Evidence-based controls for epidemics using spatio-temporal stochastic models in a Bayesian framework
Evidence-based controls for epidemics using spatio-temporal stochastic models in a Bayesian framework
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DOI：
10.1098/rsif.2017.0386
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发表时间：
2017-11-01
影响因子：
3.9
通讯作者：
Gibson, Gavin J.
中科院分区：
文献类型：
--
作者：
Adrakey, Hola K.;Streftaris, George;Gibson, Gavin J.
控制农业和种植园作物及牲畜的高度传染性疾病是流行病学和生态建模的一个关键挑战，实施的控制战略往往存在争议。数学模型，包括这里考虑的时空随机模型，在控制设计中发挥着越来越大的作用，因为机构寻求加强所选择的策略所依据的证据。在这里，我们调查的一般方法通知控制策略的选择，使用贝叶斯框架内的时空模型。我们说明的方法的情况下，战略的基础上先发制人地删除个别主机。对于一个示例模型，使用佛罗里达亚洲柑橘溃疡病流行的模拟数据和历史数据，我们评估了一系列考虑到对新出现流行病的观察结果的优先考虑个人清除的措施。这些措施基于宿主对易感个体造成的潜在感染危害（危害）、宿主感染的可能性（风险）以及危害和风险（威胁）相结合的措施。我们发现，威胁措施通常会导致最有效的控制策略，特别是在资源稀缺的聚集性流行病。扩展的方法，以一系列的其他设置进行了讨论。该方法的一个关键特征是使用流行病模型的函数模型表示来耦合不同控制策略下的流行病轨迹。这导致了在各自控制下的流行病结果之间的强正相关，有助于减少结果差异的方差，从而减少广泛模拟的必要性。
The control of highly infectious diseases of agricultural and plantation crops and livestock represents a key challenge in epidemiological and ecological modelling, with implemented control strategies often being controversial. Mathematical models, including the spatio-temporal stochastic models considered here, are playing an increasing role in the design of control as agencies seek to strengthen the evidence on which selected strategies are based. Here, we investigate a general approach to informing the choice of control strategies using spatio-temporal models within the Bayesian framework. We illustrate the approach for the case of strategies based on pre-emptive removal of individual hosts. For an exemplar model, using simulated data and historic data on an epidemic of Asiatic citrus canker in Florida, we assess a range of measures for prioritizing individuals for removal that take account of observations of an emerging epidemic. These measures are based on the potential infection hazard a host poses to susceptible individuals (hazard), the likelihood of infection of a host (risk) and a measure that combines both the hazard and risk (threat). We find that the threat measure typically leads to the most effective control strategies particularly for clustered epidemics when resources are scarce. The extension of the methods to a range of other settings is discussed. A key feature of the approach is the use of functional-model representations of the epidemic model to couple epidemic trajectories under different control strategies. This induces strong positive correlations between the epidemic outcomes under the respective controls, serving to reduce both the variance of the difference in outcomes and, consequently, the need for extensive simulation.