期刊
STATISTICS IN MEDICINE
卷 31, 期 19, 页码 2123-2136出版社
WILEY
DOI: 10.1002/sim.5350
关键词
conditional autoregressive model; hierarchical model; hidden Markov model; influenza
类别
资金
- NSF [DMS-0914906, DMS-0914903, DMS-0914603, DMS-0914921, DMS-0112069]
- National Security Agency [H98230-11-1-0208]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1106435] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0914603, 0914906] Funding Source: National Science Foundation
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据