Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics

Autores
Aguiar, Maíra; Van Dierdonck, Joseba Bidaurrazaga; Mar, Javier; Cusimano, Nicole; Knopoff, Damián Alejandro; Anam, Vizda; Stollenwerk, Nico
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
As the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. The momentary reproduction ratio r(t) of an epidemic is used as a public health guiding tool to evaluate the course of the epidemic, with the evolution of r(t) being the reasoning behind tightening and relaxing control measures over time. Here we investigate critical fluctuations around the epidemiological threshold, resembling new waves, even when the community disease transmission rate β is not significantly changing. Without loss of generality, we use simple models that can be treated analytically and results are applied to more complex models describing COVID-19 epidemics. Our analysis shows that, rather than the supercritical regime (infectivity larger than a critical value, β> βc) leading to new exponential growth of infection, the subcritical regime (infectivity smaller than a critical value, β< βc) with small import is able to explain the dynamic behaviour of COVID-19 spreading after a lockdown lifting, with r(t) ≈ 1 hovering around its threshold value.
Fil: Aguiar, Maíra. Basque Center for Applied Mathematics; España. Ikerbasque; España. Universita degli Studi di Trento; Italia
Fil: Van Dierdonck, Joseba Bidaurrazaga. Basque Health Department; España
Fil: Mar, Javier. Debagoiena Integrated Healthcare Organisation; España. Biodonostia Health Research Institute; España. Kronikgune Institute for Health Services Research; España
Fil: Cusimano, Nicole. Basque Center for Applied Mathematics; España
Fil: Knopoff, Damián Alejandro. Basque Center for Applied Mathematics; España. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Anam, Vizda. Basque Center for Applied Mathematics; España
Fil: Stollenwerk, Nico. Basque Center for Applied Mathematics; España. Universita degli Studi di Trento; Italia
Materia
EPIDEMIOLOGICAL MODELS
COVID-19
CRITICAL FLUCTUATIONS
STOCHASTIC MODELS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/159641
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamicsAguiar, MaíraVan Dierdonck, Joseba BidaurrazagaMar, JavierCusimano, NicoleKnopoff, Damián AlejandroAnam, VizdaStollenwerk, NicoEPIDEMIOLOGICAL MODELSCOVID-19CRITICAL FLUCTUATIONSSTOCHASTIC MODELShttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1https://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3As the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. The momentary reproduction ratio r(t) of an epidemic is used as a public health guiding tool to evaluate the course of the epidemic, with the evolution of r(t) being the reasoning behind tightening and relaxing control measures over time. Here we investigate critical fluctuations around the epidemiological threshold, resembling new waves, even when the community disease transmission rate β is not significantly changing. Without loss of generality, we use simple models that can be treated analytically and results are applied to more complex models describing COVID-19 epidemics. Our analysis shows that, rather than the supercritical regime (infectivity larger than a critical value, β> βc) leading to new exponential growth of infection, the subcritical regime (infectivity smaller than a critical value, β< βc) with small import is able to explain the dynamic behaviour of COVID-19 spreading after a lockdown lifting, with r(t) ≈ 1 hovering around its threshold value.Fil: Aguiar, Maíra. Basque Center for Applied Mathematics; España. Ikerbasque; España. Universita degli Studi di Trento; ItaliaFil: Van Dierdonck, Joseba Bidaurrazaga. Basque Health Department; EspañaFil: Mar, Javier. Debagoiena Integrated Healthcare Organisation; España. Biodonostia Health Research Institute; España. Kronikgune Institute for Health Services Research; EspañaFil: Cusimano, Nicole. Basque Center for Applied Mathematics; EspañaFil: Knopoff, Damián Alejandro. Basque Center for Applied Mathematics; España. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Anam, Vizda. Basque Center for Applied Mathematics; EspañaFil: Stollenwerk, Nico. Basque Center for Applied Mathematics; España. Universita degli Studi di Trento; ItaliaNature Research2021-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/159641Aguiar, Maíra; Van Dierdonck, Joseba Bidaurrazaga; Mar, Javier; Cusimano, Nicole; Knopoff, Damián Alejandro; et al.; Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics; Nature Research; Scientific Reports; 11; 13839; 12-2021; 1-122045-2322CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-021-93366-7info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41598-021-93366-7info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:33:18Zoai:ri.conicet.gov.ar:11336/159641instacron: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 15:33:18.759CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics
title Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics
spellingShingle Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics
Aguiar, Maíra
EPIDEMIOLOGICAL MODELS
COVID-19
CRITICAL FLUCTUATIONS
STOCHASTIC MODELS
title_short Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics
title_full Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics
title_fullStr Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics
title_full_unstemmed Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics
title_sort Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics
dc.creator.none.fl_str_mv Aguiar, Maíra
Van Dierdonck, Joseba Bidaurrazaga
Mar, Javier
Cusimano, Nicole
Knopoff, Damián Alejandro
Anam, Vizda
Stollenwerk, Nico
author Aguiar, Maíra
author_facet Aguiar, Maíra
Van Dierdonck, Joseba Bidaurrazaga
Mar, Javier
Cusimano, Nicole
Knopoff, Damián Alejandro
Anam, Vizda
Stollenwerk, Nico
author_role author
author2 Van Dierdonck, Joseba Bidaurrazaga
Mar, Javier
Cusimano, Nicole
Knopoff, Damián Alejandro
Anam, Vizda
Stollenwerk, Nico
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv EPIDEMIOLOGICAL MODELS
COVID-19
CRITICAL FLUCTUATIONS
STOCHASTIC MODELS
topic EPIDEMIOLOGICAL MODELS
COVID-19
CRITICAL FLUCTUATIONS
STOCHASTIC MODELS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/3.3
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv As the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. The momentary reproduction ratio r(t) of an epidemic is used as a public health guiding tool to evaluate the course of the epidemic, with the evolution of r(t) being the reasoning behind tightening and relaxing control measures over time. Here we investigate critical fluctuations around the epidemiological threshold, resembling new waves, even when the community disease transmission rate β is not significantly changing. Without loss of generality, we use simple models that can be treated analytically and results are applied to more complex models describing COVID-19 epidemics. Our analysis shows that, rather than the supercritical regime (infectivity larger than a critical value, β> βc) leading to new exponential growth of infection, the subcritical regime (infectivity smaller than a critical value, β< βc) with small import is able to explain the dynamic behaviour of COVID-19 spreading after a lockdown lifting, with r(t) ≈ 1 hovering around its threshold value.
Fil: Aguiar, Maíra. Basque Center for Applied Mathematics; España. Ikerbasque; España. Universita degli Studi di Trento; Italia
Fil: Van Dierdonck, Joseba Bidaurrazaga. Basque Health Department; España
Fil: Mar, Javier. Debagoiena Integrated Healthcare Organisation; España. Biodonostia Health Research Institute; España. Kronikgune Institute for Health Services Research; España
Fil: Cusimano, Nicole. Basque Center for Applied Mathematics; España
Fil: Knopoff, Damián Alejandro. Basque Center for Applied Mathematics; España. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Anam, Vizda. Basque Center for Applied Mathematics; España
Fil: Stollenwerk, Nico. Basque Center for Applied Mathematics; España. Universita degli Studi di Trento; Italia
description As the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. The momentary reproduction ratio r(t) of an epidemic is used as a public health guiding tool to evaluate the course of the epidemic, with the evolution of r(t) being the reasoning behind tightening and relaxing control measures over time. Here we investigate critical fluctuations around the epidemiological threshold, resembling new waves, even when the community disease transmission rate β is not significantly changing. Without loss of generality, we use simple models that can be treated analytically and results are applied to more complex models describing COVID-19 epidemics. Our analysis shows that, rather than the supercritical regime (infectivity larger than a critical value, β> βc) leading to new exponential growth of infection, the subcritical regime (infectivity smaller than a critical value, β< βc) with small import is able to explain the dynamic behaviour of COVID-19 spreading after a lockdown lifting, with r(t) ≈ 1 hovering around its threshold value.
publishDate 2021
dc.date.none.fl_str_mv 2021-12
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/11336/159641
Aguiar, Maíra; Van Dierdonck, Joseba Bidaurrazaga; Mar, Javier; Cusimano, Nicole; Knopoff, Damián Alejandro; et al.; Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics; Nature Research; Scientific Reports; 11; 13839; 12-2021; 1-12
2045-2322
CONICET Digital
CONICET
url http://hdl.handle.net/11336/159641
identifier_str_mv Aguiar, Maíra; Van Dierdonck, Joseba Bidaurrazaga; Mar, Javier; Cusimano, Nicole; Knopoff, Damián Alejandro; et al.; Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics; Nature Research; Scientific Reports; 11; 13839; 12-2021; 1-12
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/s41598-021-93366-7
info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41598-021-93366-7
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Nature Research
publisher.none.fl_str_mv Nature Research
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_ 1846083463487160320
score 13.22299

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