A Bayesian spatio-temporal method for disease outbreak detection.
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.
Jiang
Xia
X
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. xij6@pitt.edu
Cooper
Gregory F
GF
D001499:Bayes Theorem; D003198:Computer Simulation; D003457:Cryptosporidiosis / Q000453:epidemiology / Q000517:prevention & control; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology / Q000517:prevention & control; D010363:Pattern Recognition, Automated; D010414:Pennsylvania / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods*; D012680:Sensitivity and Specificity; D013029:Space-Time Clustering