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Schlickeiser, R., & Kröger, M. Near-Exact Analytical Solution of the SIR-Model for the Precise Temporal Dynamics of Epidemics. Applied Statistical Analysis and Computing. 2024. doi: Retrieved from https://www.sciltp.com/journals/asac/article/view/584
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Article

Near-Exact Analytical Solution of the SIR-Model for the Precise Temporal Dynamics of Epidemics

Reinhard Schlickeiser 1,2,∗ and Martin Kröger 3,4,∗

1 Institut fur Theoretische Physik, Lehrstuhl IV: Weltraum- und Astrophysik, Ruhr-Universitat Bochum, D-44780 Bochum, Germany

2 Institut fur Theoretische Physik und Astrophysik, Christian-Albrechts-Universitat zu Kiel, Leibnizstr. 15, D-24118 Kiel, Germany

3 Magnetism and Interface Physics, Department of Materials, ETH Zurich, CH-8093 Zurich, Switzerland

4 Computational Polymer Physics, Department of Materials, ETH Zurich, CH-8093 Zurich, Switzerland

∗ Correspondence: rsch@tp4.rub.de (R.S.); mk@mat.ethz.ch (M.K.)

Received: 6 November 2024; Revised: 28 November 2024; Accepted: 11 December 2024; Published: 26 December 2024

Abstract: A near-exact analytical solution of the statistical Susceptible-Infectious-Recovered (SIR) epidemics model for a constant ratio k0 of infection to recovery rates is derived. The derived solution is not of inverse form as the known solutions in the literature but expresses rather directly the three compartmental fractions S(τ), I(τ) and R(τ) and thus the rate of new infections j(τ) = S(τ)I(τ) in terms of the single function U(τ) and the reduced time τ (the time-integrated infection rate), involving the principal and non-principal branches of Lambert’s function. Exact analytical formulas for the peak time and the maximum fraction of I(τ) are obtained proving that the rate of new infections peaks before the fraction of infected persons. Our analysis is not entirely analytically exact because the reduced time dependence of the function U(τ) obeying a nonlinear integro-differential equation is only obtained approximately by expanding a double-exponential function to first-order at small reduced times, and employing an accurate simple approximation of the principal Lambert function at large times, respectively.

Keywords:

coronavirus statistical analysis Covid-19 pandemic spreading

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