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A Modular Bayesian Salmonella Source Attribution Model for Sparse Data

机译：稀疏数据的模块化贝叶斯沙门氏菌源归因模型

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摘要

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.
机译：在文献中介绍了许多用于沙门氏菌源归因的统计模型。但是，这些模型通常被发现对模型参数化敏感，以及所使用的数据集的细节。此处提供的贝叶斯轿车源归因归因模型是开发的，通常适用于多年来获得的小而稀疏的年度数据集。完整的贝叶斯模型被模块化为三个部分（曝光模型，子类型分布模型和流行病学模型），以便在每个模块中分别估计未知参数。拟议的模型利用所研究来源的消费和整体沙门氏菌，以及邻近年份的细菌类型。后者用于平滑估计每个来源中不同沙门氏菌亚型的年相对比例。流行病学模型中包含特异性特异性效果和沙门氏菌亚型特异性效应，以描述源之间的差异以及亚型之间的感染人类的​​能力。这些参数的估计基于多年的数据。最后，该模型将来自不同模块的总证据结合在一起根据它们的来源比例的人类沙门氏菌病例。该模型用于分配报告的人类沙门氏菌病例，从2008年至2015年到八个食物来源。

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