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

Michael F Rayo1, Daria Faulkner2, David Kline3

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

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|August 15, 2023
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Summary
This summary is machine-generated.

This study successfully used a Bayesian search algorithm for COVID-19 surveillance, maximizing case detection and reaching underserved communities. This innovative approach enhances infectious disease surveillance in urban settings.

Keywords:
COVID-19SARS-CoV-2active surveillancebandit algorithmscommunity healthinfectious diseasereinforcement learning

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Area of Science:

  • Epidemiology
  • Public Health
  • Biostatistics

Background:

  • The Flexible Adaptive Algorithmic Surveillance Testing (FAAST) program pioneered Bayesian adaptive approaches for active disease surveillance.
  • The urgent need to detect SARS-CoV-2 cases, especially with available COVID-19 treatments, drove this research.
  • Bayesian search algorithms, while used in other fields, were novel for active infectious disease surveillance.

Purpose of the Study:

  • To evaluate a Bayesian search algorithm for targeting SARS-CoV-2 transmission hotspots in Columbus, Ohio.
  • To maximize the detection of new SARS-CoV-2 cases over time across various community locations.
  • To assess the feasibility of adaptive algorithms in real-world public health surveillance.

Main Methods:

  • A Thompson sampling-based Bayesian search algorithm directed pop-up SARS-CoV-2 testing.
  • Pop-up testing sites were established in 13 diverse locations across Columbus, Ohio.
  • Incentives such as gift cards and rapid antigen tests encouraged participation in testing events.

Main Results:

  • The Bayesian algorithm effectively directed testing to locations with higher SARS-CoV-2 case yields.
  • The strategy unexpectedly maximized case identification among minority residents in underserved communities.
  • Testing efforts successfully overrepresented African American participants relative to local demographics.

Conclusions:

  • Pop-up testing utilizing a bandit algorithm is a feasible urban pandemic strategy.
  • This marks the first real-world application of these algorithms for disease surveillance.
  • The study validates the effectiveness of adaptive algorithms in detecting undiagnosed SARS-CoV-2 and other infections.