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Explicit formulae for the peak time of an epidemic from the SIR model. Which approximant to use?
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier B.V.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8225312/ https://www.ncbi.nlm.nih.gov/pubmed/34188342 http://dx.doi.org/10.1016/j.physd.2021.132981 |
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author | Kröger, Martin Turkyilmazoglu, Mustafa Schlickeiser, Reinhard |
author_facet | Kröger, Martin Turkyilmazoglu, Mustafa Schlickeiser, Reinhard |
author_sort | Kröger, Martin |
collection | PubMed |
description | 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 Kröger, 2021; Carvalho and Gonçalves, 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. |
format | Online Article Text |
id | pubmed-8225312 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Author(s). Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82253122021-06-25 Explicit formulae for the peak time of an epidemic from the SIR model. Which approximant to use? Kröger, Martin Turkyilmazoglu, Mustafa Schlickeiser, Reinhard Physica D Article 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 Kröger, 2021; Carvalho and Gonçalves, 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. The Author(s). Published by Elsevier B.V. 2021-11 2021-06-24 /pmc/articles/PMC8225312/ /pubmed/34188342 http://dx.doi.org/10.1016/j.physd.2021.132981 Text en © 2021 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Kröger, Martin Turkyilmazoglu, Mustafa Schlickeiser, Reinhard Explicit formulae for the peak time of an epidemic from the SIR model. Which approximant to use? |
title | Explicit formulae for the peak time of an epidemic from the SIR model. Which approximant to use? |
title_full | Explicit formulae for the peak time of an epidemic from the SIR model. Which approximant to use? |
title_fullStr | Explicit formulae for the peak time of an epidemic from the SIR model. Which approximant to use? |
title_full_unstemmed | Explicit formulae for the peak time of an epidemic from the SIR model. Which approximant to use? |
title_short | Explicit formulae for the peak time of an epidemic from the SIR model. Which approximant to use? |
title_sort | explicit formulae for the peak time of an epidemic from the sir model. which approximant to use? |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8225312/ https://www.ncbi.nlm.nih.gov/pubmed/34188342 http://dx.doi.org/10.1016/j.physd.2021.132981 |
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