A Spatio-Temporal Absorbing State Model for Disease and Syndromic Surveillance (2010)

Summary:

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. While many statistical models have been devised for surveillance purposes, none are able to simultaneously achieve important practical goals such as good sensitivity and specificity, proper use of domain information, inclusion of spatio-temporal dynamics, and transparent support to decision-makers. In an effort to achieve some of 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.

Keywords:

Conditional autoregressive model; Hierarchical model; Hidden Markov model; Influenza.