A Bayesian spatio-temporal method for disease outbreak detection
- Correspondence to Dr Xia Jiang, 183M Parkvale Building, 200 Meyran Avenue, Pittsburgh, PA 15260, USA;
- Received 4 May 2009
- Accepted 27 April 2010
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.
- Disease outbreak detection
- Bayesian networks
- spatial scan statistic
Funding This research was supported by grant IIS-0325581 from the National Science Foundation.
Competing interests None.
Ethics approval This study was conducted with the approval of the University of Pittsburgh.
Provenance and peer review Not commissioned; externally peer reviewed.