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Bio-ALIRT biosurveillance detection algorithm evaluation.

David Siegrist1, J Pavlin

  • 1Potomac Institute for Policy Studies, 901 N. Stuart Street, Suite 200, Arlington, Virginia 22203, USA. dsiegrist@potomacinstitute.org.

MMWR Supplements
|February 18, 2005
PubMed
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Automated biosurveillance algorithms can effectively detect natural disease outbreaks, mirroring potential bioterrorism events. These systems identify outbreaks rapidly with acceptable false-alert rates, enhancing public health surveillance.

Area of Science:

  • Public Health Surveillance
  • Epidemiology
  • Biosecurity

Background:

  • Biosurveillance systems require reliable data and effective detection algorithms for early outbreak identification.
  • Assessing algorithm performance is crucial for timely disease detection and response.

Purpose of the Study:

  • To evaluate automated detection algorithms for identifying natural disease outbreaks as surrogates for bioterrorism.
  • To determine if algorithms can detect outbreaks quickly with acceptable false-alert rates.

Main Methods:

  • Utilized 23 months of de-identified International Classification of Diseases, Ninth Revision (ICD-9) data from five metropolitan areas.
  • Trained algorithms on labeled natural outbreaks and tested on unseen outbreak data.
  • Assessed algorithm performance based on outbreak detection probability and false-alert rates.

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Main Results:

  • The best algorithms detected all simulated outbreaks at false-alert rates of one per 2-6 weeks.
  • Detection often occurred on the same day human investigators identified the outbreak onset.

Conclusions:

  • Biosurveillance algorithms can rapidly detect seasonal respiratory and gastrointestinal illness outbreaks.
  • Further research is needed on electronic data sources and simulations for enhanced predictive detection of bioterrorism events.