Risk Assessment Unit, Finnish Food Authority, Helsinki, Finland.
Risk Anal. 2019 Aug;39(8):1796-1811. doi: 10.1111/risa.13310. Epub 2019 Mar 20.
Several statistical models for salmonella source attribution have been presented in the literature. However, these models have often been found to be sensitive to the model parameterization, as well as the specifics of the data set used. The Bayesian salmonella source attribution model presented here was developed to be generally applicable with small and sparse annual data sets obtained over several years. The full Bayesian model was modularized into three parts (an exposure model, a subtype distribution model, and an epidemiological model) in order to separately estimate unknown parameters in each module. The proposed model takes advantage of the consumption and overall salmonella prevalence of the studied sources, as well as bacteria typing results from adjacent years. The latter were used for a smoothed estimation of the annual relative proportions of different salmonella subtypes in each of the sources. The source-specific effects and the salmonella subtype-specific effects were included in the epidemiological model to describe the differences between sources and between subtypes in their ability to infect humans. The estimation of these parameters was based on data from multiple years. Finally, the model combines the total evidence from different modules to proportion human salmonellosis cases according to their sources. The model was applied to allocate reported human salmonellosis cases from the years 2008 to 2015 to eight food sources.
文献中提出了几种用于沙门氏菌溯源的统计模型。然而，这些模型往往对模型参数化以及所使用的数据集的具体情况非常敏感。本文提出的贝叶斯沙门氏菌溯源模型旨在与多年获得的小型稀疏年度数据集具有普遍适用性。完整的贝叶斯模型被模块化分为三个部分（暴露模型、亚型分布模型和流行病学模型），以便分别估计每个模块中的未知参数。所提出的模型利用了所研究来源的消费和总体沙门氏菌流行率，以及相邻年份的细菌分型结果。后者用于平滑估计每个来源中不同沙门氏菌亚型的年度相对比例。在流行病学模型中纳入了来源特异性效应和沙门氏菌亚型特异性效应，以描述来源之间以及不同亚型之间感染人类的能力差异。这些参数的估计基于多年的数据。最后，该模型结合了不同模块的全部证据，根据来源对人类沙门氏菌病病例进行分类。该模型应用于将 2008 年至 2015 年报告的人类沙门氏菌病病例分配到 8 种食物来源。
