AUTHOR=Birdi Sharon , Patel Atushi , Rabet Roxana , Singh Navreet , Durant Steve , Vosoughi Tina , Kapra Faris , Shergill Mahek , Mesfin Elnathan , Ziegler Carolyn , Ali Shehzad , Buckeridge David , Ghassemi Marzyeh , Gibson Jennifer , John-Baptiste Ava , Macklin Jillian , Mccradden Melissa , Mckenzie Kwame , Mishra Sharmistha , Naraei Parisa , Owusu-Bempah Akwasi , Rosella Laura , Shaw James , Upshur Ross , Pinto Andrew D. 
  
TITLE=Machine Learning Used in Communicable Disease Control: A Scoping Review
  
JOURNAL=Public Health Reviews
  
VOLUME=Volume 47 - 2026
  
YEAR=2026
  
URL=https://www.ssph-journal.org/journals/public-health-reviews/articles/10.3389/phrs.2026.1608074
  
DOI=10.3389/phrs.2026.1608074
  
ISSN=2107-6952
  
ABSTRACT=ObjectivesCommunicable 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.MethodsWe searched MEDLINE, Embase, Cochrane Central, Scopus, ACM DL, INSPEC, and Web of Science to identify peer-reviewed studies from 1 January 2000, to 15 July 2022. Included studies applied ML models in population and public health to address ten communicable diseases with high prevalence.Results28,378 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. Eighteen studies (8.61%) considered bias, and only eleven implemented mitigation strategies.ConclusionA 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.