IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v135y2020ics0960077920302411.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920302411
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.109841?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    3. Bagal, Dilip Kumar & Rath, Arati & Barua, Abhishek & Patnaik, Dulu, 2020. "Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periods," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Freire-Flores, Danton & Llanovarced-Kawles, Nyna & Sanchez-Daza, Anamaria & Olivera-Nappa, Álvaro, 2021. "On the heterogeneous spread of COVID-19 in Chile," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    5. Hwang, Tienyu, 2021. "Coronavirus lockdown and virus suppression: An international analysis," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    6. Milad Haghani & Michiel C. J. Bliemer, 2020. "Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCoV literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2695-2726, December.
    7. Qiu, Hong & Wang, Qian & Wu, Qun & Zhou, Hongyong, 2022. "Does flattening the curve make a difference? An investigation of the COVID-19 pandemic based on an SIR model," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 159-165.
    8. Daniele Proverbio & Françoise Kemp & Stefano Magni & Andreas Husch & Atte Aalto & Laurent Mombaerts & Alexander Skupin & Jorge Gonçalves & Jose Ameijeiras-Alonso & Christophe Ley, 2021. "Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-21, May.
    9. Nathan H. Schumaker & Sydney M. Watkins, 2021. "Adding Space to Disease Models: A Case Study with COVID-19 in Oregon, USA," Land, MDPI, vol. 10(4), pages 1-13, April.
    10. Srivastava, H.M. & Saad, Khaled M. & Khader, M.M., 2020. "An efficient spectral collocation method for the dynamic simulation of the fractional epidemiological model of the Ebola virus," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    11. Iván Area & Henrique Lorenzo & Pedro J. Marcos & Juan J. Nieto, 2021. "One Year of the COVID-19 Pandemic in Galicia: A Global View of Age-Group Statistics during Three Waves," IJERPH, MDPI, vol. 18(10), pages 1-14, May.
    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).
    13. Arash Najmaei & Zahra Sadeghinejad, 2023. "Green and sustainable business models: historical roots, growth trajectory, conceptual architecture and an agenda for future research—A bibliometric review of green and sustainable business models," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 957-999, February.
    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.
    18. Consolini, Giuseppe & Materassi, Massimo, 2020. "A stretched logistic equation for pandemic spreading," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marson, Marta & Migheli, Matteo & Saccone, Donatella, 2022. "Free to Die: Economic Freedoms and Influenza Mortality," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202210, University of Turin.
    2. František Božek & Irena Tušer, 2021. "Measures for Ensuring Sustainability during the Current Spreading of Coronaviruses in the Czech Republic," Sustainability, MDPI, vol. 13(12), pages 1-22, June.
    3. Singhal, Amit & Singh, Pushpendra & Lall, Brejesh & Joshi, Shiv Dutt, 2020. "Modeling and prediction of COVID-19 pandemic using Gaussian mixture model," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    4. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    5. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    6. Song, Jialu & Xie, Hujin & Gao, Bingbing & Zhong, Yongmin & Gu, Chengfan & Choi, Kup-Sze, 2021. "Maximum likelihood-based extended Kalman filter for COVID-19 prediction," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    7. Huang, Yubo & Wu, Yan & Zhang, Weidong, 2020. "Comprehensive identification and isolation policies have effectively suppressed the spread of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    8. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    9. Gaetano Perone, 2020. "An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/07, HEDG, c/o Department of Economics, University of York.
    10. Luca Bonacini & Giovanni Gallo & Fabrizio Patriarca, 2021. "Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 275-301, January.
    11. Memon, Zaibunnisa & Qureshi, Sania & Memon, Bisharat Rasool, 2021. "Assessing the role of quarantine and isolation as control strategies for COVID-19 outbreak: A case study," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    12. Jahanshahi, Hadi & Munoz-Pacheco, Jesus M. & Bekiros, Stelios & Alotaibi, Naif D., 2021. "A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    13. Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H., 2020. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    14. Bimal Kumar Mishra, 2022. "Stochastic models on the transmission of novel COVID-19," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 599-603, April.
    15. Swapnarekha, H. & Behera, Himansu Sekhar & Nayak, Janmenjoy & Naik, Bighnaraj, 2020. "Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    16. Aguilar-Canto, Fernando Javier & de León, Ugo Avila-Ponce & Avila-Vales, Eric, 2022. "Sensitivity theorems of a model of multiple imperfect vaccines for COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    17. Han, Zhimin & Wang, Yi & Cao, Jinde, 2023. "Impact of contact heterogeneity on initial growth behavior of an epidemic: Complex network-based approach," Applied Mathematics and Computation, Elsevier, vol. 451(C).
    18. Ashwin Muniyappan & Balamuralitharan Sundarappan & Poongodi Manoharan & Mounir Hamdi & Kaamran Raahemifar & Sami Bourouis & Vijayakumar Varadarajan, 2022. "Stability and Numerical Solutions of Second Wave Mathematical Modeling on COVID-19 and Omicron Outbreak Strategy of Pandemic: Analytical and Error Analysis of Approximate Series Solutions by Using HPM," Mathematics, MDPI, vol. 10(3), pages 1-27, January.
    19. Reis, Ruy Freitas & de Melo Quintela, Bárbara & de Oliveira Campos, Joventino & Gomes, Johnny Moreira & Rocha, Bernardo Martins & Lobosco, Marcelo & Weber dos Santos, Rodrigo, 2020. "Characterization of the COVID-19 pandemic and the impact of uncertainties, mitigation strategies, and underreporting of cases in South Korea, Italy, and Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    20. Xu, Yuan-Hao & Wang, Hao-Jie & Lu, Zhong-Wen & Hu, Mao-Bin, 2023. "Impact of awareness dissemination on epidemic reaction–diffusion in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:135:y:2020:i:c:s0960077920302411. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.