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Estimation and sensitivity analysis of a COVID-19 model considering the use of face mask and vaccination

To model the COVID-19 infection and develop effective control measures, this paper proposes an SEIR-type epidemic model considering the impact of face-mask wearing and vaccination. Firstly, the effective reproduction number and the threshold conditions are obtained. Secondly, based on the data of So...

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Autores principales: Bai, Zhongtian, Ma, Zhihui, Jing, Libaihe, Li, Yonghong, Wang, Shufan, Wang, Bin-Guo, Wu, Yan, Han, Xiaotao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116124/
https://www.ncbi.nlm.nih.gov/pubmed/37081069
http://dx.doi.org/10.1038/s41598-023-33499-z
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author Bai, Zhongtian
Ma, Zhihui
Jing, Libaihe
Li, Yonghong
Wang, Shufan
Wang, Bin-Guo
Wu, Yan
Han, Xiaotao
author_facet Bai, Zhongtian
Ma, Zhihui
Jing, Libaihe
Li, Yonghong
Wang, Shufan
Wang, Bin-Guo
Wu, Yan
Han, Xiaotao
author_sort Bai, Zhongtian
collection PubMed
description To model the COVID-19 infection and develop effective control measures, this paper proposes an SEIR-type epidemic model considering the impact of face-mask wearing and vaccination. Firstly, the effective reproduction number and the threshold conditions are obtained. Secondly, based on the data of South Korea from January 20, 2022 to March 21, 2022, the model parameters are estimated. Finally, a sensitivity analysis and the numerical study are conducted. The results show that the face-mask wearing is associated with [Formula: see text] and [Formula: see text] reductions in the numbers of cumulative cases and newly confirmed cases, respectively, after a period of 60 days, when the face mask wearing rate increases by [Formula: see text] . Furthermore, the vaccination rate is associated with [Formula: see text] and [Formula: see text] reductions in the numbers of cumulative cases and the newly confirmed cases, respectively, after the same period of 60 days when the vaccination rate is increased by [Formula: see text] . A combined measure involving face-mask wearing and vaccination may be more effective and reasonable in preventing and controlling this infection. It is also suggested that disease control departments should strongly recommended the wearing of face masks s as well as vaccination to prevent the unvaccinated people from becoming infected.
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spelling pubmed-101161242023-04-22 Estimation and sensitivity analysis of a COVID-19 model considering the use of face mask and vaccination Bai, Zhongtian Ma, Zhihui Jing, Libaihe Li, Yonghong Wang, Shufan Wang, Bin-Guo Wu, Yan Han, Xiaotao Sci Rep Article To model the COVID-19 infection and develop effective control measures, this paper proposes an SEIR-type epidemic model considering the impact of face-mask wearing and vaccination. Firstly, the effective reproduction number and the threshold conditions are obtained. Secondly, based on the data of South Korea from January 20, 2022 to March 21, 2022, the model parameters are estimated. Finally, a sensitivity analysis and the numerical study are conducted. The results show that the face-mask wearing is associated with [Formula: see text] and [Formula: see text] reductions in the numbers of cumulative cases and newly confirmed cases, respectively, after a period of 60 days, when the face mask wearing rate increases by [Formula: see text] . Furthermore, the vaccination rate is associated with [Formula: see text] and [Formula: see text] reductions in the numbers of cumulative cases and the newly confirmed cases, respectively, after the same period of 60 days when the vaccination rate is increased by [Formula: see text] . A combined measure involving face-mask wearing and vaccination may be more effective and reasonable in preventing and controlling this infection. It is also suggested that disease control departments should strongly recommended the wearing of face masks s as well as vaccination to prevent the unvaccinated people from becoming infected. Nature Publishing Group UK 2023-04-20 /pmc/articles/PMC10116124/ /pubmed/37081069 http://dx.doi.org/10.1038/s41598-023-33499-z Text en © The Author(s) 2023 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
Bai, Zhongtian
Ma, Zhihui
Jing, Libaihe
Li, Yonghong
Wang, Shufan
Wang, Bin-Guo
Wu, Yan
Han, Xiaotao
Estimation and sensitivity analysis of a COVID-19 model considering the use of face mask and vaccination
title Estimation and sensitivity analysis of a COVID-19 model considering the use of face mask and vaccination
title_full Estimation and sensitivity analysis of a COVID-19 model considering the use of face mask and vaccination
title_fullStr Estimation and sensitivity analysis of a COVID-19 model considering the use of face mask and vaccination
title_full_unstemmed Estimation and sensitivity analysis of a COVID-19 model considering the use of face mask and vaccination
title_short Estimation and sensitivity analysis of a COVID-19 model considering the use of face mask and vaccination
title_sort estimation and sensitivity analysis of a covid-19 model considering the use of face mask and vaccination
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116124/
https://www.ncbi.nlm.nih.gov/pubmed/37081069
http://dx.doi.org/10.1038/s41598-023-33499-z
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