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Behavioral dynamics of COVID‐19: estimating underreporting, multiple waves, and adherence fatigue across 92 nations

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  • Hazhir Rahmandad
  • Tse Yang Lim
  • John Sterman

Abstract

Effective responses to the COVID‐19 pandemic require integrating behavioral factors such as risk‐driven contact reduction, improved treatment, and adherence fatigue with asymptomatic transmission, disease acuity, and hospital capacity. We build one such model and estimate it for all 92 nations with reliable testing data. Cumulative cases and deaths through 22 December 2020 are estimated to be 7.03 and 1.44 times official reports, yielding an infection fatality rate (IFR) of 0.51 percent, which has been declining over time. Absent adherence fatigue, cumulative cases would have been 47 percent lower. Scenarios through June 2021 show that modest improvement in responsiveness could reduce cases and deaths by about 14 percent, more than the impact of vaccinating half of the population by that date. Variations in responsiveness to risk explain two orders of magnitude difference in per‐capita deaths despite reproduction numbers fluctuating around one across nations. A public online simulator facilitates scenario analysis over the coming months. © 2021 System Dynamics Society.

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  • Hazhir Rahmandad & Tse Yang Lim & John Sterman, 2021. "Behavioral dynamics of COVID‐19: estimating underreporting, multiple waves, and adherence fatigue across 92 nations," System Dynamics Review, System Dynamics Society, vol. 37(1), pages 5-31, January.
    • Handle: RePEc:bla:sysdyn:v:37:y:2021:i:1:p:5-31
    DOI: 10.1002/sdr.1673
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    Cited by:

    1. Alexander Chudik & M. Hashem Pesaran & Alessandro Rebucci, 2023. "Social Distancing, Vaccination and Evolution of COVID-19 Transmission Rates in Europe," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(2), pages 474-508, June.
    2. Robin L. Dillon & Vicki M. Bier & Richard Sheffield John & Abdullah Althenayyan, 2023. "Closing the Gap Between Decision Analysis and Policy Analysts Before the Next Pandemic," Decision Analysis, INFORMS, vol. 20(2), pages 109-132, June.
    3. Sabah Bushaj & Xuecheng Yin & Arjeta Beqiri & Donald Andrews & İ. Esra Büyüktahtakın, 2023. "A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization," Annals of Operations Research, Springer, vol. 328(1), pages 245-277, September.
    4. Rocha Filho, T.M. & Mendes, J.F.F. & Lucio, M.L. & Moret, M.A., 2023. "COVID-19 data, mitigation policies and Newcomb–Benford law," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    5. Duggan, Jim & Andrade, Jair & Murphy, Thomas Brendan & Gleeson, James P. & Walsh, Cathal & Nolan, Philip, 2024. "An age-cohort simulation model for generating COVID-19 scenarios: A study from Ireland's pandemic response," European Journal of Operational Research, Elsevier, vol. 313(1), pages 343-358.

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