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A differential equations model-fitting analysis of COVID-19 epidemiological data to explain multi-wave dynamics
Compartmental epidemiological models are, by far, the most popular in the study of dynamics related with infectious diseases. It is, therefore, not surprising that they are frequently used to study the current COVID-19 pandemic. Taking advantage of the real-time availability of COVID-19 related data...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358058/ https://www.ncbi.nlm.nih.gov/pubmed/34381088 http://dx.doi.org/10.1038/s41598-021-95494-6 |
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author | Beira, Maria Jardim Sebastião, Pedro José |
author_facet | Beira, Maria Jardim Sebastião, Pedro José |
author_sort | Beira, Maria Jardim |
collection | PubMed |
description | Compartmental epidemiological models are, by far, the most popular in the study of dynamics related with infectious diseases. It is, therefore, not surprising that they are frequently used to study the current COVID-19 pandemic. Taking advantage of the real-time availability of COVID-19 related data, we perform a compartmental model fitting analysis of the portuguese case, using an online open-access platform with the integrated capability of solving systems of differential equations. This analysis enabled the data-driven validation of the used model and was the basis for robust projections of different future scenarios, namely, increasing the detected infected population, reopening schools at different moments, allowing Easter celebrations to take place and population vaccination. The method presented in this work can easily be used to perform the non-trivial task of simultaneously fitting differential equation solutions to different epidemiological data sets, regardless of the model or country that might be considered in the analysis. |
format | Online Article Text |
id | pubmed-8358058 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83580582021-08-13 A differential equations model-fitting analysis of COVID-19 epidemiological data to explain multi-wave dynamics Beira, Maria Jardim Sebastião, Pedro José Sci Rep Article Compartmental epidemiological models are, by far, the most popular in the study of dynamics related with infectious diseases. It is, therefore, not surprising that they are frequently used to study the current COVID-19 pandemic. Taking advantage of the real-time availability of COVID-19 related data, we perform a compartmental model fitting analysis of the portuguese case, using an online open-access platform with the integrated capability of solving systems of differential equations. This analysis enabled the data-driven validation of the used model and was the basis for robust projections of different future scenarios, namely, increasing the detected infected population, reopening schools at different moments, allowing Easter celebrations to take place and population vaccination. The method presented in this work can easily be used to perform the non-trivial task of simultaneously fitting differential equation solutions to different epidemiological data sets, regardless of the model or country that might be considered in the analysis. Nature Publishing Group UK 2021-08-11 /pmc/articles/PMC8358058/ /pubmed/34381088 http://dx.doi.org/10.1038/s41598-021-95494-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Beira, Maria Jardim Sebastião, Pedro José A differential equations model-fitting analysis of COVID-19 epidemiological data to explain multi-wave dynamics |
title | A differential equations model-fitting analysis of COVID-19 epidemiological data to explain multi-wave dynamics |
title_full | A differential equations model-fitting analysis of COVID-19 epidemiological data to explain multi-wave dynamics |
title_fullStr | A differential equations model-fitting analysis of COVID-19 epidemiological data to explain multi-wave dynamics |
title_full_unstemmed | A differential equations model-fitting analysis of COVID-19 epidemiological data to explain multi-wave dynamics |
title_short | A differential equations model-fitting analysis of COVID-19 epidemiological data to explain multi-wave dynamics |
title_sort | differential equations model-fitting analysis of covid-19 epidemiological data to explain multi-wave dynamics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358058/ https://www.ncbi.nlm.nih.gov/pubmed/34381088 http://dx.doi.org/10.1038/s41598-021-95494-6 |
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