A multiscale network-based model of contagion dynamics: Heterogeneity, spatial distancing and vaccination

Autores
Aguiar, Maíra; Dosi, Giovanni; Knopoff, Damián Alejandro; Virgillito, Maria Enrica
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Lockdown and vaccination policies have been the major concern in the last year in order to contain the SARS-CoV-2 infection during the COVID-19 pandemic. In this paper, we present a model able to evaluate alternative lockdown policies and vaccination strategies. Our approach integrates and refines the multiscale model proposed by Bellomo et al., 2020, analyzing alternative network structures and bridging two perspectives to study complexity of living systems. Inside different matrices of contacts we explore the impact of closures of distinct nodes upon the overall contagion dynamics. Social distancing is shown to be more effective when targeting the reduction of contacts among and inside the most vulnerable nodes, namely hospitals/nursing homes. Moreover, our results suggest that school closures alone would not significantly affect the infection dynamics and the number of deaths in the population. Finally, we investigate a scenario with immunization in order to understand the effectiveness of targeted vaccination policies towards the most vulnerable individuals. Our model agrees with the current proposed vaccination strategy prioritizing the most vulnerable segment of the population to reduce severe cases and deaths.
Fil: Aguiar, Maíra. Università Di Trento; Italia. Basque Center For Applied Mathematics (bcam); España. Ikerbasque, Basque Foundation For Science; España
Fil: Dosi, Giovanni. Sant'anna Scuola Universitaria Superiore Pisa; Italia
Fil: Knopoff, Damián Alejandro. Universidad Nacional de Córdoba; Argentina. Basque Center For Applied Mathematics; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina
Fil: Virgillito, Maria Enrica. Sant'anna Scuola Universitaria Superiore Pisa; Italia
Materia
ACTIVE PARTICLES
COVID-19
EPIDEMIOLOGICAL MODELS
HEALTH POLICIES
KINETIC THEORY
NETWORKS
PANDEMIC
SPATIAL PATTERNS
VACCINATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/172725
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A multiscale network-based model of contagion dynamics: Heterogeneity, spatial distancing and vaccinationAguiar, MaíraDosi, GiovanniKnopoff, Damián AlejandroVirgillito, Maria EnricaACTIVE PARTICLESCOVID-19EPIDEMIOLOGICAL MODELSHEALTH POLICIESKINETIC THEORYNETWORKSPANDEMICSPATIAL PATTERNSVACCINATIONhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Lockdown and vaccination policies have been the major concern in the last year in order to contain the SARS-CoV-2 infection during the COVID-19 pandemic. In this paper, we present a model able to evaluate alternative lockdown policies and vaccination strategies. Our approach integrates and refines the multiscale model proposed by Bellomo et al., 2020, analyzing alternative network structures and bridging two perspectives to study complexity of living systems. Inside different matrices of contacts we explore the impact of closures of distinct nodes upon the overall contagion dynamics. Social distancing is shown to be more effective when targeting the reduction of contacts among and inside the most vulnerable nodes, namely hospitals/nursing homes. Moreover, our results suggest that school closures alone would not significantly affect the infection dynamics and the number of deaths in the population. Finally, we investigate a scenario with immunization in order to understand the effectiveness of targeted vaccination policies towards the most vulnerable individuals. Our model agrees with the current proposed vaccination strategy prioritizing the most vulnerable segment of the population to reduce severe cases and deaths.Fil: Aguiar, Maíra. Università Di Trento; Italia. Basque Center For Applied Mathematics (bcam); España. Ikerbasque, Basque Foundation For Science; EspañaFil: Dosi, Giovanni. Sant'anna Scuola Universitaria Superiore Pisa; ItaliaFil: Knopoff, Damián Alejandro. Universidad Nacional de Córdoba; Argentina. Basque Center For Applied Mathematics; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; ArgentinaFil: Virgillito, Maria Enrica. Sant'anna Scuola Universitaria Superiore Pisa; ItaliaWorld Scientific2021-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/172725Aguiar, Maíra; Dosi, Giovanni; Knopoff, Damián Alejandro; Virgillito, Maria Enrica; A multiscale network-based model of contagion dynamics: Heterogeneity, spatial distancing and vaccination; World Scientific; Mathematical Models And Methods In Applied Sciences; 31; 12; 11-2021; 2425-24540218-2025CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1142/S0218202521500524info:eu-repo/semantics/altIdentifier/url/https://www.worldscientific.com/doi/abs/10.1142/S0218202521500524info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:40:37Zoai:ri.conicet.gov.ar:11336/172725instacron: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:34982025-10-15 15:40:37.885CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A multiscale network-based model of contagion dynamics: Heterogeneity, spatial distancing and vaccination
title A multiscale network-based model of contagion dynamics: Heterogeneity, spatial distancing and vaccination
spellingShingle A multiscale network-based model of contagion dynamics: Heterogeneity, spatial distancing and vaccination
Aguiar, Maíra
ACTIVE PARTICLES
COVID-19
EPIDEMIOLOGICAL MODELS
HEALTH POLICIES
KINETIC THEORY
NETWORKS
PANDEMIC
SPATIAL PATTERNS
VACCINATION
title_short A multiscale network-based model of contagion dynamics: Heterogeneity, spatial distancing and vaccination
title_full A multiscale network-based model of contagion dynamics: Heterogeneity, spatial distancing and vaccination
title_fullStr A multiscale network-based model of contagion dynamics: Heterogeneity, spatial distancing and vaccination
title_full_unstemmed A multiscale network-based model of contagion dynamics: Heterogeneity, spatial distancing and vaccination
title_sort A multiscale network-based model of contagion dynamics: Heterogeneity, spatial distancing and vaccination
dc.creator.none.fl_str_mv Aguiar, Maíra
Dosi, Giovanni
Knopoff, Damián Alejandro
Virgillito, Maria Enrica
author Aguiar, Maíra
author_facet Aguiar, Maíra
Dosi, Giovanni
Knopoff, Damián Alejandro
Virgillito, Maria Enrica
author_role author
author2 Dosi, Giovanni
Knopoff, Damián Alejandro
Virgillito, Maria Enrica
author2_role author
author
author
dc.subject.none.fl_str_mv ACTIVE PARTICLES
COVID-19
EPIDEMIOLOGICAL MODELS
HEALTH POLICIES
KINETIC THEORY
NETWORKS
PANDEMIC
SPATIAL PATTERNS
VACCINATION
topic ACTIVE PARTICLES
COVID-19
EPIDEMIOLOGICAL MODELS
HEALTH POLICIES
KINETIC THEORY
NETWORKS
PANDEMIC
SPATIAL PATTERNS
VACCINATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Lockdown and vaccination policies have been the major concern in the last year in order to contain the SARS-CoV-2 infection during the COVID-19 pandemic. In this paper, we present a model able to evaluate alternative lockdown policies and vaccination strategies. Our approach integrates and refines the multiscale model proposed by Bellomo et al., 2020, analyzing alternative network structures and bridging two perspectives to study complexity of living systems. Inside different matrices of contacts we explore the impact of closures of distinct nodes upon the overall contagion dynamics. Social distancing is shown to be more effective when targeting the reduction of contacts among and inside the most vulnerable nodes, namely hospitals/nursing homes. Moreover, our results suggest that school closures alone would not significantly affect the infection dynamics and the number of deaths in the population. Finally, we investigate a scenario with immunization in order to understand the effectiveness of targeted vaccination policies towards the most vulnerable individuals. Our model agrees with the current proposed vaccination strategy prioritizing the most vulnerable segment of the population to reduce severe cases and deaths.
Fil: Aguiar, Maíra. Università Di Trento; Italia. Basque Center For Applied Mathematics (bcam); España. Ikerbasque, Basque Foundation For Science; España
Fil: Dosi, Giovanni. Sant'anna Scuola Universitaria Superiore Pisa; Italia
Fil: Knopoff, Damián Alejandro. Universidad Nacional de Córdoba; Argentina. Basque Center For Applied Mathematics; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina
Fil: Virgillito, Maria Enrica. Sant'anna Scuola Universitaria Superiore Pisa; Italia
description Lockdown and vaccination policies have been the major concern in the last year in order to contain the SARS-CoV-2 infection during the COVID-19 pandemic. In this paper, we present a model able to evaluate alternative lockdown policies and vaccination strategies. Our approach integrates and refines the multiscale model proposed by Bellomo et al., 2020, analyzing alternative network structures and bridging two perspectives to study complexity of living systems. Inside different matrices of contacts we explore the impact of closures of distinct nodes upon the overall contagion dynamics. Social distancing is shown to be more effective when targeting the reduction of contacts among and inside the most vulnerable nodes, namely hospitals/nursing homes. Moreover, our results suggest that school closures alone would not significantly affect the infection dynamics and the number of deaths in the population. Finally, we investigate a scenario with immunization in order to understand the effectiveness of targeted vaccination policies towards the most vulnerable individuals. Our model agrees with the current proposed vaccination strategy prioritizing the most vulnerable segment of the population to reduce severe cases and deaths.
publishDate 2021
dc.date.none.fl_str_mv 2021-11
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/11336/172725
Aguiar, Maíra; Dosi, Giovanni; Knopoff, Damián Alejandro; Virgillito, Maria Enrica; A multiscale network-based model of contagion dynamics: Heterogeneity, spatial distancing and vaccination; World Scientific; Mathematical Models And Methods In Applied Sciences; 31; 12; 11-2021; 2425-2454
0218-2025
CONICET Digital
CONICET
url http://hdl.handle.net/11336/172725
identifier_str_mv Aguiar, Maíra; Dosi, Giovanni; Knopoff, Damián Alejandro; Virgillito, Maria Enrica; A multiscale network-based model of contagion dynamics: Heterogeneity, spatial distancing and vaccination; World Scientific; Mathematical Models And Methods In Applied Sciences; 31; 12; 11-2021; 2425-2454
0218-2025
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1142/S0218202521500524
info:eu-repo/semantics/altIdentifier/url/https://www.worldscientific.com/doi/abs/10.1142/S0218202521500524
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv World Scientific
publisher.none.fl_str_mv World Scientific
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
_version_ 1846083519676153856
score 13.22299

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