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Modelling the interplay of SARS-CoV-2 variants in the United Kingdom
Many COVID-19 vaccines are proving to be highly effective to prevent severe disease and to diminish infections. Their uneven geographical distribution favors the appearance of new variants of concern, as the highly transmissible Delta variant, affecting particularly non-vaccinated people. It is impo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9296900/ https://www.ncbi.nlm.nih.gov/pubmed/35859100 http://dx.doi.org/10.1038/s41598-022-16147-w |
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author | Barreiro, N. L. Govezensky, T. Ventura, C. I. Núñez, M. Bolcatto, P. G. Barrio, R. A. |
author_facet | Barreiro, N. L. Govezensky, T. Ventura, C. I. Núñez, M. Bolcatto, P. G. Barrio, R. A. |
author_sort | Barreiro, N. L. |
collection | PubMed |
description | Many COVID-19 vaccines are proving to be highly effective to prevent severe disease and to diminish infections. Their uneven geographical distribution favors the appearance of new variants of concern, as the highly transmissible Delta variant, affecting particularly non-vaccinated people. It is important to device reliable models to analyze the spread of the different variants. A key factor is to consider the effects of vaccination as well as other measures used to contain the pandemic like social behaviour. The stochastic geographical model presented here, fulfills these requirements. It is based on an extended compartmental model that includes various strains and vaccination strategies, allowing to study the emergence and dynamics of the new COVID-19 variants. The model conveniently separates the parameters related to the disease from the ones related to social behavior and mobility restrictions. We applied the model to the United Kingdom by using available data to fit the recurrence of the currently prevalent variants. Our computer simulations allow to describe the appearance of periodic waves and the features that determine the prevalence of certain variants. They also provide useful predictions to help planning future vaccination boosters. We stress that the model could be applied to any other country of interest. |
format | Online Article Text |
id | pubmed-9296900 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92969002022-07-20 Modelling the interplay of SARS-CoV-2 variants in the United Kingdom Barreiro, N. L. Govezensky, T. Ventura, C. I. Núñez, M. Bolcatto, P. G. Barrio, R. A. Sci Rep Article Many COVID-19 vaccines are proving to be highly effective to prevent severe disease and to diminish infections. Their uneven geographical distribution favors the appearance of new variants of concern, as the highly transmissible Delta variant, affecting particularly non-vaccinated people. It is important to device reliable models to analyze the spread of the different variants. A key factor is to consider the effects of vaccination as well as other measures used to contain the pandemic like social behaviour. The stochastic geographical model presented here, fulfills these requirements. It is based on an extended compartmental model that includes various strains and vaccination strategies, allowing to study the emergence and dynamics of the new COVID-19 variants. The model conveniently separates the parameters related to the disease from the ones related to social behavior and mobility restrictions. We applied the model to the United Kingdom by using available data to fit the recurrence of the currently prevalent variants. Our computer simulations allow to describe the appearance of periodic waves and the features that determine the prevalence of certain variants. They also provide useful predictions to help planning future vaccination boosters. We stress that the model could be applied to any other country of interest. Nature Publishing Group UK 2022-07-20 /pmc/articles/PMC9296900/ /pubmed/35859100 http://dx.doi.org/10.1038/s41598-022-16147-w Text en © The Author(s) 2022 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 Barreiro, N. L. Govezensky, T. Ventura, C. I. Núñez, M. Bolcatto, P. G. Barrio, R. A. Modelling the interplay of SARS-CoV-2 variants in the United Kingdom |
title | Modelling the interplay of SARS-CoV-2 variants in the United Kingdom |
title_full | Modelling the interplay of SARS-CoV-2 variants in the United Kingdom |
title_fullStr | Modelling the interplay of SARS-CoV-2 variants in the United Kingdom |
title_full_unstemmed | Modelling the interplay of SARS-CoV-2 variants in the United Kingdom |
title_short | Modelling the interplay of SARS-CoV-2 variants in the United Kingdom |
title_sort | modelling the interplay of sars-cov-2 variants in the united kingdom |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9296900/ https://www.ncbi.nlm.nih.gov/pubmed/35859100 http://dx.doi.org/10.1038/s41598-022-16147-w |
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