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Easing or tightening control strategies: determination of COVID-19 parameters for an agent-based model

Author

Listed:
  • Ali Najmi

    (The University of New South Wales)

  • Sahar Nazari

    (Macquarie University
    University of New South Wales)

  • Farshid Safarighouzhdi

    (The University of New South Wales)

  • Eric J. Miller

    (University of Toronto)

  • Raina MacIntyre

    (Arizona State University College of Health Solutions
    Kirby Institute, The University of New South Wales)

  • Taha H. Rashidi

    (The University of New South Wales)

Abstract

Some agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate strategies aimed to control the outbreak of the infectious disease. Nonetheless, COVID-19 parameter estimation methods are limited to observational epidemiologic studies which are essentially aggregated models. We propose a mathematical structure to determine parameters of agent-based models accounting for the mutual effects of parameters. We then use the agent-based model to assess the extent to which different control strategies can intervene the transmission of COVID-19. Easing social distancing restrictions, opening businesses, speed of enforcing control strategies, quarantining family members of isolated cases on the disease progression and encouraging the use of facemask are the strategies assessed in this study. We estimate the social distancing compliance level in Sydney greater metropolitan area and then elaborate the consequences of moderating the compliance level in the disease suppression. We also show that social distancing and facemask usage are complementary and discuss their interactive effects in detail.

Suggested Citation

  • Ali Najmi & Sahar Nazari & Farshid Safarighouzhdi & Eric J. Miller & Raina MacIntyre & Taha H. Rashidi, 2022. "Easing or tightening control strategies: determination of COVID-19 parameters for an agent-based model," Transportation, Springer, vol. 49(5), pages 1265-1293, October.
    • Handle: RePEc:kap:transp:v:49:y:2022:i:5:d:10.1007_s11116-021-10210-7
    DOI: 10.1007/s11116-021-10210-7
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    References listed on IDEAS

    as
    1. Ali Najmi & Sahar Nazari & Farshid Safarighouzhdi & C Raina MacIntyre & Eric J Miller & Taha H. Rashidi, 2021. "Facemask and social distancing, pillars of opening up economies," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-13, April.
    2. Ali Najmi & Taha H. Rashidi & James Vaughan & Eric J. Miller, 2020. "Calibration of large-scale transport planning models: a structured approach," Transportation, Springer, vol. 47(4), pages 1867-1905, August.
    3. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
    4. Roorda, Matthew J. & Carrasco, Juan A. & Miller, Eric J., 2009. "An integrated model of vehicle transactions, activity scheduling and mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 43(2), pages 217-229, February.
    5. Ali Najmi & Taha H. Rashidi & Eric J. Miller, 2019. "A novel approach for systematically calibrating transport planning model systems," Transportation, Springer, vol. 46(5), pages 1915-1950, October.
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