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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Heaton, MJ; Banks, DL; Zou, J; Karr, AF; Datta, G; Lynch, J; Vera, F

Published Date

  • August 2012

Published In

Volume / Issue

  • 31 / 19

Start / End Page

  • 2123 - 2136

PubMed ID

  • 22388709

Pubmed Central ID

  • 22388709

Electronic International Standard Serial Number (EISSN)

  • 1097-0258

International Standard Serial Number (ISSN)

  • 0277-6715

Digital Object Identifier (DOI)

  • 10.1002/sim.5350

Language

  • eng