Logistic equation and COVID-19

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.

Submitted for Chaos, Solitons and Fractals

References in corpus (8)

• Mathematical Modeling of COVID-19 Transmission Dynamics with a Case Study of Wuhan
• Why integral equations should be used instead of differential equations to describe the dynamics of epidemics
• Mathematical Modeling of the Spread of COVID-19 in Moscow and Russian Regions
• COVID-19: Estimating spread in Spain solving an inverse problem with a probabilistic model
• Prospects and limits of SIR-type Mathematical Models to Capture the COVID-19 Pandemic
• The dynamics of natural selection in dispersal-structured populations
• Failure of monotonicity in epidemic models
• The comparison of trends in Spain and the Nederland: a Dynamical compartment model of the transmission of Coronavirus

Cited by in corpus (4)

• The starting dates of COVID-19 multiple waves
• Logistic wavelets and logistic function: An application to model the spread of SARS-CoV-2 virus infections
• A mathematical model for understanding and controlling monkeypox transmission dynamics in the United States and its implications for future epidemic management
• Stability analysis and optimal control of the logistic equation