Bayesian Information Fusion Networks for Biosurveillance Applications
- Affiliation of the authors: The Johns Hopkins University Applied Physics Laboratory (JHU/APL), Laurel, MD
- Correspondence: Zaruhi R. Mnatsakanyan, PhD, The Johns Hopkins University Applied Physics Laboratory (JHU/APL), 11100 Johns Hopkins Road, Laurel, MD 20723
- Received 10 October 2007
- Accepted 16 August 2009
Abstract
This study introduces new information fusion algorithms to enhance disease surveillance systems with Bayesian decision support capabilities. A detection system was built and tested using chief complaints from emergency department visits, International Classification of Diseases Revision 9 (ICD-9) codes from records of outpatient visits to civilian and military facilities, and influenza surveillance data from health departments in the National Capital Region (NCR). Data anomalies were identified and distribution of time offsets between events in the multiple data streams were established. The Bayesian Network was built to fuse data from multiple sources and identify influenza-like epidemiologically relevant events. Results showed increased specificity compared with the alerts generated by temporal anomaly detection algorithms currently deployed by NCR health departments. Further research should be done to investigate correlations between data sources for efficient fusion of the collected data.
Footnotes
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This article was supported in part by a Stuart S. Janney Fellowship award from The Johns Hopkins University Applied Physics Laboratory and Grant Number P01 HK000028-02 from the Centers for Disease Control and Prevention (CDC). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC.
