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dc.contributor.author
Barreiro, Nadia Luisina  
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Govezensky, T.  
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Ventura, Cecilia Ileana  
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Nuñez, Matias  
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Bolcatto, Pablo Guillermo  
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Barrio, R. A.  
dc.date.available
2023-07-04T17:46:20Z  
dc.date.issued
2022-12  
dc.identifier.citation
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  
dc.identifier.issn
2045-2322  
dc.identifier.uri
http://hdl.handle.net/11336/202320  
dc.description.abstract
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.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Nature  
dc.rights
info:eu-repo/semantics/openAccess  
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https://creativecommons.org/licenses/by/2.5/ar/  
dc.subject
COVID-19  
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Vaccines  
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Strains  
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United Kingdom  
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Epidemiología  
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Ciencias de la Salud  
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CIENCIAS MÉDICAS Y DE LA SALUD  
dc.title
Modelling the interplay of SARS-CoV-2 variants in the United Kingdom  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2023-06-29T10:08:26Z  
dc.journal.volume
12  
dc.journal.number
1  
dc.journal.pagination
1-8  
dc.journal.pais
Estados Unidos  
dc.description.fil
Fil: Barreiro, Nadia Luisina. Ministerio de Defensa. Instituto de Investigaciones Científicas y Técnicas para la Defensa; Argentina  
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Fil: Govezensky, T.. Universidad Nacional Autónoma de México; México  
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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  
dc.description.fil
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  
dc.description.fil
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  
dc.description.fil
Fil: Barrio, R. A.. Universidad Nacional Autónoma de México; México  
dc.journal.title
Scientific Reports  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1038/s41598-022-16147-w