Skip to main content

Naturally Occurring Incidents as Facsimiles for Biochemical Terrorist Attacks

  • Conference paper
Intelligence and Security Informatics (ISI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3975))

Included in the following conference series:

  • 1832 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alexander, D.A., Klein, S.: Biochemical Terrorism: Too Awful to Contemplate, Too Serious to Ignore. British Journal of Psychiatry 183, 491–497 (2003)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  7. Cambel, P.: Current Population Reports: Population Projections: 1995-2025. US Bureau of the Census (1997)

    Google Scholar 

  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. Dwass, M.: Modified randomization tests for nonparametric hypothesis. Annals of Mathematical Statistics 28, 181–187 (1957)

    Article  MATH  MathSciNet  Google Scholar 

  10. Florida Wildfires Threaten all of Flagler County, CNN.com 3 (July 1998), http://www.cnn.com/US/9807/03/florida.fires.01/

  11. Grahm-Rowe: Intelligence analysis software could predict attacks. New Scientist (2001)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Huang, L., Kulldorff, M., Kassen, A.: A Spatial Scan Statistic for Survival Data. Manuscript (2005)

    Google Scholar 

  17. Jung, I., Kulldorff, M., Klassen, A.: A spatial scan statistic for ordinal data. Manuscript (2005)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  20. Kulldorff, M.: A spatial scan statistic. Communications in Statistics: Theory and Methods 26, 1481–1496 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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), http://www.satscan.org

  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. Kulldorff, K., Nagarwalla, N.: Spatial disease clusters: Detection and Inference. Statistics in Medicine 14, 799–810 (1995)

    Article  Google Scholar 

  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. Minnesota Department of Health: Syndromic Surveillance: a New Tool to Detect Disease Outbreaks. Disease Control Newsletter 32, 16–17 (2004)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  31. Mostashari, F., Hartman, J.: Syndromic Surveillance: a Local Perspective. Journal of Urban Health 80(2) (2003)

    Google Scholar 

  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. 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. 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), http://www.cdc.gov/mmwr/preview/mmwrhtml/00056377.htm

  35. SaTScanTM Version History. Viewed November 7, Version 6. October 24 (2005), http://www.satscan.org/techdoc.html

  36. SaTScanTM web site. Viewed November 7 (2005), http://www.satscan.org

  37. Surveillance of Morbidity during Wildfires – Central Florida 1998. In: MMWR, vol. 48(40) (1999)

    Google Scholar 

  38. United States Geological Survey (USGS), Geographic Information Systems (GIS) Poster, http://www.erg/isgs/gpv/isb/pubs/gis_poster/

  39. Wildfires burn 70,000 acres in Everglades, April 19 (1999), http://www.cnn.com

  40. Wildfires Fact Sheet: Health Threat from Wildfire Smoke. Department of Health and Human Services. Center for Disease Control and Prevention (2003), www.bt/cdc/gov/firesafety

  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. 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. 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. 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. ZipCode Data. Great Data Frequently Asked Questions, http://www.greatdata.com/zipcodefaqs.php

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Griffiths, J.L., Berndt, D.J., Hevner, A.R. (2006). Naturally Occurring Incidents as Facsimiles for Biochemical Terrorist Attacks. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B., Wang, FY. (eds) Intelligence and Security Informatics. ISI 2006. Lecture Notes in Computer Science, vol 3975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760146_20

Download citation

  • DOI: https://doi.org/10.1007/11760146_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34478-0

  • Online ISBN: 978-3-540-34479-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics