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Smart Testing with Vaccination: A Bandit Algorithm for Active Sampling for Managing COVID-19

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  • Yingfei Wang

    (Foster School of Business, University of Washington, Seattle, Washington 98195)

  • Inbal Yahav

    (Coller School of Management, Tel Aviv University, Tel Aviv 6997801, Israel)

  • Balaji Padmanabhan

    (Muma College of Business, University of South Florida, Tampa, Florida 33620)

Abstract

This paper presents methods to choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers. The smart-testing ideas presented here are motivated by active learning and multi-armed bandit techniques in machine learning. Our active sampling method works in conjunction with quarantine policies, can handle different objectives, and is dynamic and adaptive in the sense that it continually adapts to changes in real-time data. The bandit algorithm uses contact tracing, location-based sampling and random sampling in order to select specific individuals to test. Using a data-driven agent-based model simulating New York City we show that the algorithm samples individuals to test in a manner that rapidly traces infected individuals. Experiments also suggest that smart-testing can significantly reduce the death rates as compared with current methods, with or without vaccination. While smart testing strategies can help mitigate disease spread, there could be unintended consequences with fairness or bias when deployed in real-world settings. To this end we show how procedural fairness can be incorporated into our method and present results that show that this can be done without hurting the effectiveness of the mitigation that can be achieved.

Suggested Citation

  • Yingfei Wang & Inbal Yahav & Balaji Padmanabhan, 2024. "Smart Testing with Vaccination: A Bandit Algorithm for Active Sampling for Managing COVID-19," Information Systems Research, INFORMS, vol. 35(1), pages 120-144, March.
    • Handle: RePEc:inm:orisre:v:35:y:2024:i:1:p:120-144
    DOI: 10.1287/isre.2023.1215
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    References listed on IDEAS

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    1. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions," LIDAM Reprints CORE 341, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions - 1," LIDAM Reprints CORE 334, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Sheryl L. Chang & Nathan Harding & Cameron Zachreson & Oliver M. Cliff & Mikhail Prokopenko, 2020. "Modelling transmission and control of the COVID-19 pandemic in Australia," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    4. Andrew Atkeson, 2020. "What Will be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios," Staff Report 595, Federal Reserve Bank of Minneapolis.
    5. Elisa F. Long & Eike Nohdurft & Stefan Spinler, 2018. "Spatial Resource Allocation for Emerging Epidemics: A Comparison of Greedy, Myopic, and Dynamic Policies," Manufacturing & Service Operations Management, INFORMS, vol. 20(2), pages 181-198, May.
    6. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    7. Maytal Saar-Tsechansky & Foster Provost, 2007. "Decision-Centric Active Learning of Binary-Outcome Models," Information Systems Research, INFORMS, vol. 18(1), pages 4-22, March.
    8. Wang Chi Cheung & David Simchi-Levi & Ruihao Zhu, 2022. "Hedging the Drift: Learning to Optimize Under Nonstationarity," Management Science, INFORMS, vol. 68(3), pages 1696-1713, March.
    9. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    10. Zhiqiang Zheng & Balaji Padmanabhan, 2006. "Selectively Acquiring Customer Information: A New Data Acquisition Problem and an Active Learning-Based Solution," Management Science, INFORMS, vol. 52(5), pages 697-712, May.
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