Let the DOCTOR Decide Whom to Test: Adaptive Testing Strategies to Tackle the COVID-19 Pandemic.

A robust testing program is necessary for containing the spread of COVID-19 infections before a vaccine becomes available. However, due to an acute shortage of testing kits (especially in low-resource developing countries), designing an optimal testing program/strategy is a challenging problem to solve. Prior literature on testing strategies suffers from two major limitations: (i) it does not account for the trade-off between testing of symptomatic and asymptomatic individuals, and (ii) it primarily focuses on static testing strategies, which leads to significant shortcomings in the testing program's effectiveness. In this paper, we address these limitations by making five novel contributions. (i) We formally define the optimal testing problem and propose the DOCTOR POMDP model to tackle it. (ii) We solve the DOCTOR POMDP using a scalable Monte Carlo tree search based algorithm. (iii) We provide a rigorous experimental analysis of DOCTOR's testing strategies against static baselines - our results show that when applied to the city of Santiago in Panama, DOCTOR's strategies result in approximately 40% fewer COVID-19 infections (over one month) as compared to state-of-the-art static baselines. (iv) In addition, we analyze DOCTOR's testing policy to derive insights about the reasons behind the optimality of DOCTOR's testing policy. (v) Finally, we characterize conditions (of the real world) under which DOCTOR's optimization would be of most benefit to government policy makers, and thus requires significant attention from researchers in this area. Our work complements the growing body of research on COVID-19, and serves as a proof-of-concept that illustrates the benefit of having an AI-driven adaptive testing strategy for COVID-19.

Citation for published work: Yu Liang and Amulya Yadav. 2021. Let the DOCTOR Decide Whom to Test: Adaptive Testing Strategies to Tackle the COVID-19 Pandemic. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS '21). International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 790–798.

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Work Title Let the DOCTOR Decide Whom to Test: Adaptive Testing Strategies to Tackle the COVID-19 Pandemic.
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Open Access
Creators
  1. Yu Liang
  2. Amulya Yadav
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems
Publication Date May 2021
Related URLs
  1. https://www.medrxiv.org/content/10.1101/2020.12.18.20248498v1
Deposited April 22, 2022

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  • Created

    April 22, 2022 15:50 by Scholarly Communications and Copyright

  • Added AAMAS21-1.pdf

    April 22, 2022 15:50 by Scholarly Communications and Copyright

  • Added Creator Yu Liang

    April 22, 2022 15:50 by Scholarly Communications and Copyright

  • Added Creator Amulya Yadav

    April 22, 2022 15:50 by Scholarly Communications and Copyright

  • Published

    April 22, 2022 15:50 by Scholarly Communications and Copyright

  • Updated Description Show Changes

    April 27, 2022 14:43 by sre53

    Description
    • <jats:title>ABSTRACT</jats:title><jats:p>A robust testing program is necessary for containing the spread of COVID-19 infections before a vaccine becomes available. However, due to an acute shortage of testing kits (especially in low-resource developing countries), designing an optimal testing program/strategy is a challenging problem to solve. Prior literature on testing strategies suffers from two major limitations: (i) it does not account for the trade-off between testing of symptomatic and asymptomatic individuals, and (ii) it primarily focuses on static testing strategies, which leads to significant shortcomings in the testing program’s effectiveness. In this paper, we address these limitations by making five novel contributions. (i) We formally define the optimal testing problem and propose the DOCTOR POMDP model to tackle it. (ii) We solve the DOCTOR POMDP using a scalable Monte Carlo tree search based algorithm. (iii) We provide a rigorous experimental analysis of DOCTOR’s testing strategies against static baselines - our results show that when applied to the city of Santiago in Panama, DOCTOR’s strategies result in ∼40% fewer COVID-19 infections (over one month) as compared to state-of-the-art static baselines. (iv) In addition, we analyze DOCTOR’s testing policy to derive insights about the reasons behind the optimality of DOCTOR’s testing policy. (v) Finally, we characterize conditions (of the real world) under which DOCTOR’s optimization would be of most benefit to government policy makers, and thus requires significant attention from researchers in this area. Our work complements the growing body of research on COVID-19, and serves as a proof-of-concept that illustrates the benefit of having an AI-driven adaptive testing strategy for COVID-19.</jats:p>
    • A robust testing program is necessary for containing the spread of COVID-19 infections before a vaccine becomes available. However, due to an acute shortage of testing kits (especially in low-resource developing countries), designing an optimal testing program/strategy is a challenging problem to solve. Prior literature on testing strategies suffers from two major limitations: (i) it does not account for the trade-off between testing of symptomatic and asymptomatic individuals, and (ii) it primarily focuses on static testing strategies, which leads to significant shortcomings in the testing program’s effectiveness. In this paper, we address these limitations by making five novel contributions. (i) We formally define the optimal testing problem and propose the DOCTOR POMDP model to tackle it. (ii) We solve the DOCTOR POMDP using a scalable Monte Carlo tree search based algorithm. (iii) We provide a rigorous experimental analysis of DOCTOR’s testing strategies against static baselines - our results show that when applied to the city of Santiago in Panama, DOCTOR’s strategies result in ∼40% fewer COVID-19 infections (over one month) as compared to state-of-the-art static baselines. (iv) In addition, we analyze DOCTOR’s testing policy to derive insights about the reasons behind the optimality of DOCTOR’s testing policy. (v) Finally, we characterize conditions (of the real world) under which DOCTOR’s optimization would be of most benefit to government policy makers, and thus requires significant attention from researchers in this area. Our work complements the growing body of research on COVID-19, and serves as a proof-of-concept that illustrates the benefit of having an AI-driven adaptive testing strategy for COVID-19.
  • Updated Publisher, Publisher Identifier (DOI), Description, and 3 more Show Changes

    May 09, 2022 14:57 by aee32

    Publisher
    • Cold Spring Harbor Laboratory
    • AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems
    Publisher Identifier (DOI)
    • 10.1101/2020.12.18.20248498
    Description
    • A robust testing program is necessary for containing the spread of COVID-19 infections before a vaccine becomes available. However, due to an acute shortage of testing kits (especially in low-resource developing countries), designing an optimal testing program/strategy is a challenging problem to solve. Prior literature on testing strategies suffers from two major limitations: (i) it does not account for the trade-off between testing of symptomatic and asymptomatic individuals, and (ii) it primarily focuses on static testing strategies, which leads to significant shortcomings in the testing program’s effectiveness. In this paper, we address these limitations by making five novel contributions. (i) We formally define the optimal testing problem and propose the DOCTOR POMDP model to tackle it. (ii) We solve the DOCTOR POMDP using a scalable Monte Carlo tree search based algorithm. (iii) We provide a rigorous experimental analysis of DOCTOR’s testing strategies against static baselines - our results show that when applied to the city of Santiago in Panama, DOCTOR’s strategies result in ∼40% fewer COVID-19 infections (over one month) as compared to state-of-the-art static baselines. (iv) In addition, we analyze DOCTOR’s testing policy to derive insights about the reasons behind the optimality of DOCTOR’s testing policy. (v) Finally, we characterize conditions (of the real world) under which DOCTOR’s optimization would be of most benefit to government policy makers, and thus requires significant attention from researchers in this area. Our work complements the growing body of research on COVID-19, and serves as a proof-of-concept that illustrates the benefit of having an AI-driven adaptive testing strategy for COVID-19.
    • A robust testing program is necessary for containing the spread of COVID-19 infections before a vaccine becomes available. However, due to an acute shortage of testing kits (especially in low-resource developing countries), designing an optimal testing program/strategy is a challenging problem to solve. Prior literature on testing strategies suffers from two major limitations: (i) it does not account for the trade-off between testing of symptomatic and asymptomatic individuals, and (ii) it primarily focuses on static testing strategies, which leads to significant shortcomings in the testing program's effectiveness. In this paper, we address these limitations by making five novel contributions. (i) We formally define the optimal testing problem and propose the DOCTOR POMDP model to tackle it. (ii) We solve the DOCTOR POMDP using a scalable Monte Carlo tree search based algorithm. (iii) We provide a rigorous experimental analysis of DOCTOR's testing strategies against static baselines - our results show that when applied to the city of Santiago in Panama, DOCTOR's strategies result in approximately 40% fewer COVID-19 infections (over one month) as compared to state-of-the-art static baselines. (iv) In addition, we analyze DOCTOR's testing policy to derive insights about the reasons behind the optimality of DOCTOR's testing policy. (v) Finally, we characterize conditions (of the real world) under which DOCTOR's optimization would be of most benefit to government policy makers, and thus requires significant attention from researchers in this area. Our work complements the growing body of research on COVID-19, and serves as a proof-of-concept that illustrates the benefit of having an AI-driven adaptive testing strategy for COVID-19.
    Related URLs
    • https://www.medrxiv.org/content/10.1101/2020.12.18.20248498v1
    Publication Date
    • 2020-12-22
    • 2021-05
    Publisher's Statement
    • Citation for published work: Yu Liang and Amulya Yadav. 2021. Let the DOCTOR Decide Whom to Test: Adaptive Testing Strategies to Tackle the COVID-19 Pandemic. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS '21). International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 790–798.
  • Updated

    April 04, 2024 10:21 by [unknown user]