A Bayesian spatio-temporal method for disease outbreak detection

Jiang X, Cooper GF. A Bayesian spatio-temporal method for disease outbreak detection. Journal of the American Medical Informatics Association 17 (2010) 462-471. PMID: 20595315PMC2995651.

A system that monitors a region for a disease outbreak is called a disease outbreak surveillance system.Aspatial 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 spatiotemporal 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 nonspatial, non-temporal approach.

Publication Year: 
Publication Credits: 
Jiang X, Cooper GF
Publication Download: 
PDF icon Jiang.pdf735.85 KB