Department of Statistical Science, Duke University, Box 90251, Durham, NC, 27708-0251, USA.
Stat Med. 2012 Aug 30;31(19):2123-36. doi: 10.1002/sim.5350. Epub 2012 Mar 2.
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.
可靠的监测模型是公共卫生的重要工具，因为它们有助于减轻疾病爆发，识别疾病爆发的地点和时间，并预测未来的发生。尽管已经设计了许多用于监测目的的统计模型，但没有一个模型能够同时实现良好的敏感性和特异性、协变量信息的正确使用、时空动态的包含以及对决策者的透明支持等重要实际目标。为了实现这些目标，本文提出了一种具有吸收状态的时空条件自回归隐马尔可夫模型。该模型在大型模拟研究和流感/肺炎病死率数据的应用中表现良好。
