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A modified SEIR model to predict the behavior of the early stage in coronavirus and coronavirus-like outbreaks

COVID-19 is a highly infectious disease that emerged in China at the end of 2019. The COVID-19 pandemic is the first known pandemic caused by a coronavirus, namely, the new and emerging SARS-CoV-2 coronavirus. In the present work, we present simulations of the initial outbreak of this new coronaviru...

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Autores principales: Angulo, Wilfredo, Ramírez, José M., De Cecchis, Dany, Primera, Juan, Pacheco, Henry, Rodríguez-Román, Eduardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357795/
https://www.ncbi.nlm.nih.gov/pubmed/34381100
http://dx.doi.org/10.1038/s41598-021-95785-y
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author Angulo, Wilfredo
Ramírez, José M.
De Cecchis, Dany
Primera, Juan
Pacheco, Henry
Rodríguez-Román, Eduardo
author_facet Angulo, Wilfredo
Ramírez, José M.
De Cecchis, Dany
Primera, Juan
Pacheco, Henry
Rodríguez-Román, Eduardo
author_sort Angulo, Wilfredo
collection PubMed
description COVID-19 is a highly infectious disease that emerged in China at the end of 2019. The COVID-19 pandemic is the first known pandemic caused by a coronavirus, namely, the new and emerging SARS-CoV-2 coronavirus. In the present work, we present simulations of the initial outbreak of this new coronavirus using a modified transmission rate SEIR model that takes into account the impact of government actions and the perception of risk by individuals in reaction to the proportion of fatal cases. The parameters related to these effects were fitted to the number of infected cases in the 33 provinces of China. The data for Hubei Province, the probable site of origin of the current pandemic, were considered as a particular case for the simulation and showed that the theoretical model reproduces the behavior of the data, thus indicating the importance of combining government actions and individual risk perceptions when the proportion of fatal cases is greater than [Formula: see text] . The results show that the adjusted model reproduces the behavior of the data quite well for some provinces, suggesting that the spread of the disease differs when different actions are evaluated. The proposed model could help to predict outbreaks of viruses with a biological and molecular structure similar to that of SARS-CoV-2.
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spelling pubmed-83577952021-08-13 A modified SEIR model to predict the behavior of the early stage in coronavirus and coronavirus-like outbreaks Angulo, Wilfredo Ramírez, José M. De Cecchis, Dany Primera, Juan Pacheco, Henry Rodríguez-Román, Eduardo Sci Rep Article COVID-19 is a highly infectious disease that emerged in China at the end of 2019. The COVID-19 pandemic is the first known pandemic caused by a coronavirus, namely, the new and emerging SARS-CoV-2 coronavirus. In the present work, we present simulations of the initial outbreak of this new coronavirus using a modified transmission rate SEIR model that takes into account the impact of government actions and the perception of risk by individuals in reaction to the proportion of fatal cases. The parameters related to these effects were fitted to the number of infected cases in the 33 provinces of China. The data for Hubei Province, the probable site of origin of the current pandemic, were considered as a particular case for the simulation and showed that the theoretical model reproduces the behavior of the data, thus indicating the importance of combining government actions and individual risk perceptions when the proportion of fatal cases is greater than [Formula: see text] . The results show that the adjusted model reproduces the behavior of the data quite well for some provinces, suggesting that the spread of the disease differs when different actions are evaluated. The proposed model could help to predict outbreaks of viruses with a biological and molecular structure similar to that of SARS-CoV-2. Nature Publishing Group UK 2021-08-11 /pmc/articles/PMC8357795/ /pubmed/34381100 http://dx.doi.org/10.1038/s41598-021-95785-y 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
Angulo, Wilfredo
Ramírez, José M.
De Cecchis, Dany
Primera, Juan
Pacheco, Henry
Rodríguez-Román, Eduardo
A modified SEIR model to predict the behavior of the early stage in coronavirus and coronavirus-like outbreaks
title A modified SEIR model to predict the behavior of the early stage in coronavirus and coronavirus-like outbreaks
title_full A modified SEIR model to predict the behavior of the early stage in coronavirus and coronavirus-like outbreaks
title_fullStr A modified SEIR model to predict the behavior of the early stage in coronavirus and coronavirus-like outbreaks
title_full_unstemmed A modified SEIR model to predict the behavior of the early stage in coronavirus and coronavirus-like outbreaks
title_short A modified SEIR model to predict the behavior of the early stage in coronavirus and coronavirus-like outbreaks
title_sort modified seir model to predict the behavior of the early stage in coronavirus and coronavirus-like outbreaks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357795/
https://www.ncbi.nlm.nih.gov/pubmed/34381100
http://dx.doi.org/10.1038/s41598-021-95785-y
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