Viral Disease and Epidemiology Research Division, National Institute of Animal Health, National Agriculture Research Organization, Tsukuba, Japan.
Faculty of Veterinary and Agricultural Sciences, Melbourne Veterinary School, Asia-Pacific Centre for Animal Health, The University of Melbourne, Parkville, Victoria, Australia.
Transbound Emerg Dis. 2019 Sep;66(5):2074-2086. doi: 10.1111/tbed.13256. Epub 2019 Jun 11.
Research aimed at understanding transmission networks, representing a network of "who infected whom" for an infectious disease outbreak, have been actively conducted in recent years. Transmission network models incorporating epidemiological and genetic data are valuable for elucidating disease transmission pathways. In this study, we reconstructed the transmission network of the foot-and-mouth disease (FMD) epidemic in Japan in 2010, and explored farm-level risk factors associated with increased risk of secondary transmission. A published, systematic Bayesian transmission network model was applied to epidemiological data of 292 infected farms and whole genome sequence data of 104 of the infected farms. This model can make inferences for known infected farms even lacking genetic data. After estimating the consensus network, the accuracy of the network was examined by comparison with epidemiological data. Then, risk factors inferred to have been sources of secondary transmission were explored using zero-inflated Poisson regression model. As far as we are aware, this study represents the largest FMD outbreak transmission network to be published by such means combining epidemiological and genetic data. The consensus network reasonably generated the epidemiological links, which were estimated from the actual epidemiological investigation. Among 292 farms, 101 farms (35%) were inferred to have been the sources of secondary transmission, and amongst these farms, the median number of secondary cases was 2 (min:1-max:18) farms. The farm-type (small and large -sized pig farms), the number of days from onset to notification, and the number of susceptible farms within a 1-km radius were significantly associated with secondary transmission. Transmission network modelling enabled inference of the connections between infected farms during the FMD epidemic and identified important factors for controlling the risk of secondary transmission. This study demonstrated that the predominant susceptible species held on a farm, farm size, and animal density were associated with increased onwards transmission.
近年来，人们积极开展了旨在了解传染病传播网络（代表传染病暴发的“谁感染了谁”网络）的研究。将流行病学和遗传数据纳入其中的传播网络模型对于阐明疾病传播途径很有价值。在这项研究中，我们重建了 2010 年日本口蹄疫（FMD）疫情的传播网络，并探讨了与继发传播风险增加相关的农场层面的风险因素。我们应用了已发表的、系统的贝叶斯传播网络模型，该模型整合了 292 个感染农场的流行病学数据和 104 个感染农场的全基因组序列数据。即使缺乏遗传数据，该模型也可以对已知感染农场进行推断。在估计共识网络后，通过与流行病学数据的比较来检查网络的准确性。然后，使用零膨胀泊松回归模型探讨推断为继发传播源的风险因素。据我们所知，这是使用这种结合流行病学和遗传数据的方法发表的最大规模的 FMD 暴发传播网络。共识网络合理地生成了从实际流行病学调查中估计出来的流行病学联系。在 292 个农场中，有 101 个农场（35%）被推断为继发传播源，在这些农场中，继发病例的中位数为 2 个（最小：1-最大：18）农场。农场类型（大小型猪农场）、从发病到通报的天数以及 1 公里半径内易感农场的数量与继发传播显著相关。传播网络建模能够推断口蹄疫疫情期间感染农场之间的联系，并确定控制继发传播风险的重要因素。本研究表明，农场中主要的易感物种、农场规模和动物密度与传播增加有关。
