Real time bayesian estimation of the epidemic potential of emerging infectious diseases.
Real time bayesian estimation of the epidemic potential of emerging infectious diseases.
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DOI：
10.1371/journal.pone.0002185
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发表时间：
2008-05-14
期刊：
影响因子：
3.7
通讯作者：
Ribeiro RM
中科院分区：
文献类型：
--
作者：
Bettencourt LM;Ribeiro RM
全球人口结构的快速变化，加上流动性的增加和土地用途的改变，使新出现的传染病的威胁日益重要。目前，全世界都在警惕H5N1禽流感变得像季节性流感一样在人类中传播，并可能导致前所未有的大流行。在这里，我们展示了如何在真实的时间内解释新发传染病的流行病学监测数据，以评估具有量化不确定性的传播率变化，并对新病例进行运行时间预测，并指导物流分配。我们开发了一个扩展的标准流行病学模型，适用于新出现的传染病，描述的概率进展的情况下，由于并发的影响（初期）人类传播和多个引进水库。该模型是投在监测观测，并立即建议一个简单的图形估计程序的有效再生数R（平均数的情况下产生的传染性个人）的标准流行病。对于新出现的传染病，通常表现出较大的相对病例数波动随着时间的推移，我们开发了一个贝叶斯方案的真实的时间估计的概率分布的有效繁殖数，并显示如何使用这样的推论，制定显着性检验未来的流行病学观察。违反这些显著性检验定义的统计异常可能标志着新出现的疾病的流行病学变化，并应引发进一步的实地调查。我们将该方法应用于世界卫生组织报告的病例数据，以确定目前H5N1流感在人类中的传播能力，并建立一个统计基础，用于监测其真实的演变。
Fast changes in human demographics worldwide, coupled with increased mobility, and modified land uses make the threat of emerging infectious diseases increasingly important. Currently there is worldwide alert for H5N1 avian influenza becoming as transmissible in humans as seasonal influenza, and potentially causing a pandemic of unprecedented proportions. Here we show how epidemiological surveillance data for emerging infectious diseases can be interpreted in real time to assess changes in transmissibility with quantified uncertainty, and to perform running time predictions of new cases and guide logistics allocations. We develop an extension of standard epidemiological models, appropriate for emerging infectious diseases, that describes the probabilistic progression of case numbers due to the concurrent effects of (incipient) human transmission and multiple introductions from a reservoir. The model is cast in terms of surveillance observables and immediately suggests a simple graphical estimation procedure for the effective reproductive number R (mean number of cases generated by an infectious individual) of standard epidemics. For emerging infectious diseases, which typically show large relative case number fluctuations over time, we develop a Bayesian scheme for real time estimation of the probability distribution of the effective reproduction number and show how to use such inferences to formulate significance tests on future epidemiological observations. Violations of these significance tests define statistical anomalies that may signal changes in the epidemiology of emerging diseases and should trigger further field investigation. We apply the methodology to case data from World Health Organization reports to place bounds on the current transmissibility of H5N1 influenza in humans and establish a statistical basis for monitoring its evolution in real time.
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