A Bayesian spatio-temporal method for disease outbreak detection

J Am Med Inform Assoc. Jul-Aug 2010;17(4):462-71. doi: 10.1136/jamia.2009.000356.

Abstract

A system that monitors a region for a disease outbreak is called a disease outbreak surveillance system. A spatial surveillance system searches for patterns of disease outbreak in spatial subregions of the monitored region. A temporal surveillance system looks for emerging patterns of outbreak disease by analyzing how patterns have changed during recent periods of time. If a non-spatial, non-temporal system could be converted to a spatio-temporal one, the performance of the system might be improved in terms of early detection, accuracy, and reliability. A Bayesian network framework is proposed for a class of space-time surveillance systems called BNST. The framework is applied to a non-spatial, non-temporal disease outbreak detection system called PC in order to create the spatio-temporal system called PCTS. Differences in the detection performance of PC and PCTS are examined. The results show that the spatio-temporal Bayesian approach performs well, relative to the non-spatial, non-temporal approach.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Bayes Theorem*
  • Computer Simulation
  • Cryptosporidiosis / epidemiology
  • Cryptosporidiosis / prevention & control
  • Disease Outbreaks / statistics & numerical data*
  • Emergency Service, Hospital / statistics & numerical data
  • Humans
  • Influenza, Human / epidemiology
  • Influenza, Human / prevention & control
  • Pattern Recognition, Automated*
  • Pennsylvania / epidemiology
  • Population Surveillance / methods*
  • Sensitivity and Specificity
  • Space-Time Clustering*