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Related Experiment Videos

Biosurveillance applying scan statistics with multiple, disparate data sources.

Howard S Burkom1

  • 1National Security Technology Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, USA. howard.burkom@jhuapl.edu

Journal of Urban Health : Bulletin of the New York Academy of Medicine
|June 7, 2003
PubMed
Summary
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Researchers enhanced early outbreak detection using scan statistics with diverse data, including medical records and over-the-counter sales, for the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). This improves public health surveillance and response capabilities.

Area of Science:

  • Epidemiology
  • Public Health Surveillance
  • Biostatistics

Background:

  • The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) is a pilot system within the Department of Defense Global Emerging Infections System (DoD-GEIS).
  • Early outbreak detection systems often face challenges with time-varying data streams influenced by factors like consumer behavior.

Purpose of the Study:

  • To apply scan statistics for early outbreak detection using both traditional and nontraditional data sources.
  • To address challenges posed by time-varying data in outbreak detection systems.
  • To integrate enhanced scan statistic capabilities into a comprehensive surveillance system.

Main Methods:

  • Utilized Kulldorff scan statistic implemented in SaTScan software.

Related Experiment Videos

  • Incorporated diverse data sources: medical diagnosis codes (ICD-9), over-the-counter remedy sales, and school absenteeism records.
  • Developed methods using modeling and historical data to establish background spatial distributions and condition data to prevent excessive clustering.

    • Main Results:

    • Successfully applied scan statistics to combine various data sources for retrospective outbreak studies and real-time surveillance.
    • Developed a Microsoft Access-based system for flexible data integration, allowing source inclusion/exclusion, adjustable time windows, data censoring, and background computation adjustments.
    • Demonstrated the ability to perform retrospective and simulated studies for enhanced public health preparedness.

    Conclusions:

    • The methodology effectively integrates diverse data streams for improved early outbreak detection and public health surveillance.
    • The developed system offers flexibility and adaptability for monitoring various public health events.
    • This approach enhances the capability to respond to emerging infectious diseases and public health threats.