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Abstract：
Preventing and controlling zoonoses through the design and implementation of public health policies requires a thorough understanding of transmission pathways. Modelling jointly the epidemiological data and genetic information of microbial isolates derived from cases provides a methodology for tracing back the source of infection. In this paper, the attribution probability for human cases of campylobacteriosis for each source, conditional on the extent to which each case resides in a rural compared to urban environment, is estimated. A model that incorporates genetic data and evolutionary processes is applied alongside a newly developed genetic-free model. We show that inference from each model is comparable except for rare microbial genotypes. Further, the effect of \'rurality\' may be modelled linearly on the logit scale, with increasing rurality leading to the increasing likelihood of ruminant-sourced campylobacteriosis.
摘要：
通过公共卫生政策的设计和实施来预防和控制人畜共患病需要对传播途径有透彻的了解。联合对从病例中获得的微生物分离株的流行病学数据和遗传信息进行建模，为追溯感染源提供了一种方法。在本文中,每种来源的弯曲杆菌感染人类病例的归因概率，条件是每个案例在农村居住的程度与城市环境相比，估计。结合遗传数据和进化过程的模型与新开发的无遗传模型一起应用。我们表明，除了罕见的微生物基因型外，每个模型的推断都是可比的。Further,“乡村性”的影响可以在Logit尺度上线性建模，随着农村的增加，导致反刍动物来源的弯曲杆菌病的可能性增加。
