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Effectiveness of feedback control and the trade-off between death by COVID-19 and costs of countermeasures

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  • Akira Watanabe

    (Yokohama National University)

  • Hiroyuki Matsuda

    (Yokohama National University)

Abstract

We provided a framework of a mathematical epidemic modeling and a countermeasure against the novel coronavirus disease (COVID-19) under no vaccines and specific medicines. The fact that even asymptomatic cases are infectious plays an important role for disease transmission and control. Some patients recover without developing the disease; therefore, the actual number of infected persons is expected to be greater than the number of confirmed cases of infection. Our study distinguished between cases of confirmed infection and infected persons in public places to investigate the effect of isolation. An epidemic model was established by utilizing a modified extended Susceptible-Exposed-Infectious-Recovered model incorporating three types of infectious and isolated compartments, abbreviated as SEIIIHHHR. Assuming that the intensity of behavioral restrictions can be controlled and be divided into multiple levels, we proposed the feedback controller approach to implement behavioral restrictions based on the active number of hospitalized persons. Numerical simulations were conducted using different detection rates and symptomatic ratios of infected persons. We investigated the appropriate timing for changing the degree of behavioral restrictions and confirmed that early initiating behavioral restrictions is a reasonable measure to reduce the burden on the health care system. We also examined the trade-off between reducing the cumulative number of deaths by the COVID-19 and saving the cost to prevent the spread of the virus. We concluded that a bang-bang control of the behavioral restriction can reduce the socio-economic cost, while a control of the restrictions with multiple levels can reduce the cumulative number of deaths by infection.

Suggested Citation

  • Akira Watanabe & Hiroyuki Matsuda, 2023. "Effectiveness of feedback control and the trade-off between death by COVID-19 and costs of countermeasures," Health Care Management Science, Springer, vol. 26(1), pages 46-61, March.
    • Handle: RePEc:kap:hcarem:v:26:y:2023:i:1:d:10.1007_s10729-022-09617-0
    DOI: 10.1007/s10729-022-09617-0
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    References listed on IDEAS

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    1. Francesco Di Lauro & István Z Kiss & Joel C Miller, 2021. "Optimal timing of one-shot interventions for epidemic control," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-24, March.
    2. Hideo Tanaka & Atsushi Hirayama & Hitomi Nagai & Chika Shirai & Yuki Takahashi & Hiroto Shinomiya & Chie Taniguchi & Tsuyoshi Ogata, 2021. "Increased Transmissibility of the SARS-CoV-2 Alpha Variant in a Japanese Population," IJERPH, MDPI, vol. 18(15), pages 1-6, July.
    3. Neil M. Ferguson & Derek A. T. Cummings & Christophe Fraser & James C. Cajka & Philip C. Cooley & Donald S. Burke, 2006. "Strategies for mitigating an influenza pandemic," Nature, Nature, vol. 442(7101), pages 448-452, July.
    4. Richard M Wood & Christopher J McWilliams & Matthew J Thomas & Christopher P Bourdeaux & Christos Vasilakis, 2020. "COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care," Health Care Management Science, Springer, vol. 23(3), pages 315-324, September.
    5. Thunström, Linda & Newbold, Stephen C. & Finnoff, David & Ashworth, Madison & Shogren, Jason F., 2020. "The Benefits and Costs of Using Social Distancing to Flatten the Curve for COVID-19," Journal of Benefit-Cost Analysis, Cambridge University Press, vol. 11(2), pages 179-195, July.
    6. Cyril Atkinson-Clement & Eléonore Pigalle, 2021. "What can we learn from Covid-19 pandemic’s impact on human behaviour? The case of France’s lockdown," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-12, December.
    7. Christine S.M. Currie & John W. Fowler & Kathy Kotiadis & Thomas Monks & Bhakti Stephan Onggo & Duncan A. Robertson & Antuela A. Tako, 2020. "How simulation modelling can help reduce the impact of COVID-19," Journal of Simulation, Taylor & Francis Journals, vol. 14(2), pages 83-97, April.
    8. Neil M. Ferguson & Derek A.T. Cummings & Simon Cauchemez & Christophe Fraser & Steven Riley & Aronrag Meeyai & Sopon Iamsirithaworn & Donald S. Burke, 2005. "Strategies for containing an emerging influenza pandemic in Southeast Asia," Nature, Nature, vol. 437(7056), pages 209-214, September.
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