A spatio-temporal absorbing state model for disease and syndromic surveillance
A spatio-temporal absorbing state model for disease and syndromic surveillance
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DOI：
10.1002/sim.5350
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发表时间：
2012-08-30
影响因子：
2
通讯作者：
Vera, Francisco
中科院分区：
文献类型：
--
作者：
Heaton, Matthew J.;Banks, David L.;Vera, Francisco
可靠的监测模型是公共卫生领域的一个重要工具，因为它们有助于减轻疾病爆发，确定疾病爆发的地点和时间，并预测未来的发生。虽然许多统计模型已被设计用于监测目的，没有一个能够同时实现重要的实际目标，良好的灵敏度和特异性，正确使用协变量信息，包括时空动态，透明的支持决策者。为了实现这些目标，本文提出了一个时空条件自回归隐马尔可夫模型的吸收状态。该模型在大型模拟研究和流感/肺炎死亡率数据的应用中表现良好。版权所有（C）2012约翰威利父子有限公司
Reliable surveillance models are an important tool in public health because they aid in mitigating disease outbreaks, identify where and when disease outbreaks occur, and predict future occurrences. Although many statistical models have been devised for surveillance purposes, none are able to simultaneously achieve the important practical goals of good sensitivity and specificity, proper use of covariate information, inclusion of spatio-temporal dynamics, and transparent support to decision-makers. In an effort to achieve these goals, this paper proposes a spatio-temporal conditional autoregressive hidden Markov model with an absorbing state. The model performs well in both a large simulation study and in an application to influenza/pneumonia fatality data. Copyright (C) 2012 John Wiley & Sons, Ltd.