Bayesian Analysis for Emerging Infectious Diseases
Bayesian Analysis for Emerging Infectious Diseases
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DOI：
10.1214/09-ba417
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发表时间：
2009-01-01
影响因子：
4.4
通讯作者：
Roberts, Gareth O.
中科院分区：
文献类型：
--
作者：
Jewell, Chris P.;Kypraios, Theodore;Roberts, Gareth O.
人类和动物群体中的传染病往往造成严重的健康和社会经济风险。从统计学的角度来看，他们的预测是复杂的，因为没有两种流行病是相同的，因为接触习惯的改变，传染媒介的突变，以及人类和动物对流行病的反应行为的改变。因此，控制感染机制的模型参数通常是未知的。另一方面，随着数据的积累，需要迅速决定流行病控制战略。在本文中，我们提出了一个完整的贝叶斯方法进行推理和在线预测的流行病在结构化的人口。我们的方法的主要特点是MCMC-（和自适应MCMC-）为基础的方法的参数估计，流行病预测和在线评估目前未观察到的感染的风险的发展。我们使用两个互补的研究来说明我们的方法：2001年英国口蹄疫疫情的分析，并模拟未来可能发生的禽流感疫情对英国家禽业的潜在风险。
Infectious diseases both within human and animal populations often pose serious health and socioeconomic risks. From a statistical perspective, their prediction is complicated by the fact that no two epidemics are identical due to changing contact habits, mutations of infectious agents, and changing human and animal behaviour in response to the presence of an epidemic. Thus model parameters governing infectious mechanisms will typically be unknown. On the other hand, epidemic control strategies need to be decided rapidly as data accumulate. In this paper we present a fully Bayesian methodology for performing inference and online prediction for epidemics in structured populations. Key features of our approach are the development of an MCMC- (and adaptive MCMC-) based methodology for parameter estimation, epidemic prediction, and online assessment of risk from currently unobserved infections. We illustrate our methods using two complementary studies: an analysis of the 2001 UK Foot and Mouth epidemic, and modelling the potential risk from a possible future Avian Influenza epidemic to the UK Poultry industry.