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

Abstract

BACKGROUND The Flexible Adaptive Algorithmic Surveillance Testing (FAAST) program represents an innovative approach for detecting cases of infectious disease, deployed here to diagnose SARS-CoV-2. OBJECTIVE This study’s objective was to evaluate a Bayesian search algorithm to target hotspots of viral transmission in the community with the objective of detecting the most cases over time across multiple locations in Columbus, Ohio from August to October 2021. METHODS The algorithm used to direct pop-up SARS-CoV-2 testing for this project is based on Thompson sampling in which the aim is to maximize the expected value of success in finding new cases of SARS-CoV-2 based on sampling from prior probability distributions for each testing site. An academic-governmental partnership between Yale University, The Ohio State University (OSU), Wake Forest University, the Ohio Department of Health (ODH), the Ohio National Guard (ONG) and the Columbus Metropolitan Libraries (CML) conducted a study of bandit algorithms to maximize the detection of new cases in SARS-CoV-2 in this Ohio city in 2021. The initiative established pop-up COVID-19 testing sites at 13 Columbus locations including library branches, recreational and community centers, movie theaters, homeless shelters, family services centers, and community events. Our team conducted between 0 and 56 tests at the 16 testing sessions, with an overall average of 25.3 tests conducted per session and a moving average that increased over time. Small incentives—including gift cards and take-home rapid antigen tests were offered to those who approached the pop-up sites to encourage their participation. RESULTS Over time, as expected, the Bayesian search algorithm directed testing efforts to locations with higher yields of new diagnoses. Surprisingly, the use of the algorithm also maximized the identification of cases among minority residents of under-served communities, particularly African Americans, with the pool of participants over-representing these people relative to the demographic profile of the local ZIP code in which testing sites were located. CONCLUSIONS This study demonstrated that a pop-up testing strategy using a bandit algorithm can be feasibly deployed in an urban setting during a pandemic. It is the first real-world use of these kinds of algorithms for disease surveillance and represents a key step in evaluating the effectiveness of their use in maximizing the detection of undiagnosed cases of SARS-CoV-2 and other infections such as HIV.