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

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.

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

“…The algorithm used to direct pop-up SARS-CoV-2 testing for this project has been described in detail elsewhere [8][9][10]. The algorithm is based on Thompson sampling, which uses a Bayesian updating process involving iteratively sampling from prior probability distributions of all potential testing sites-the set of all locations at which testing is being considered-to home in on those with the highest probability over the long run in finding new cases of SARS-CoV-2 [16,17].…”

“…How to optimize resource allocation over time is a well-studied problem in sequential decision-making and reinforcement learning. The introduction of a spatial component to these kinds of dilemmas has been applied in a variety of settings, from military search and rescue to oil exploration [8]. We have previously described the use of one set of tools, bandit algorithms, to address these kinds of problems for detection of HIV and SARS-CoV-2 in the community [8][9][10].…”

“…The introduction of a spatial component to these kinds of dilemmas has been applied in a variety of settings, from military search and rescue to oil exploration [8]. We have previously described the use of one set of tools, bandit algorithms, to address these kinds of problems for detection of HIV and SARS-CoV-2 in the community [8][9][10]. Up until now, these methods have only been evaluated in computer simulations.…”

The Final Inch: What Pop-Up COVID Testing Tells Us about Community Engagement

“…To determine which sites would be visited on which days, we developed a Bayesian bandit algorithm, which are well-studied in other search-and-rescue and needle-in-haystack scenarios, including aircraft recovery, oil exploration, and slot machine (i.e., one-armed bandits) winnings optimization. Details of the algorithm and our site selection process are detailed elsewhere (Gonsalves et. al, 2021).…”