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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2021
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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. |
format | Online Article Text |
id | pubmed-8300658 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Royal Society Publishing |
record_format | MEDLINE/PubMed |
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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