Explicit formulae for the peak time of an epidemic from the SIR model. Which approximant to use?


Kroeger M., TÜRKYILMAZOĞLU M., Schlickeiser R.

PHYSICA D-NONLINEAR PHENOMENA, vol.425, 2021 (Peer-Reviewed Journal) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 425
  • Publication Date: 2021
  • Doi Number: 10.1016/j.physd.2021.132981
  • Journal Name: PHYSICA D-NONLINEAR PHENOMENA
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, INSPEC, MathSciNet, Metadex, zbMATH, Civil Engineering Abstracts
  • Keywords: Epidemic, SIR model, Peak thresholds, Peak time, COVID-19, VACCINATION STRATEGY, MATHEMATICAL-THEORY, ROYAL SOCIETY, THRESHOLD, DYNAMICS, ENDEMICITY, STABILITY, BEHAVIOR

Abstract

An analytic evaluation of the peak time of a disease allows for the installment of effective epidemic precautions. Recently, an explicit analytic, approximate expression (MT) for the peak time of the fraction of infected persons during an outbreak within the susceptible-infectious-recovered/removed (SIR) model had been presented and discussed (Turkyilmazoglu, 2021). There are three existing approximate solutions (SK-I, SK-II, and CG) of the semi-time SIR model in its reduced formulation that allow one to come up with different explicit expressions for the peak time of the infected compartment (Schlickeiser and Kroger, 2021; Carvalho and Goncalves, 2021). Here we compare the four expressions for any choice of SIR model parameters and find that SK-I, SK-II and CG are more accurate than MT as long as the amount of population to which the SIR model is applied exceeds hundred by far (countries, ss, cities). For small populations with less than hundreds of individuals (families, small towns), however, the approximant MT outperforms the other approximants. To be able to compare the various approaches, we clarify the equivalence between the four-parametric dimensional SIR equations and their two-dimensional dimensionless analogue. Using Covid-19 data from various countries and sources we identify the relevant regime within the parameter space of the SIR model. (C) 2021 The Author(s). Published by Elsevier B.V.