A Temporal Extension of the Bayesian Aerosol Release Detector

Authors:
Xiaohui Kong;Garrick L. Wallstrom;William R. Hogan
Affiliations:
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15260;Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15260;Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15260
Venue:
BioSecure '08 Proceedings of the 2008 International Workshop on Biosurveillance and Biosecurity
Year:
2008

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Abstract

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 [1]. 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.