A temporal extension of the Bayesian aerosol release detector

Xiaohui Kong, Garrick L. Wallstrom, William R. Hogan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationBiosurveillance and Biosecurity - International Workshop, BioSecure 2008, Proceedings
Number of pages11
StatePublished - 2008
Externally publishedYes
EventInternational Workshop on Biosurveillance and Biosecurity, BioSecure 2008 - Raleigh, NC, United States
Duration: Dec 2 2008Dec 2 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5354 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherInternational Workshop on Biosurveillance and Biosecurity, BioSecure 2008
Country/TerritoryUnited States
CityRaleigh, NC


  • Anthrax outbreak
  • Bayesian inference
  • Spatial-temporal pattern recognition
  • Syndromic surveillance

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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