提出针对Hald模型的替代参数化方法。我们用非伤寒沙门氏菌的2005年法国数据集说明了这种分析。 Mullner修改后的Hald模型和简单的确定性模型用于比较结果并评估估计的准确性。设置特定于特定来源（而不是最常见来源）的细菌类型的参数，并使用基于数据的值而不是任意值，可以提高估计的收敛性和充分性，并导致与其他模型的结果一致的归因估计。类型和源参数估计值也与Mullner的模型估计值一致。该模型似乎对参数化高度敏感。所提出的基于特定类型和基于数据的值的解决方案提高了估计的鲁棒性，并使得该非常有价值的工具能够与法国数据集一起成功使用。

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  • 来源
    《Risk analysis》 |2013年第3期|397-408|共12页
  • 作者

    J. M. David; D. Guillemot; N. Bemrah; A. Thebault; A. Brisabois; M. Chemaly; FX. Weill; P. Sanders; L. Watier;

  • 作者单位

    Anses, BP 90203 Fougeres, F-35302, France,INSERM, U 657, Paris, F-75015, France,Institut Pasteur, Pharmacoepidemiologie et Maladies Infectieuses, Paris, F-75015, France,Universite Versailles Saint Quentin, EA4499, F-Garches 92380,France;

    INSERM, U 657, Paris, F-75015, France,Institut Pasteur, Pharmacoepidemiologie et Maladies Infectieuses, Paris, F-75015, France,Universite Versailles Saint Quentin, EA4499, F-Garches 92380,France;

    Anses, Maisons-Alfort, F-94701, France;

    Anses, Maisons-Alfort, F-94701, France;

    Anses, Maisons-Alfort, F-94701, France;

    Anses, BP 53, Ploufragan, F-22440, France;

    Institut Pasteur, Centre National de Reference des Salmonella,Unite des Bacteries Pathogenes Enteriques, Paris, F-75015,France;

    Anses, BP 90203 Fougeres, F-35302, France;

    INSERM, U 657, Paris, F-75015, France,Institut Pasteur, Pharmacoepidemiologie et Maladies Infectieuses, Paris, F-75015, France,Universite Versailles Saint Quentin, EA4499, F-Garches 92380,France,Inserm U657,UVSQ EA 4499, Unite de Sante Publique, Hopital Raymond Poincare, Bat Rabelais, 104 av R. Poincare, F-92380 Garches,France;

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  • 正文语种 eng
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  • 关键词

    bayesian model; epidemiology; sensitivity analysis; source attribution;

    机译：贝叶斯模型流行病学敏感性分析;来源归因;

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