Naturally Occurring Incidents as Facsimiles for Biochemical Terrorist Attacks

  • Jamie L. Griffiths
  • Donald J. Berndt
  • Alan R. Hevner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3975)


Research on techniques for effective bioterrorism surveillance is limited by the availability of data from actual bioterrorism incidents. This research explores the potential contribution of naturally occurring incidents, such as Florida wildfires, as reasonable facsimiles for airborne bioterrorist attacks. Hospital discharge data on respiratory illnesses are analyzed to uncover patterns that might resemble the effects of an aerosolized biological or chemical attack. Previous research [3] is extended by (1) utilizing Geographic Information Systems (GIS) to introduce appropriate spatial data and (2) increasing the sophistication of the spatial analysis by applying the retrospective space-time permutation model available through SaTScanTM. Initial results are promising and lead to a confirmation that Florida wildfires are potentially interesting surrogates for aerosolized biochemical terrorist attacks. Research implications are discussed in reference to the on-going development of effective bioterrorism surveillance systems.


Geographic Information System West Nile Virus Syndromic Surveillance Hospital Discharge Data Broward County 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Alexander, D.A., Klein, S.: Biochemical Terrorism: Too Awful to Contemplate, Too Serious to Ignore. British Journal of Psychiatry 183, 491–497 (2003)CrossRefGoogle Scholar
  2. 2.
    Al-Rawi, K.R., Casinova, J.L., Romo, A.: IFEMS: a new approach for monitoring wild fire evolution with NOAA-AVHRR imagery. International Journal of Remote Sensing 22(10), 2033–2042 (2001)CrossRefGoogle Scholar
  3. 3.
    Berndt, D.J., Bhat, S., Fisher, J.W., Hevner, A.R., Studnicki, J.: Data Analytics for Bioterrorism Surveillance. In: Chen, H., Moore, R., Zeng, D.D., Leavitt, J. (eds.) ISI 2004. LNCS, vol. 3073, pp. 17–27. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Berndt, D.J., Fisher, J., Hevner, A., Studnicki, J.: Data Warehousing and Quality Assurance. IEEE Computer 34(12), 33–42 (2001)Google Scholar
  5. 5.
    Berndt, D.J., Hevner, A., Studnicki, J.: The CATCH Data Warehouse: Support for Community Health Care Decision Making. Decision Support Systems 35, 367–384 (2003)CrossRefGoogle Scholar
  6. 6.
    Berndt, D.J., Hevner, A.R., Studnicki, J.: Bioterrorism surveillance with real-time data warehousing. In: Chen, H., Miranda, R., Zeng, D.D., Demchak, C.C., Schroeder, J., Madhusudan, T. (eds.) ISI 2003. LNCS, vol. 2665, pp. 322–335. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Cambel, P.: Current Population Reports: Population Projections: 1995-2025. US Bureau of the Census (1997)Google Scholar
  8. 8.
    Burkom, H.S., Elbert, Y., Feldman, A., Lin, J.: Role of Data Aggregation in Biosurveillance Detection Strategies with Applications from ESSENSE. MMWR 53, 67–73 (2004)Google Scholar
  9. 9.
    Dwass, M.: Modified randomization tests for nonparametric hypothesis. Annals of Mathematical Statistics 28, 181–187 (1957)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Florida Wildfires Threaten all of Flagler County, 3 (July 1998),
  11. 11.
    Grahm-Rowe: Intelligence analysis software could predict attacks. New Scientist (2001)Google Scholar
  12. 12.
    Green, M.S., Kaufmann, Z.: Surveillance for Early Detection Monitoring of Infectious Disease Outbreak Associated with Bioterrorism. The Israeli Medical Association Journal 4, 503–506 (2002)Google Scholar
  13. 13.
    Greenfield, R.A., Brown, B.R., Hutchins, J.B., Iandolo, J.J., Jackson, R., Slater, L.N., Bronze, M.S.: Microbiologica, Biological, and Chemical Weapons of Warfare and Terrorism. The American Journal of Medical Sciences 323(6), 326–340 (2002)CrossRefGoogle Scholar
  14. 14.
    Hearne, S.A., Segal, L.M.: Leveraging the Nation’s Bioterrorism Investments: Foundation Efforts to Ensure A Revitalized Public Health System. Health Affairs 22(4), 230–234 (2003)CrossRefGoogle Scholar
  15. 15.
    Heffernan, R., Mostashari, F., Das, D., Karpati, A., Kulldorff, M., Weiss, D.: Syndromic Surveillance in Public Health Practice: The New York City emergency department system. Emerging Infectious Diseases 10, 858–864 (2004)Google Scholar
  16. 16.
    Huang, L., Kulldorff, M., Kassen, A.: A Spatial Scan Statistic for Survival Data. Manuscript (2005)Google Scholar
  17. 17.
    Jung, I., Kulldorff, M., Klassen, A.: A spatial scan statistic for ordinal data. Manuscript (2005)Google Scholar
  18. 18.
    Kleinman, K., Abrams, A., Kulldorff, M., Platt, R.: A model-adjusted space-time scan statistic with an application to syndromic surveillance. Epidemiology and Infectious Disease 133, 409–419 (2003)CrossRefGoogle Scholar
  19. 19.
    Krieger, N., Weterman, P., Chen, J., May-Jabeen, S.: Zip Code Caveat: Bias Due to Spatio-Temporal Mismatches Between Zip Codes and US Census – Defined Geographic Areas – The Public Health Disparities Project. American Journal of Public Health 92(7), 1100–1103 (2002)CrossRefGoogle Scholar
  20. 20.
    Kulldorff, M.: A spatial scan statistic. Communications in Statistics: Theory and Methods 26, 1481–1496 (1997)MATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Kulldorff, M.: Prospective time-periodic geographical disease surveillance using a scan statistic. Journal of the Royal Statistical Society, 61–72 (2001)Google Scholar
  22. 22.
    Kulldorff, M., Athas, W., Feuer, E., Miller, B., Key, C.: Evaluating Cluster Alarms: A space-time scan statistic and brain cancer in Los Alamos. American Journal of Public Health 88, 1377–1380 (1998)CrossRefGoogle Scholar
  23. 23.
    Kulldorff, M., Heffernan, R., Hartman, J., Assuncao, R.M., Mostashari, F.: A space-time permutation statistic for the early detection of disease outbreaks. PLoS Medicine 2, 216–224 (2005)CrossRefGoogle Scholar
  24. 24.
    Kulldorff, M.: Information Management Services, Inc.: SaTScanTM. Software for spatial and space-time scan statistics (computer program). Version 2.1 Bethesda, MD: National Cancer Institute (2005),
  25. 25.
    Kulldorff, M., Mostashari, F., Duczmal, L., Yih, K., Kleinman, K., Platt, R.: Multivariate spatial scan statistics for disease surveillance. Manuscript (2005)Google Scholar
  26. 26.
    Kulldorff, K., Nagarwalla, N.: Spatial disease clusters: Detection and Inference. Statistics in Medicine 14, 799–810 (1995)CrossRefGoogle Scholar
  27. 27.
    Kulldorff, K., Lazarus, R., Platt, R.: A generalized linear mixed models approach for detecting incident clusters of disease in small areas with application to biological terrorism. American Journal of Epidemiology, 217–224 (2004)Google Scholar
  28. 28.
    Minnesota Department of Health: Syndromic Surveillance: a New Tool to Detect Disease Outbreaks. Disease Control Newsletter 32, 16–17 (2004)Google Scholar
  29. 29.
    Lombardo, J., Burkom, H., Elbert, E., Magruder, S., Lewis, S.H., Loschen, W., Sari, J., Sniegoski, C., Wojcik, R., Pavilin, J.: A Systems Overview of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II). Journal of Urban Health 80(2), 32–41 (2003)Google Scholar
  30. 30.
    Mandl, K., Overhage, J., Wagner, M., Lober, W., Sebastiani, P., Mostashari, F., Pavlin, J., Gestland, P., Tradwell, T., Koki, E., Hutwagner, L., Buckeridge, D., Aller, R., Grannis, S.: Implementing Synromic Surveillance: a Practical Guide Informed by Early Experience. Journal of the American Medical Informatics Association 11(2), 141–150 (2004)CrossRefGoogle Scholar
  31. 31.
    Mostashari, F., Hartman, J.: Syndromic Surveillance: a Local Perspective. Journal of Urban Health 80(2) (2003)Google Scholar
  32. 32.
    Mostashari, F., Kulldorff, M., Hartman, J.J., Miller, J.R., Kulasekera, V.: Dead bird clustering: A potential early warning system for West Nile virus activity. Emerging Infectious Diseases 9, 641–646 (2003)Google Scholar
  33. 33.
    Nordin, J.D., Goodman, M.J., Kulldorff, M., Ritzwoller, D.P., Abrams, A.M., Kleinman, K., Levitt, M.J., Donahue, J., Platt, R.: Simulated anthrax attacks and syndromic surveillance. Emerging Infectious Diseases 11, 1394–1398 (2005)Google Scholar
  34. 34.
    Ostroff, S.: The CDC and Emergency Preparedness for the Elderly and Disabled: Testimonly before the Senate Special Committee on Aging – NY Field Hearing, February 22 (2002),
  35. 35.
    SaTScanTM Version History. Viewed November 7, Version 6. October 24 (2005),
  36. 36.
    SaTScanTM web site. Viewed November 7 (2005),
  37. 37.
    Surveillance of Morbidity during Wildfires – Central Florida 1998. In: MMWR, vol. 48(40) (1999)Google Scholar
  38. 38.
    United States Geological Survey (USGS), Geographic Information Systems (GIS) Poster, http://www.erg/isgs/gpv/isb/pubs/gis_poster/
  39. 39.
    Wildfires burn 70,000 acres in Everglades, April 19 (1999),
  40. 40.
    Wildfires Fact Sheet: Health Threat from Wildfire Smoke. Department of Health and Human Services. Center for Disease Control and Prevention (2003),
  41. 41.
    Yih, W.K., Caldwell, B., Harmon, R., Kleinman, K., Lazarus, K., Lazarus, R., Nelson, A., Nordin, J., Rehm, B.: National Bioterrorism Syndromic Surveillance Demonstration Program. MMWR 53, 43–46 (2004)Google Scholar
  42. 42.
    Yih, K., Abrams, A., Kleinman, K., Kulldorff, M., Nordin, J., Platt, R.: Ambulatory-care diagnosis as potential indicators of outbreaks of gastrointestinal illness – Minnesota. MMWR (suppl. 54), 157–162 (2004)Google Scholar
  43. 43.
    Zeng, D., Hsinchun, C., Lynch, C., Edson, M., Gotham, I.: Infectious Disease Informatics and Outbreak Detection. In: Medical Informatics, Ch. 13, pp. 359–395 (2006)Google Scholar
  44. 44.
    Zeng, X., Wagner, M.: Modeling Effects of Epidemics on Routinely Collected Data. Journal of the American Medical Informatics Association, (Suppl. 9), s17–s22 (2002)Google Scholar
  45. 45.
    ZipCode Data. Great Data Frequently Asked Questions,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jamie L. Griffiths
    • 1
  • Donald J. Berndt
    • 1
  • Alan R. Hevner
    • 1
  1. 1.University of South FloridaTampa

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