SYSTEMATIC REVIEW

Public Health Rev.

Machine Learning Used in Communicable Disease Control: A Scoping Review

  • 1. Upstream Lab, MAP/Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Canada, Ontario

  • 2. Michael G. DeGroote School of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada, Ontario, L8S4L8

  • 3. Library Services, Unity Health Toronto, Toronto, Canada

  • 4. Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Canada, Ontario, N6A 5C1

  • 5. Department of Health Sciences, Faculty of Sciences, University of York, Heslington, United Kingdom, Yorkshire and the Humber, YO10 5DD

  • 6. WHO Collaborating Centre for Knowledge Translation and Health Technology Assessment in Health Equity, Ottawa, Canada

  • 7. Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada, Quebec, H3A 1A2

  • 8. Department of Electrical Engineering and Computer Science, School of Engineering, Massachusetts Institute of Technology, Cambridge, United States, Massachusetts, 02139

  • 9. Institute for Medical Engineering and Science, School of Engineering, Massachusetts Institute of Technology, Cambridge, United States, Maryland

  • 10. Joint Centre for Bioethics, University of Toronto, Toronto, Canada

  • 11. Department of Anesthesia and Perioperative Medicine, Schulich School of Medicine and Dentistry, Western University, London, Canada, Ontario, N6A 5A5

  • 12. Schulich Interfaculty Program in Public Health, Schulich School of Medicine and Dentistry, Western University, London, Canada, Ontario, N6G 2M1

  • 13. Undergraduate Medical Education, Faculty of Medicine, University of Toronto, Toronto, Canada, Ontario

  • 14. Department of Bioethics, The Hospital for Sick Children, Toronto, Canada

  • 15. Genetics & Genome Biology, SickKids Research Institute, Toronto, Canada

  • 16. Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada, Ontario, M5T 3M7

  • 17. Wellesley Institute, Toronto, Canada

  • 18. The Centre for Addiction and Mental Health, Toronto, Canada, Ontario

  • 19. Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, Canada, Ontario, M5S 3H2

  • 20. MAP Centre for Urban Health Solutions, Toronto, Canada, Ontario, M5B 1W8

  • 21. Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada, Ontario, M5S 1A8

  • 22. Institute of Health Policy Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada, Ontario, ON M5T 3M6

  • 23. Department of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada, Ontario, M5T 3M7

  • 24. Institute for Clinical Evaluative Sciences, Toronto, Canada

  • 25. Department of Computer Science, Toronto Metropolitan University, Toronto, Canada

  • 26. Department of Sociology, Faculty of Arts and Science, University of Toronto, Toronto, Canada, Ontario, M5S 2J4

  • 27. Institute for Better Health, Trillium Health Partners, Toronto, Canada

  • 28. Department of Physical Therapy, Faculty of Medicine, University of Toronto, Toronto, Canada, Ontario, M5G 1V7

  • 29. Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Canada, Ontario, M5G 1V7

  • 30. Department of Family and Community Medicine, St. Michael's Hospital, Toronto, Canada, Ontario

  • 31. Temerty Faculty of Medicine, University of Toronto, Toronto, Canada, Ontario, M5S 1A8

The final, formatted version of the article will be published soon.

Abstract

Introduction: Communicable diseases continue to threaten global health, with COVID-19 as a recent example. Rapid data analysis using machine learning (ML) is crucial for detecting and controlling outbreaks. We aimed to identify how ML approaches have been applied to achieve public health objectives in communicable disease control and to explore algorithmic biases in model design, training, and implementation, and strategies to mitigate these biases. Methods: We searched MEDLINE, Embase, Cochrane Central, Scopus, ACM DL, INSPEC, and Web of Science to identify peer-reviewed studies from January 1, 2000, to July 15, 2022. Included studies applied ML models in population and public health to address ten communicable diseases with high prevalence.. Results: 28,378 citations were retrieved, and 209 met our inclusion criteria. ML for communicable diseases has risen since 2020, particularly for SARS-CoV-2 (n=177), followed by malaria, HIV, and tuberculosis. 18 studies (8.61%) considered bias, and only eleven implemented mitigation strategies. Conclusion: A growing number of studies used ML for disease surveillance. Addressing biases in model design should be prioritized in future research to improve reliability and equity in public health outcomes.

Summary

Keywords

Public Health, communicable diseases, Machine learning, Artificial intelligence, Population Health

Received

21 October 2024

Accepted

26 January 2026

Copyright

© 2026 Birdi, Patel, Rabet, Singh, Durant, Vosoughi, Kapra, Shergill, Mesfin, Ziegler, Ali, Buckeridge, Ghassemi, Gibson, John-Baptiste, Macklin, Mccradden, Mckenzie, Mishra, Naraei, Owusu-Bempah, Rosella, Shaw, Upshur and Pinto. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Andrew D. Pinto, andrew.pinto@utoronto.ca

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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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