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Machine Learning Used in Communicable Disease Control: A Scoping Review.

Sharon Birdi1, Atushi Patel1, Roxana Rabet1

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Summary
This summary is machine-generated.

Machine learning (ML) is increasingly used for communicable disease control, especially for COVID-19. Future research must address algorithmic bias in ML models to ensure reliable and equitable public health outcomes.

Keywords:
artificial intelligencecommunicable diseasesmachine learningpopulation healthpublic healthscoping review

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Area of Science:

  • Public Health
  • Infectious Disease Epidemiology
  • Machine Learning Applications

Background:

  • Communicable diseases pose significant global health risks, exemplified by the COVID-19 pandemic.
  • Rapid data analysis through machine learning (ML) is vital for effective outbreak detection and control.

Purpose of the Study:

  • To systematically review ML applications in communicable disease control for public health.
  • To investigate algorithmic biases in ML models and explore mitigation strategies.

Main Methods:

  • Comprehensive literature search across multiple databases (MEDLINE, Embase, Scopus, etc.) from 2000 to 2022.
  • Inclusion of peer-reviewed studies applying ML models to high-prevalence communicable diseases.

Main Results:

  • 209 studies met inclusion criteria, with a surge in ML applications since 2020, particularly for SARS-CoV-2.
  • ML use expanded to diseases like malaria, HIV, and tuberculosis.
  • Only 8.61% of studies addressed bias, with fewer implementing mitigation strategies.

Conclusions:

  • Machine learning shows growing utility in disease surveillance for communicable diseases.
  • Prioritizing bias mitigation in ML model design is crucial for enhancing reliability and equity in public health.