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The first 100 days: Modeling the evolution of the COVID-19 pandemic

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  • Kaxiras, Efthimios
  • Neofotistos, Georgios
  • Angelaki, Eleni

Abstract

A simple analytical model for modeling the evolution of the 2020 COVID-19 pandemic is presented. The model is based on the numerical solution of the widely used Susceptible-Infectious-Removed (SIR) populations model for describing epidemics. We consider an expanded version of the original Kermack-McKendrick model, which includes a decaying value of the parameter β (the effective contact rate), interpreted as an effect of externally imposed conditions, to which we refer as the forced-SIR (FSIR) model. We introduce an approximate analytical solution to the differential equations that represent the FSIR model which gives very reasonable fits to real data for a number of countries over a period of 100 days (from the first onset of exponential increase, in China). The proposed model contains 3 adjustable parameters which are obtained by fitting actual data (up to April 28, 2020). We analyze these results to infer the physical meaning of the parameters involved. We use the model to make predictions about the total expected number of infections in each country as well as the date when the number of infections will have reached 99% of this total. We also compare key findings of the model with recently reported results on the high contagiousness and rapid spread of the disease.

Suggested Citation

  • Kaxiras, Efthimios & Neofotistos, Georgios & Angelaki, Eleni, 2020. "The first 100 days: Modeling the evolution of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    • Handle: RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920305117
    DOI: 10.1016/j.chaos.2020.110114
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    References listed on IDEAS

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    1. Chris Groendyke & David Welch & David R. Hunter, 2011. "Bayesian Inference for Contact Networks Given Epidemic Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(3), pages 600-616, September.
    2. Mingxin Zhang & Alexander Verbraeck & Rongqing Meng & Bin Chen & Xiaogang Qiu, 2016. "Modeling Spatial Contacts for Epidemic Prediction in a Large-Scale Artificial City," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(4), pages 1-3.
    3. Barmparis, G.D. & Tsironis, G.P., 2020. "Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    4. Tagliazucchi, E. & Balenzuela, P. & Travizano, M. & Mindlin, G.B. & Mininni, P.D., 2020. "Lessons from being challenged by COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
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    Cited by:

    1. Ballı, Serkan, 2021. "Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    2. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Randolph Hall & Andrew Moore & Mingdong Lyu, 2023. "Tracking Covid-19 cases and deaths in the United States: metrics of pandemic progression derived from a queueing framework," Health Care Management Science, Springer, vol. 26(1), pages 79-92, March.
    4. Fokas, A.S. & Cuevas-Maraver, J. & Kevrekidis, P.G., 2020. "A quantitative framework for exploring exit strategies from the COVID-19 lockdown," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    5. Gandzha, I.S. & Kliushnichenko, O.V. & Lukyanets, S.P., 2021. "Modeling and controlling the spread of epidemic with various social and economic scenarios," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    6. Noha S. Alghamdi & Saeed M. Alghamdi, 2022. "The Role of Digital Technology in Curbing COVID-19," IJERPH, MDPI, vol. 19(14), pages 1-12, July.

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