Maximizing the Efficiency of Active Case Finding for SARS-CoV-2 Using Bandit Algorithms

Gregg S. Gonsalves, J. Tyler Copple, A. David Paltiel, Eli P. Fenichel, Jude Bayham, Mark Abraham, David Kline, Sam Malloy, Michael F. Rayo, Net Zhang, Daria Faulkner, Dane A. Morey, Frank Wu, Thomas Thornhill, Suzan Iloglu and Joshua L. Warren
Additional contact information
Gregg S. Gonsalves: Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
J. Tyler Copple: Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
A. David Paltiel: Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
Eli P. Fenichel: Yale School of the Environment, New Haven, CT, USA
Mark Abraham: DataHaven, New Haven, CT, USA
David Kline: Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
Sam Malloy: Battelle Center for Science, Engineering, and Public Policy, John Glenn College of Public Affairs, The Ohio State University, Columbus, OH, USA
Michael F. Rayo: Integrated Systems Engineering, The Ohio State University, Columbus, OH, USA
Net Zhang: Battelle Center for Science, Engineering, and Public Policy, John Glenn College of Public Affairs, The Ohio State University, Columbus, OH, USA
Daria Faulkner: College of Public Health, The Ohio State University, Columbus, OH, USA
Dane A. Morey: Integrated Systems Engineering, The Ohio State University, Columbus, OH, USA
Frank Wu: Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
Thomas Thornhill: Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
Suzan Iloglu: Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
Joshua L. Warren: Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA

Medical Decision Making, 2021, vol. 41, issue 8, 970-977

Abstract: Even as vaccination for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) expands in the United States, cases will linger among unvaccinated individuals for at least the next year, allowing the spread of the coronavirus to continue in communities across the country. Detecting these infections, particularly asymptomatic ones, is critical to stemming further transmission of the virus in the months ahead. This will require active surveillance efforts in which these undetected cases are proactively sought out rather than waiting for individuals to present to testing sites for diagnosis. However, finding these pockets of asymptomatic cases (i.e., hotspots) is akin to searching for needles in a haystack as choosing where and when to test within communities is hampered by a lack of epidemiological information to guide decision makers’ allocation of these resources. Making sequential decisions with partial information is a classic problem in decision science, the explore v. exploit dilemma. Using methods—bandit algorithms—similar to those used to search for other kinds of lost or hidden objects, from downed aircraft or underground oil deposits, we can address the explore v. exploit tradeoff facing active surveillance efforts and optimize the deployment of mobile testing resources to maximize the yield of new SARS-CoV-2 diagnoses. These bandit algorithms can be implemented easily as a guide to active case finding for SARS-CoV-2. A simple Thompson sampling algorithm and an extension of it to integrate spatial correlation in the data are now embedded in a fully functional prototype of a web app to allow policymakers to use either of these algorithms to target SARS-CoV-2 testing. In this instance, potential testing locations were identified by using mobility data from UberMedia to target high-frequency venues in Columbus, Ohio, as part of a planned feasibility study of the algorithms in the field. However, it is easily adaptable to other jurisdictions, requiring only a set of candidate test locations with point-to-point distances between all locations, whether or not mobility data are integrated into decision making in choosing places to test.

Keywords: bandit algorithms; reinforcement learning; SARS-CoV-2; surveillance; testing (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:41:y:2021:i:8:p:970-977

DOI: 10.1177/0272989X211021603

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