A spatio-temporal absorbing state model for disease and syndromic surveillance

Abstract:

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 pbkp_redict 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. © 2012 John Wiley & Sons, Ltd.

Año de publicación:

2012

Keywords:

• influenza
• Hierarchical model
• Hidden markov model
• Conditional autoregressive model

Fuente:

scopus

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