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Related Experiment Video

Updated: Dec 22, 2025

Developing a Salivary Antibody Multiplex Immunoassay to Measure Human Exposure to Environmental Pathogens
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Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development.

Prosper Kandabongee Yeng1,2, Ashenafi Zebene Woldaregay1, Terje Solvoll3

  • 1Department of Computer Science, University of Tromsø, The Arctic University of Norway, Gjøvik, Norway.

JMIR Public Health and Surveillance
|May 2, 2020
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Summary

This review systematically examined disease surveillance clustering algorithms, identifying practical implementations for temporal, spatial, and spatiotemporal detection. The findings led to a framework for enhanced disease outbreak detection systems.

Keywords:
aberration detectionsentinel surveillancespace-time clustering

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Area of Science:

  • Public Health
  • Computer Science
  • Data Science

Background:

  • Timely detection of disease outbreaks is crucial for global health security.
  • Advancements in technology facilitate access to health data for syndromic surveillance.
  • Clustering algorithms are central to disease surveillance for identifying outbreaks based on data similarities.

Purpose of the Study:

  • To systematically review implemented disease surveillance clustering algorithms (temporal, spatial, spatiotemporal).
  • To evaluate the usage and performance of these clustering mechanisms.
  • To develop an efficient cluster detection mechanism framework.

Main Methods:

  • Conducted a systematic literature review across multiple databases (Google Scholar, PubMed, Scopus, etc.).
  • Included studies published in peer-reviewed journals focusing on practically implemented syndromic surveillance systems and infectious diseases.
  • Developed a framework for cluster detection mechanisms based on the review findings.

Main Results:

  • Identified and reviewed 27 relevant articles after an extensive search and filtering process.
  • Confirmed that various clustering and aberration detection algorithms have been empirically implemented and tested with real data.
  • Developed a comprehensive framework encompassing data processing, clustering, visualization, and alerting.

Conclusions:

  • The review identified numerous practically implemented and tested algorithms for disease surveillance.
  • These findings can guide the development of effective cluster detection mechanisms for syndromic surveillance.
  • The proposed framework supports diverse spatiotemporal surveillance needs for improved outbreak detection.