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Phenomenological dynamics of COVID-19 pandemic: Meta-analysis for adjustment parameters

We present a phenomenological procedure of dealing with the COVID-19 (coronavirus disease 2019) data provided by government health agencies of 11 different countries. Usually, the exact or approximate solutions of susceptible–infected–recovered (or other) model(s) are obtained fitting the data by ad...

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Detalles Bibliográficos
Autores principales: Hojman, Sergio A., Asenjo, Felipe A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AIP Publishing LLC 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7585449/
https://www.ncbi.nlm.nih.gov/pubmed/33138458
http://dx.doi.org/10.1063/5.0019742
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author Hojman, Sergio A.
Asenjo, Felipe A.
author_facet Hojman, Sergio A.
Asenjo, Felipe A.
author_sort Hojman, Sergio A.
collection PubMed
description We present a phenomenological procedure of dealing with the COVID-19 (coronavirus disease 2019) data provided by government health agencies of 11 different countries. Usually, the exact or approximate solutions of susceptible–infected–recovered (or other) model(s) are obtained fitting the data by adjusting the time-independent parameters that are included in those models. Instead of that, in this work, we introduce dynamical parameters whose time-dependence may be phenomenologically obtained by adequately extrapolating a chosen subset of the daily provided data. This phenomenological approach works extremely well to properly adjust the number of infected (and removed) individuals in time for the countries we consider. Besides, it can handle the sub-epidemic events that some countries may experience. In this way, we obtain the evolution of the pandemic without using any a priori model based on differential equations.
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spelling pubmed-75854492020-10-27 Phenomenological dynamics of COVID-19 pandemic: Meta-analysis for adjustment parameters Hojman, Sergio A. Asenjo, Felipe A. Chaos Regular Articles We present a phenomenological procedure of dealing with the COVID-19 (coronavirus disease 2019) data provided by government health agencies of 11 different countries. Usually, the exact or approximate solutions of susceptible–infected–recovered (or other) model(s) are obtained fitting the data by adjusting the time-independent parameters that are included in those models. Instead of that, in this work, we introduce dynamical parameters whose time-dependence may be phenomenologically obtained by adequately extrapolating a chosen subset of the daily provided data. This phenomenological approach works extremely well to properly adjust the number of infected (and removed) individuals in time for the countries we consider. Besides, it can handle the sub-epidemic events that some countries may experience. In this way, we obtain the evolution of the pandemic without using any a priori model based on differential equations. AIP Publishing LLC 2020-10 2020-10-20 /pmc/articles/PMC7585449/ /pubmed/33138458 http://dx.doi.org/10.1063/5.0019742 Text en © 2020 Author(s) Published under license by AIP Publishing. 1054-1500/2020/30(10)/103120/12/$30.00 All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Regular Articles
Hojman, Sergio A.
Asenjo, Felipe A.
Phenomenological dynamics of COVID-19 pandemic: Meta-analysis for adjustment parameters
title Phenomenological dynamics of COVID-19 pandemic: Meta-analysis for adjustment parameters
title_full Phenomenological dynamics of COVID-19 pandemic: Meta-analysis for adjustment parameters
title_fullStr Phenomenological dynamics of COVID-19 pandemic: Meta-analysis for adjustment parameters
title_full_unstemmed Phenomenological dynamics of COVID-19 pandemic: Meta-analysis for adjustment parameters
title_short Phenomenological dynamics of COVID-19 pandemic: Meta-analysis for adjustment parameters
title_sort phenomenological dynamics of covid-19 pandemic: meta-analysis for adjustment parameters
topic Regular Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7585449/
https://www.ncbi.nlm.nih.gov/pubmed/33138458
http://dx.doi.org/10.1063/5.0019742
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