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

Michael F Rayo   1 , PhD ;   Daria Faulkner   2 , MPH ;   David Kline   3 , PhD ;   Thomas Thornhill IV   4 , MPH ;   Samuel Malloy   5 , MA ;   Dante Della Vella   1 , MSc ;   Dane A Morey   1 , PhD ;   Net Zhang   5 , BSc ;   Gregg Gonsalves   4 , MPhil, PhD

1 Department of Integrated Systems Engineering, College of Engineering, The Ohio State University, Columbus, OH, United States

2 College of Public Health, The Ohio State University, Columbus, OH, United States

3 Department of Biostatistics and Data Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, United States

4 Public Health Modeling Unit, Department of the Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, United States

5 Battelle Center for Science, Engineering, and Public Policy, John Glenn College of Public Affairs, The Ohio State University, Columbus, OH, United States

Corresponding Author:

  • Gregg Gonsalves, MPhil, PhD
  • Public Health Modeling Unit
  • Department of the Epidemiology of Microbial Diseases
  • Yale School of Public Health
  • 350 George Street, 3rd Floor
  • New Haven, CT, 06511
  • United States
  • Phone: 1 2036069149
  • Email: gregg.gonsalves@yale.edu