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Logistic equation and COVID-19

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  • Pelinovsky, Efim
  • Kurkin, Andrey
  • Kurkina, Oxana
  • Kokoulina, Maria
  • Epifanova, Anastasia

Abstract

The generalized logistic equation is used to interpret the COVID-19 epidemic data in several countries: Austria, Switzerland, the Netherlands, Italy, Turkey and South Korea. The model coefficients are calculated: the growth rate and the expected number of infected people, as well as the exponent indexes in the generalized logistic equation. It is shown that the dependence of the number of the infected people on time is well described on average by the logistic curve (within the framework of a simple or generalized logistic equation) with a determination coefficient exceeding 0.8. At the same time, the dependence of the number of the infected people per day on time has a very uneven character and can be described very roughly by the logistic curve. To describe it, it is necessary to take into account the dependence of the model coefficients on time or on the total number of cases. Variations, for example, of the growth rate can reach 60%. The variability spectra of the coefficients have characteristic peaks at periods of several days, which corresponds to the observed serial intervals. The use of the stochastic logistic equation is proposed to estimate the number of probable peaks in the coronavirus incidence.

Suggested Citation

  • Pelinovsky, Efim & Kurkin, Andrey & Kurkina, Oxana & Kokoulina, Maria & Epifanova, Anastasia, 2020. "Logistic equation and COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920306378
    DOI: 10.1016/j.chaos.2020.110241
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    References listed on IDEAS

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    1. Calatayud, Julia & Jornet, Marc & Mateu, Jorge & Pinto, Carla M.A., 2023. "A new population model for urban infestations," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    2. Otunuga, Olusegun Michael, 2021. "Time-dependent probability distribution for number of infection in a stochastic SIS model: case study COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    3. Pelinovsky, E. & Kokoulina, M. & Epifanova, A. & Kurkin, A. & Kurkina, O. & Tang, M. & Macau, E. & Kirillin, M., 2022. "Gompertz model in COVID-19 spreading simulation," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    4. Paul, Ayan & Reja, Selim & Kundu, Sayani & Bhattacharya, Sabyasachi, 2021. "COVID-19 pandemic models revisited with a new proposal: Plenty of epidemiological models outcast the simple population dynamics solution," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    5. Wang, Qiubao & Hu, Zhouyu & Yang, Yanling & Zhang, Congqing & Han, Zikun, 2023. "The impact of memory effect on time-delay logistic systems driven by a class of non-Gaussian noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    6. Perrier, Frédéric & Girault, Frédéric, 2022. "Scaling and fine structure of superstable periodic orbits in the logistic map," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    7. Area, I. & Nieto, J.J., 2021. "Power series solution of the fractional logistic equation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).

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