Source attribution methods attribute human cases of a zoonotic disease to a certain source putatively responsible for this disease. Identifying and quantifying the contribution of different sources to human infections is important for taking appropriate actions for reducing the exposure of the consumer to zoonotic pathogens. One widely used method is the microbial subtyping approach, whose principle is to compare the frequency of pathogen subtypes from different sources (e.g. animals or food) with the frequency of these subtypes in human cases. This paper studies the relationship between a Bayesian microbial subtyping approach described by Hald and coworkers subsequently modified by David and coworkers, here called the Hald model, and a frequentist approach known as the “Dutch model.” The comparison between the Bayesian and frequentist model is done for two data sets on salmonellosis in Germany from different time periods (year 2004–2007 and 2010–2011). The results of both approaches are in good agreement with each other for the used data. It is shown here mathematically that a certain parameterization can be used to transform the probabilistic Hald model into a deterministic form, which is equivalent to the Dutch model. That certain parameterization secures independence of the model outcomes from the choice of so‐called unique subtypes (which are unique in the sense that they are found exclusively in one of the sources). It is shown that deviating from that certain parameterization leads variations in the model outcome dependent on which unique subtypes are chosen in the process of modelling.

中文翻译：

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参数化在比较基于沙门氏菌微生物亚型的来源归因模型中的作用。

来源归因方法将人畜共患疾病的人类病例归因于某种推定对该疾病负责的来源。识别和量化不同来源对人类感染的贡献，对于采取适当措施减少消费者对人畜共患病原体的暴露至关重要。一种广泛使用的方法是微生物分型方法，其原理是将不同来源（例如动物或食物）的病原体亚型的频率与人类病例中这些亚型的频率进行比较。本文研究了Hald所描述的贝叶斯微生物分型方法与后来由David和其同事修改的同事（这里称为Hald模型）与一种被称为“荷兰模型”的常客方法之间的关系。针对不同时期（2004-2007年和2010-2011年）德国沙门氏菌病的两个数据集，对贝叶斯模型和常人模型进行了比较。对于所使用的数据，两种方法的结果彼此非常吻合。在数学上显示，可以使用某些参数化将概率Hald模型转换为确定性形式，该形式等同于Dutch模型。某些参数化可确保从选择所谓的唯一子类型（在它们仅在一种来源中唯一的意义上说是唯一的）的选择中确保模型结果的独立性。结果表明，偏离某些参数会导致模型结果的变化，具体取决于在建模过程中选择了哪些唯一的子类型。