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Estimation of COVID-19 dynamics “on a back-of-envelope”: Does the simplest SIR model provide quantitative parameters and predictions?

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  • Postnikov, Eugene B.

Abstract

Basing on existence of the mathematically sequential reduction of the three-compartmental (Susceptible-Infected-Recovered/Removed) model to the Verhulst (logistic) equation with the parameters determined by the basic characteristic of epidemic process, this model is tested in application to the recent data on COVID-19 outbreak reported by the European Centre for Disease Prevention and Control. It is shown that such a simple model adequately reproduces the epidemic dynamics not only qualitatively but for a number of countries quantitatively with a high degree of correlation that allows to use it for predictive estimations. In addition, some features of SIR model are discussed in the context, how its parameters and conditions reflect measures attempted for the disease growth prevention that is also clearly indicated by deviations from such model solutions.

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  • Postnikov, Eugene B., 2020. "Estimation of COVID-19 dynamics “on a back-of-envelope”: Does the simplest SIR model provide quantitative parameters and predictions?," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    • Handle: RePEc:eee:chsofr:v:135:y:2020:i:c:s0960077920302411
    DOI: 10.1016/j.chaos.2020.109841
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    References listed on IDEAS

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    1. Eugene B. Postnikov, 2016. "Dynamical prediction of flu seasonality driven by ambient temperature: influenza vs. common cold," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(1), pages 1-6, January.
    2. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    3. Eugene B. Postnikov, 2016. "Dynamical prediction of flu seasonality driven by ambient temperature: influenza vs. common cold," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(1), pages 1-6, January.
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    7. Hwang, Tienyu, 2021. "Coronavirus lockdown and virus suppression: An international analysis," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
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    12. Adak, Debadatta & Majumder, Abhijit & Bairagi, Nandadulal, 2021. "Mathematical perspective of Covid-19 pandemic: Disease extinction criteria in deterministic and stochastic models," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
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    14. Jones, Andrew & Strigul, Nikolay, 2021. "Is spread of COVID-19 a chaotic epidemic?," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    15. Yeşilkanat, Cafer Mert, 2020. "Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    16. S. A. Trigger & A. M. Ignatov, 2022. "Strain-stream model of epidemic spread in application to COVID-19," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(11), pages 1-8, November.
    17. Haiyue Chen & Benedikt Haus & Paolo Mercorelli, 2021. "Extension of SEIR Compartmental Models for Constructive Lyapunov Control of COVID-19 and Analysis in Terms of Practical Stability," Mathematics, MDPI, vol. 9(17), pages 1-25, August.
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