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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/159641
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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 Técnicas2025-09-29T10:35:43Zoai: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:34982025-09-29 10:35:43.721CONICET 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 |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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1844614376401666048 |
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13.070432 |