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A SIR-type model describing the successive waves of COVID-19

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  • Muñoz-Fernández, Gustavo A.
  • Seoane, Jesús M.
  • Seoane-Sepúlveda, Juan B.

Abstract

It is well-known that the classical SIR model is unable to make accurate predictions on the course of illnesses such as COVID-19. In this paper, we show that the official data released by the authorities of several countries (Italy, Spain and The USA) regarding the expansion of COVID-19 are compatible with a non-autonomous SIR type model with vital dynamics and non-constant population, calibrated according to exponentially decaying infection and death rates. Using this calibration we construct a model whose outcomes for most relevant epidemiological paramenters, such as the number of active cases, cumulative deaths, daily new deaths and daily new cases (among others) fit available real data about the first and successive waves of COVID-19. In addition to this, we also provide predictions on the evolution of this pandemic in Italy and the USA in several plausible scenarios.

Suggested Citation

  • Muñoz-Fernández, Gustavo A. & Seoane, Jesús M. & Seoane-Sepúlveda, Juan B., 2021. "A SIR-type model describing the successive waves of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    • Handle: RePEc:eee:chsofr:v:144:y:2021:i:c:s0960077921000357
    DOI: 10.1016/j.chaos.2021.110682
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    Citations

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    Cited by:

    1. Shah, Kamal & Abdeljawad, Thabet & Ud Din, Rahim, 2022. "To study the transmission dynamic of SARS-CoV-2 using nonlinear saturated incidence rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    2. Vivekanandhan, Gayathri & Nourian Zavareh, Mahdi & Natiq, Hayder & Nazarimehr, Fahimeh & Rajagopal, Karthikeyan & Svetec, Milan, 2022. "Investigation of vaccination game approach in spreading covid-19 epidemic model with considering the birth and death rates," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    3. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    4. RabieiMotlagh, Omid & Soleimani, Leila, 2023. "Effect of mutations on stochastic dynamics of infectious diseases, a probability approach," Applied Mathematics and Computation, Elsevier, vol. 451(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).

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