A Temporal Extension of the Bayesian Aerosol Release Detector
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Early detection of bio-terrorist attacks is an important problem in public health surveillance. In this paper, we focus on the detection and characterization of outdoor aerosol releases of Bacillus anthracis. Recent research has shown promising results of early detection using Bayesian inference from syndromic data in conjunction with meteorological and geographical data . Here we propose an extension of this algorithm that models multiple days of syndromic data to better exploit the temporal characteristics of anthrax outbreaks. Motivations, mechanism and evaluation of our proposed algorithm are described and discussed. An improvement is shown in timeliness of detection on simulated outdoor aerosol Bacillus anthracis releases.
KeywordsAnthrax outbreak syndromic surveillance Bayesian inference spatial-temporal pattern recognition
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- 8.Lombardo, J.: The ESSENCE disease surveillance test bed for the National Capital Area. Johns Hopkins APL Technical Digest, 327–334 (2003)Google Scholar
- 11.Tuner’s Method (Accessed 2005 March 15) (2002), http://www.webmet.com/met_monitoring/641.html
- 13.Wong, W.K., et al.: WSARE: What’s Strange About Recent Events? Journal of Urban Health 80(2 suppl 1), 66–75 (2003)Google Scholar
- 14.Fawcett, T., Provost, F.: Activity monitoring: noticing interesting changes in behavior. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, San Diego (1999)Google Scholar