Redefining the "carrier" state for foot-and-mouth disease from the dynamics of virus persistence in endemically affected cattle populations
Redefining the "carrier" state for foot-and-mouth disease from the dynamics of virus persistence in endemically affected cattle populations
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DOI：
10.1038/srep29059
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发表时间：
2016-07-06
影响因子：
4.6
通讯作者：
Morgan, Kenton L.
中科院分区：
文献类型：
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作者：
Bronsvoort, Barend M. deC.;Handel, Ian G.;Morgan, Kenton L.
1959年，货车Bekkum定义了口蹄疫病毒（FMDV）的“携带者”状态。它基于感染后28天或更长时间的感染性病毒的恢复，并且已经成为用于实验研究的有用构建体。使用历史数据从1,107牛，收集作为一个人口为基础的研究的一部分，流行性口蹄疫在2000年，我们开发了一个混合效应的逻辑回归模型来预测恢复可行的口蹄疫病毒的概率，由probang和文化，条件动物的年龄和时间，因为最近一次报告的爆发。我们构建了第二组模型来预测动物在三种常见的非结构蛋白（NSP）ELISA中的抗体应答及其年龄为先证者阳性的概率。我们认为，在自然生态环境中，目前的定义“载体”未能捕捉到的动态，无论是持久性的病毒（恢复使用probangs测量）或不确定性，从这些动物的传播，该术语意味着。在这些方面，它不是特别有用。因此，我们提出了第一个预测统计模型，用于识别持续感染的牛在流行的设置，捕捉一些动态的持久性的概率。此外，我们还提供了一套与NSP ELISA一起使用的预测工具，以帮助靶向持续感染的牛。
The foot-and-mouth disease virus (FMDV) "carrier" state was defined by van Bekkum in 1959. It was based on the recovery of infectious virus 28 days or more post infection and has been a useful construct for experimental studies. Using historic data from 1,107 cattle, collected as part of a population based study of endemic FMD in 2000, we developed a mixed effects logistic regression model to predict the probability of recovering viable FMDV by probang and culture, conditional on the animal's age and time since last reported outbreak. We constructed a second set of models to predict the probability of an animal being probang positive given its antibody response in three common non-structural protein (NSP) ELISAs and its age. We argue that, in natural ecological settings, the current definition of a "carrier" fails to capture the dynamics of either persistence of the virus (as measured by recovery using probangs) or the uncertainty in transmission from such animals that the term implies. In these respects it is not particularly useful. We therefore propose the first predictive statistical models for identifying persistently infected cattle in an endemic setting that captures some of the dynamics of the probability of persistence. Furthermore, we provide a set of predictive tools to use alongside NSP ELISAs to help target persistently infected cattle.