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Benchmark data and power calculations for evaluating disease outbreak detection methods.

Martin Kulldorff1, Z Zhang, J Hartman

  • 1Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, 133 Brookline Avenue, 6th Floor, Boston, MA 02215, USA. martin_kulldorff@hms.harvard.edu

MMWR Supplements
|February 18, 2005
PubMed
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Space-time scan statistics effectively detect localized disease outbreaks, outperforming purely temporal methods for early detection. Benchmark data sets were created to evaluate these surveillance systems.

Area of Science:

  • Public Health
  • Epidemiology
  • Biostatistics

Background:

  • Early detection of disease outbreaks is crucial for public health officials to implement timely control measures.
  • Computer-based syndromic surveillance systems enhance traditional reporting for faster outbreak detection.
  • Space-time surveillance methods offer a supplement to temporal methods for identifying localized outbreaks.

Purpose of the Study:

  • To create benchmark data sets for evaluating the statistical power of space-time early detection methods.
  • To assess the power of prospective temporal and space-time scan statistics using these benchmark data sets.

Main Methods:

  • Simulated New York City data sets were generated, incorporating outbreaks of varying size and location.
  • Data sets without outbreak effects were also created for baseline comparison.

Related Experiment Videos

  • Scan statistics were applied to the simulated data, and their power performance was analyzed.

    • Main Results:

    • The prospective space-time scan statistic demonstrated strong performance across various outbreak models.
    • Purely temporal scan statistics showed higher power for citywide outbreaks.
    • Geographically localized outbreaks were detected more effectively by space-time methods.

    Conclusions:

    • The developed benchmark data sets are valuable for statistical power evaluations and comparisons.
    • Space-time methods are essential for detecting localized anomalies that purely temporal methods may miss.
    • Early detection of localized outbreaks necessitates the use of space-time surveillance.