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A new modelling of the COVID 19 pandemic

Author

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  • Soukhovolsky, Vladislav
  • Kovalev, Anton
  • Pitt, Anne
  • Kessel, Boris

Abstract

А model of coronavirus incidence is proposed. Process of disease development is represented as analogue of first- and second order phase transition in physical systems. The model is very simple in terms of the data necessary for the calculations. To verify the proposed model, only data on the current incidence rate are required. However, the determination coefficient of model R2 is very high and exceeds 0.95 for most countries. The model permits the accurate prediction of the pandemics dynamics at intervals of up to 10 days. The ADL(autoregressive distributed lag)-model was introduced in addition to the phase transition model to describe the development of the disease at the exponential phase.The ADL-model allows describing nonmonotonic changes in relative infection over the time, and providing to governments and health care decision makers the possibility to predict the outcomes of their decisions on public health.

Suggested Citation

  • Soukhovolsky, Vladislav & Kovalev, Anton & Pitt, Anne & Kessel, Boris, 2020. "A new modelling of the COVID 19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:chsofr:v:139:y:2020:i:c:s0960077920304379
    DOI: 10.1016/j.chaos.2020.110039
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    References listed on IDEAS

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    1. Ebenezer Bonyah & Muhammad Altaf Khan & K O Okosun & Saeed Islam, 2017. "A theoretical model for Zika virus transmission," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-26, October.
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    Cited by:

    1. Hwang, Eunju, 2022. "Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement," Chaos, Solitons & Fractals, Elsevier, vol. 155(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. Ullah, Ihsan & Ahmad, Saeed & Rahman, Mati ur & Arfan, Muhammad, 2021. "Investigation of fractional order tuberculosis (TB) model via Caputo derivative," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    4. Soukhovolsky, Vladislav & Kovalev, Anton & Pitt, Anne & Shulman, Katerina & Tarasova, Olga & Kessel, Boris, 2021. "The Cyclicity of coronavirus cases: “Waves” and the “weekend effect”," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    5. Mishra, Bimal Kumar & Keshri, Ajit Kumar & Saini, Dinesh Kumar & Ayesha, Syeda & Mishra, Binay Kumar & Rao, Yerra Shankar, 2021. "Mathematical model, forecast and analysis on the spread of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).

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