Advertisement

A Temporal Extension of the Bayesian Aerosol Release Detector

  • Xiaohui Kong
  • Garrick L. Wallstrom
  • William R. Hogan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5354)

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.

Keywords

Anthrax outbreak syndromic surveillance Bayesian inference spatial-temporal pattern recognition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hogan, W.R., et al.: The Bayesian aerosol release detector: an algorithm for detecting and characterizing outbreaks caused by an atmospheric release of Bacillus anthracis. Statistics in Medicine 26(29), 5225–5252 (2007)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Kaufmann, A.F., Meltzer, M.I., Schmid, G.P.: The economic impact of a bioterrorist attack: are prevention and postattack intervention programs justifiable? Emerging Infectious Diseases 3(2), 83–94 (1997)CrossRefGoogle Scholar
  3. 3.
    Wagner, M.M., et al.: The emerging science of very early detection of disease outbreaks. Journal of Public Health Management & Practice 7(6), 51–59 (2001)CrossRefGoogle Scholar
  4. 4.
    Buckeridge, D.L., et al.: Evaluating detection of an inhalational anthrax outbreak. Emerging Infectious Diseases 12(12), 1942–1949 (2006)CrossRefGoogle Scholar
  5. 5.
    Heffernan, R., et al.: Syndromic surveillance in public health practice, New York City [erratum appears in Emerg Infect Dis. 2006 September 12(9):1472]. Emerging Infectious Diseases 10(5), 858–864 (2004)CrossRefGoogle Scholar
  6. 6.
    Mandl, K.D., et al.: Implementing syndromic surveillance: a practical guide informed by the early experience. Journal of the American Medical Informatics Association 11(2), 141–150 (2004)CrossRefGoogle Scholar
  7. 7.
    Lewis, M.D., et al.: Disease outbreak detection system using syndromic data in the greater Washington DC area [see comment]. American Journal of Preventive Medicine 23(3), 180–186 (2002)CrossRefGoogle Scholar
  8. 8.
    Lombardo, J.: The ESSENCE disease surveillance test bed for the National Capital Area. Johns Hopkins APL Technical Digest, 327–334 (2003)Google Scholar
  9. 9.
    Tsui, F.C., et al.: Technical description of RODS: a real-time public health surveillance system. Journal of the American Medical Informatics Association 10(5), 399–408 (2003)CrossRefGoogle Scholar
  10. 10.
    Wagner, M.M., et al.: Design of a national retail data monitor for public health surveillance. Journal of the American Medical Informatics Association 10(5), 409–418 (2003)CrossRefGoogle Scholar
  11. 11.
    Tuner’s Method (Accessed 2005 March 15) (2002), http://www.webmet.com/met_monitoring/641.html
  12. 12.
    Buckeridge, D.L., et al.: Algorithms for rapid outbreak detection: a research synthesis. Journal of Biomedical Informatics 38(2), 99–113 (2005)CrossRefGoogle Scholar
  13. 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. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Xiaohui Kong
    • 1
  • Garrick L. Wallstrom
    • 1
  • William R. Hogan
    • 1
  1. 1.Department of Biomedical InformaticsUniversity of PittsburghPittsburghUSA

Personalised recommendations