Bayesian methodology for the analysis of spatial–temporal surveillance data

Authors:
Jian Zou;Alan F. Karr;David Banks;Matthew J. Heaton;Gauri Datta;James Lynch;Francisco Vera
Affiliations:
National Institute of Statistical Sciences, Research Triangle Park, NC, USA;National Institute of Statistical Sciences, Research Triangle Park, NC, USA;Department of Statistical Science, Duke University, Durham, NC, USA;Department of Statistical Science, Duke University, Durham, NC, USA;Department of Statistics, University of Georgia, Athens, GA, USA;Department of Statistics, University of South Carolina, Columbia, SC, USA;Escuela Superior Politecnica del Litoral, Ecuador, and Clemson University, Clemson, SC, USA
Venue:
Statistical Analysis and Data Mining
Year:
2012

Citing 3
Cited 0

Bayesian forecasting and dynamic models (2nd ed.)

Bayesian forecasting and dynamic models (2nd ed.)
Infectious Disease Informatics: Syndromic Surveillance for Public Health and Bio-Defense

Infectious Disease Informatics: Syndromic Surveillance for Public Health and Bio-Defense
Bayesian CAR models for syndromic surveillance on multiple data streams: Theory and practice

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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. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 5: 194–204, 2012 © 2012 Wiley Periodicals, Inc.