Bayesian Methodology for Spatio-Temporal Syndromic Surveillance (2010)

Abstract:

Early and accurate detection of outbreaks is one of the most important objectives of syndromic surveillance systems. We propose a general Bayesian framework for syndromic surveillance systems. The methodology incorporates Gaussian Markov random field (GMRF) and Spatio-Temporal conditional autoregressive (CAR) modeling. By contrast, most previous approaches have been based on only spatial or time series models. The model has appealing probabilistic representations as well as attractive statistical properties. Based on extensive simulation studies, the model is capable of capturing outbreaks rapidly, while still limiting false positives.

Keywords:

Syndromic surveillance, spatial statistics, Markov random field, spatio-temporal, conditional autoregressive process