This paper is in the following e-collection/theme issue:

Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1543) Outbreak and Pandemic Preparedness and Management (1828) Infectious Diseases (non-STD/STI) (1727) Innovative Methods in Public Health and Surveillance (385) Surveillance Systems (514) JMIR Theme Issue: COVID-19 Special Issue (2555) Behavioural Surveillance for Public Health (294) Surveillance Reports (157)

Published on in Vol 9 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39754, first published .
Using Bandit Algorithms to Maximize SARS-CoV-2 Case-Finding: Evaluation and Feasibility Study

Using Bandit Algorithms to Maximize SARS-CoV-2 Case-Finding: Evaluation and Feasibility Study

Using Bandit Algorithms to Maximize SARS-CoV-2 Case-Finding: Evaluation and Feasibility Study

Authors of this article:

Michael F Rayo1 Author Orcid Image ;   Daria Faulkner2 Author Orcid Image ;   David Kline3 Author Orcid Image ;   Thomas Thornhill IV4 Author Orcid Image ;   Samuel Malloy5 Author Orcid Image ;   Dante Della Vella1 Author Orcid Image ;   Dane A Morey1 Author Orcid Image ;   Net Zhang5 Author Orcid Image ;   Gregg Gonsalves4 Author Orcid Image
Article Authors Cited by (2) Tweetations (4) Metrics

Journals

  1. Warren J, Prunas O, Paltiel A, Thornhill T, Gonsalves G. Integrating testing volume into bandit algorithms for infectious disease surveillance. Journal of the Royal Statistical Society Series A: Statistics in Society 2025;188(4):1029 View
  2. Zhang J, Linde L, Puma D, Millones A, Tintaya K, Jimenez J, Jenkins H, Becerra M, Keshavjee S, Lecca L, Cutkosky A, Brooks M. Adaptive bandit algorithms increase efficiency of mobile tuberculosis screening programs. Scientific Reports 2025 View