Modelling the interplay of SARS-CoV-2 variants in the United Kingdom

Autores
Barreiro, Nadia Luisina; Govezensky, T.; Ventura, Cecilia Ileana; Nuñez, Matias; Bolcatto, Pablo Guillermo; Barrio, R. A.
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Barreiro, Nadia Luisina. Ministerio de Defensa. Instituto de Investigaciones Científicas y Técnicas para la Defensa; Argentina
Fil: Govezensky, T.. Universidad Nacional Autónoma de México; México
Fil: Ventura, Cecilia Ileana. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina
Fil: Nuñez, Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina
Fil: Bolcatto, Pablo Guillermo. Ministerio de Defensa. Instituto de Investigaciones Científicas y Técnicas para la Defensa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina
Fil: Barrio, R. A.. Universidad Nacional Autónoma de México; México
Materia
COVID-19
Vaccines
Strains
United Kingdom
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/202320
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Modelling the interplay of SARS-CoV-2 variants in the United KingdomBarreiro, Nadia LuisinaGovezensky, T.Ventura, Cecilia IleanaNuñez, MatiasBolcatto, Pablo GuillermoBarrio, R. A.COVID-19VaccinesStrainsUnited Kingdomhttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3Many 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.Fil: Barreiro, Nadia Luisina. Ministerio de Defensa. Instituto de Investigaciones Científicas y Técnicas para la Defensa; ArgentinaFil: Govezensky, T.. Universidad Nacional Autónoma de México; MéxicoFil: Ventura, Cecilia Ileana. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; ArgentinaFil: Nuñez, Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaFil: Bolcatto, Pablo Guillermo. Ministerio de Defensa. Instituto de Investigaciones Científicas y Técnicas para la Defensa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; ArgentinaFil: Barrio, R. A.. Universidad Nacional Autónoma de México; MéxicoNature2022-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/202320Barreiro, Nadia Luisina; Govezensky, T.; Ventura, Cecilia Ileana; Nuñez, Matias; Bolcatto, Pablo Guillermo; et al.; Modelling the interplay of SARS-CoV-2 variants in the United Kingdom; Nature; Scientific Reports; 12; 1; 12-2022; 1-82045-2322CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-022-16147-winfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:26:35Zoai:ri.conicet.gov.ar:11336/202320instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:26:36.273CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv 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
spellingShingle Modelling the interplay of SARS-CoV-2 variants in the United Kingdom
Barreiro, Nadia Luisina
COVID-19
Vaccines
Strains
United Kingdom
title_short 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_sort Modelling the interplay of SARS-CoV-2 variants in the United Kingdom
dc.creator.none.fl_str_mv Barreiro, Nadia Luisina
Govezensky, T.
Ventura, Cecilia Ileana
Nuñez, Matias
Bolcatto, Pablo Guillermo
Barrio, R. A.
author Barreiro, Nadia Luisina
author_facet Barreiro, Nadia Luisina
Govezensky, T.
Ventura, Cecilia Ileana
Nuñez, Matias
Bolcatto, Pablo Guillermo
Barrio, R. A.
author_role author
author2 Govezensky, T.
Ventura, Cecilia Ileana
Nuñez, Matias
Bolcatto, Pablo Guillermo
Barrio, R. A.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv COVID-19
Vaccines
Strains
United Kingdom
topic COVID-19
Vaccines
Strains
United Kingdom
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.3
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv 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.
Fil: Barreiro, Nadia Luisina. Ministerio de Defensa. Instituto de Investigaciones Científicas y Técnicas para la Defensa; Argentina
Fil: Govezensky, T.. Universidad Nacional Autónoma de México; México
Fil: Ventura, Cecilia Ileana. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina
Fil: Nuñez, Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina
Fil: Bolcatto, Pablo Guillermo. Ministerio de Defensa. Instituto de Investigaciones Científicas y Técnicas para la Defensa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina
Fil: Barrio, R. A.. Universidad Nacional Autónoma de México; México
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.
publishDate 2022
dc.date.none.fl_str_mv 2022-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/202320
Barreiro, Nadia Luisina; Govezensky, T.; Ventura, Cecilia Ileana; Nuñez, Matias; Bolcatto, Pablo Guillermo; et al.; Modelling the interplay of SARS-CoV-2 variants in the United Kingdom; Nature; Scientific Reports; 12; 1; 12-2022; 1-8
2045-2322
CONICET Digital
CONICET
url http://hdl.handle.net/11336/202320
identifier_str_mv Barreiro, Nadia Luisina; Govezensky, T.; Ventura, Cecilia Ileana; Nuñez, Matias; Bolcatto, Pablo Guillermo; et al.; Modelling the interplay of SARS-CoV-2 variants in the United Kingdom; Nature; Scientific Reports; 12; 1; 12-2022; 1-8
2045-2322
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-022-16147-w
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Nature
publisher.none.fl_str_mv Nature
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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