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Global convergence of COVID-19 basic reproduction number and estimation from early-time SIR dynamics

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  • Gabriel G Katul
  • Assaad Mrad
  • Sara Bonetti
  • Gabriele Manoli
  • Anthony J Parolari

Abstract

The SIR (‘susceptible-infectious-recovered’) formulation is used to uncover the generic spread mechanisms observed by COVID-19 dynamics globally, especially in the early phases of infectious spread. During this early period, potential controls were not effectively put in place or enforced in many countries. Hence, the early phases of COVID-19 spread in countries where controls were weak offer a unique perspective on the ensemble-behavior of COVID-19 basic reproduction number Ro inferred from SIR formulation. The work here shows that there is global convergence (i.e., across many nations) to an uncontrolled Ro = 4.5 that describes the early time spread of COVID-19. This value is in agreement with independent estimates from other sources reviewed here and adds to the growing consensus that the early estimate of Ro = 2.2 adopted by the World Health Organization is low. A reconciliation between power-law and exponential growth predictions is also featured within the confines of the SIR formulation. The effects of testing ramp-up and the role of ‘super-spreaders’ on the inference of Ro are analyzed using idealized scenarios. Implications for evaluating potential control strategies from this uncontrolled Ro are briefly discussed in the context of the maximum possible infected fraction of the population (needed to assess health care capacity) and mortality (especially in the USA given diverging projections). Model results indicate that if intervention measures still result in Ro > 2.7 within 44 days after first infection, intervention is unlikely to be effective in general for COVID-19.

Suggested Citation

  • Gabriel G Katul & Assaad Mrad & Sara Bonetti & Gabriele Manoli & Anthony J Parolari, 2020. "Global convergence of COVID-19 basic reproduction number and estimation from early-time SIR dynamics," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-22, September.
  • Handle: RePEc:plo:pone00:0239800
    DOI: 10.1371/journal.pone.0239800
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    Cited by:

    1. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," CESifo Working Paper Series 8977, CESifo.
    2. Mendonça, J.P. & Brum, Arthur A. & Lyra, M.L. & Lira, Sérgio A., 2024. "Evolutionary game dynamics and the phase portrait diversity in a pandemic scenario," Applied Mathematics and Computation, Elsevier, vol. 475(C).
    3. Sefa Awaworyi Churchill & John Inekwe & Kris Ivanovski, 2023. "Has the COVID-19 pandemic converged across countries?," Empirical Economics, Springer, vol. 64(5), pages 2027-2052, May.
    4. Lee, Chaeyoung & Kwak, Soobin & Kim, Sangkwon & Hwang, Youngjin & Choi, Yongho & Kim, Junseok, 2021. "Robust optimal parameter estimation for the susceptible-unidentified infected-confirmed model," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    5. Panagiotidis, Theodore & Papapanagiotou, Georgios & Stengos, Thanasis, 2023. "Dying together: A convergence analysis of fatalities during COVID-19," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    6. Ida Johnsson & M. Hashem Pesaran & Cynthia Fan Yang, 2023. "Structural Econometric Estimation of the Basic Reproduction Number for Covid-19 across U.S. States and Selected Countries," CESifo Working Paper Series 10659, CESifo.
    7. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers 21-18, Federal Reserve Bank of Philadelphia.
    8. Martínez-Guerra, Rafael & Flores-Flores, Juan Pablo, 2021. "An algorithm for the robust estimation of the COVID-19 pandemic’s population by considering undetected individuals," Applied Mathematics and Computation, Elsevier, vol. 405(C).
    9. Jonas E. Arias & Jesús Fernández-Villaverde & Juan Rubio Ramírez & Minchul Shin, 2021. "The Causal Effects of Lockdown Policies on Health and Macroeconomic Outcomes," NBER Working Papers 28617, National Bureau of Economic Research, Inc.
    10. Papageorgiou, Vasileios E. & Tsaklidis, George, 2023. "An improved epidemiological-unscented Kalman filter (hybrid SEIHCRDV-UKF) model for the prediction of COVID-19. Application on real-time data," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).

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