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Covid-19: predictive mathematical formulae for the number of deaths during lockdown and possible scenarios for the post-lockdown period

In a recent article, we introduced two novel mathematical expressions and a deep learning algorithm for characterizing the dynamics of the number of reported infected cases with SARS-CoV-2. Here, we show that such formulae can also be used for determining the time evolution of the associated number...

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Detalles Bibliográficos
Autores principales: Fokas, Athanassios S., Dikaios, Nikolaos, Kastis, George A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8300658/
https://www.ncbi.nlm.nih.gov/pubmed/35153555
http://dx.doi.org/10.1098/rspa.2020.0745
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author Fokas, Athanassios S.
Dikaios, Nikolaos
Kastis, George A.
author_facet Fokas, Athanassios S.
Dikaios, Nikolaos
Kastis, George A.
author_sort Fokas, Athanassios S.
collection PubMed
description In a recent article, we introduced two novel mathematical expressions and a deep learning algorithm for characterizing the dynamics of the number of reported infected cases with SARS-CoV-2. Here, we show that such formulae can also be used for determining the time evolution of the associated number of deaths: for the epidemics in Spain, Germany, Italy and the UK, the parameters defining these formulae were computed using data up to 1 May 2020, a period of lockdown for these countries; then, the predictions of the formulae were compared with the data for the following 122 days, namely until 1 September. These comparisons, in addition to demonstrating the remarkable predictive capacity of our simple formulae, also show that for a rather long time the easing of the lockdown measures did not affect the number of deaths. The importance of these results regarding predictions of the number of Covid-19 deaths during the post-lockdown period is discussed.
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spelling pubmed-83006582022-02-11 Covid-19: predictive mathematical formulae for the number of deaths during lockdown and possible scenarios for the post-lockdown period Fokas, Athanassios S. Dikaios, Nikolaos Kastis, George A. Proc Math Phys Eng Sci Research Articles In a recent article, we introduced two novel mathematical expressions and a deep learning algorithm for characterizing the dynamics of the number of reported infected cases with SARS-CoV-2. Here, we show that such formulae can also be used for determining the time evolution of the associated number of deaths: for the epidemics in Spain, Germany, Italy and the UK, the parameters defining these formulae were computed using data up to 1 May 2020, a period of lockdown for these countries; then, the predictions of the formulae were compared with the data for the following 122 days, namely until 1 September. These comparisons, in addition to demonstrating the remarkable predictive capacity of our simple formulae, also show that for a rather long time the easing of the lockdown measures did not affect the number of deaths. The importance of these results regarding predictions of the number of Covid-19 deaths during the post-lockdown period is discussed. The Royal Society Publishing 2021-05 2021-05-19 /pmc/articles/PMC8300658/ /pubmed/35153555 http://dx.doi.org/10.1098/rspa.2020.0745 Text en © 2021 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Research Articles
Fokas, Athanassios S.
Dikaios, Nikolaos
Kastis, George A.
Covid-19: predictive mathematical formulae for the number of deaths during lockdown and possible scenarios for the post-lockdown period
title Covid-19: predictive mathematical formulae for the number of deaths during lockdown and possible scenarios for the post-lockdown period
title_full Covid-19: predictive mathematical formulae for the number of deaths during lockdown and possible scenarios for the post-lockdown period
title_fullStr Covid-19: predictive mathematical formulae for the number of deaths during lockdown and possible scenarios for the post-lockdown period
title_full_unstemmed Covid-19: predictive mathematical formulae for the number of deaths during lockdown and possible scenarios for the post-lockdown period
title_short Covid-19: predictive mathematical formulae for the number of deaths during lockdown and possible scenarios for the post-lockdown period
title_sort covid-19: predictive mathematical formulae for the number of deaths during lockdown and possible scenarios for the post-lockdown period
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8300658/
https://www.ncbi.nlm.nih.gov/pubmed/35153555
http://dx.doi.org/10.1098/rspa.2020.0745
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