A Temporal Extension of the Bayesian Aerosol Release Detector

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.

• Type: Conference
• Year: 2008
• Where: BIOSURVEILLANCE
• Authors: Xiaohui Kong, Garrick L. Wallstrom, William R. Hogan
• Keywords: Bacillus Anthracis, BIOSURVEILLANCE 2008, Early Detection, Outdoor Aerosol, Security Privacy