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Related Experiment Videos

Using laboratory-based surveillance data for prevention: an algorithm for detecting Salmonella outbreaks

L C Hutwagner1, E K Maloney, N H Bean

  • 1Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

Emerging Infectious Diseases
|July 1, 1997
PubMed
Summary
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This study introduces a novel CUSUM-based algorithm for detecting disease outbreaks. The method effectively identifies clusters of laboratory isolates, aiding in public health surveillance and response.

Area of Science:

  • Epidemiology
  • Public Health Surveillance
  • Statistical Process Control

Background:

  • Detecting disease outbreaks is crucial for public health.
  • Traditional surveillance methods may have limitations in timely outbreak detection.
  • Quality control methods from manufacturing offer potential for epidemiological surveillance.

Purpose of the Study:

  • To adapt the cumulative sums (CUSUM) quality control method for detecting unusual clusters of disease-causing organisms.
  • To develop and evaluate a computer algorithm based on CUSUM for identifying laboratory-reported disease outbreaks.
  • To estimate the sensitivity, specificity, and false-positive rate of the CUSUM-based outbreak detection method.

Main Methods:

  • Applied cumulative sums (CUSUM) methodology, a manufacturing quality control technique.

Related Experiment Videos

  • Developed a computer algorithm with minimal adjustments to CUSUM to sum differences between observed and expected isolate frequencies.
  • Utilized the algorithm to detect Salmonella Enteritidis outbreaks reported in 1993.
  • Compared algorithm-detected outbreaks with known reported outbreaks to assess performance metrics.

    • Main Results:

    • The CUSUM-based algorithm successfully identified clusters of disease-causing organisms.
    • Sensitivity varied by state, ranging from 0% to 100%.
    • Specificity ranged from 64% to 100%, with a false-positive rate between 0 and 1.

    Conclusions:

    • The adapted CUSUM method provides a viable approach for detecting laboratory-reported disease outbreaks.
    • The algorithm demonstrates potential for enhancing public health surveillance systems.
    • Further validation across different pathogens and geographical regions is warranted.