Lessons from being challenged by COVID-19

Autores
Tagliazucchi, Enzo Rodolfo; Balenzuela, Pablo; Travizano, M.; Mindlin, Bernardo Gabriel; Mininni, Pablo Daniel
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We present results of different approaches to model the evolution of the COVID-19 epidemic in Argentina, with a special focus onthe megacity conformed by the city of Buenos Aires and its metropolitan area, including a total of 41 districts with over 13 millioninhabitants. We first highlight the relevance of interpreting the early stage of the epidemic in light of incoming infectious travelersfrom abroad. Next, we critically evaluate certain proposed solutions to contain the epidemic based on instantaneous modificationsof the reproductive number. Finally, we build increasingly complex and realistic models, ranging from simple homogeneous modelsused to estimate local reproduction numbers, to fully coupled inhomogeneous (deterministic or stochastic) models incorporatingmobility estimates from cell phone location data. The models are capable of producing forecasts highly consistent with the officialnumber of cases with minimal parameter fitting and fine-tuning.  We discuss the strengths and limitations of the proposed models,focusing on the validity of different necessary first approximations, and caution future modeling efforts to exercise great care in theinterpretation of long-term forecasts, and in the adoption of non-pharmaceutical interventions backed by numerical simulations.
Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Balenzuela, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Travizano, M.. Grandata Labs; Estados Unidos
Fil: Mindlin, Bernardo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Mininni, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Materia
COVID-19
EPIDEMIOLOGY
DYNAMICS
PANDEMIA
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:11336/109068
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Lessons from being challenged by COVID-19Tagliazucchi, Enzo RodolfoBalenzuela, PabloTravizano, M.Mindlin, Bernardo GabrielMininni, Pablo DanielCOVID-19EPIDEMIOLOGYDYNAMICSPANDEMIAhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1We present results of different approaches to model the evolution of the COVID-19 epidemic in Argentina, with a special focus onthe megacity conformed by the city of Buenos Aires and its metropolitan area, including a total of 41 districts with over 13 millioninhabitants. We first highlight the relevance of interpreting the early stage of the epidemic in light of incoming infectious travelersfrom abroad. Next, we critically evaluate certain proposed solutions to contain the epidemic based on instantaneous modificationsof the reproductive number. Finally, we build increasingly complex and realistic models, ranging from simple homogeneous modelsused to estimate local reproduction numbers, to fully coupled inhomogeneous (deterministic or stochastic) models incorporatingmobility estimates from cell phone location data. The models are capable of producing forecasts highly consistent with the officialnumber of cases with minimal parameter fitting and fine-tuning.  We discuss the strengths and limitations of the proposed models,focusing on the validity of different necessary first approximations, and caution future modeling efforts to exercise great care in theinterpretation of long-term forecasts, and in the adoption of non-pharmaceutical interventions backed by numerical simulations.Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Balenzuela, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Travizano, M.. Grandata Labs; Estados UnidosFil: Mindlin, Bernardo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Mininni, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaPergamon-Elsevier Science Ltd2020-08info: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/11336/109068Tagliazucchi, Enzo Rodolfo; Balenzuela, Pablo; Travizano, M.; Mindlin, Bernardo Gabriel; Mininni, Pablo Daniel; Lessons from being challenged by COVID-19; Pergamon-Elsevier Science Ltd; Chaos, Solitons And Fractals; 137; 8-2020; 1-15; 1099230960-0779CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.chaos.2020.109923info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0960077920303180info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7245296/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:10:58Zoai:ri.conicet.gov.ar:11336/109068instacron: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:34982025-10-22 12:10:58.627CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Lessons from being challenged by COVID-19
title Lessons from being challenged by COVID-19
spellingShingle Lessons from being challenged by COVID-19
Tagliazucchi, Enzo Rodolfo
COVID-19
EPIDEMIOLOGY
DYNAMICS
PANDEMIA
title_short Lessons from being challenged by COVID-19
title_full Lessons from being challenged by COVID-19
title_fullStr Lessons from being challenged by COVID-19
title_full_unstemmed Lessons from being challenged by COVID-19
title_sort Lessons from being challenged by COVID-19
dc.creator.none.fl_str_mv Tagliazucchi, Enzo Rodolfo
Balenzuela, Pablo
Travizano, M.
Mindlin, Bernardo Gabriel
Mininni, Pablo Daniel
author Tagliazucchi, Enzo Rodolfo
author_facet Tagliazucchi, Enzo Rodolfo
Balenzuela, Pablo
Travizano, M.
Mindlin, Bernardo Gabriel
Mininni, Pablo Daniel
author_role author
author2 Balenzuela, Pablo
Travizano, M.
Mindlin, Bernardo Gabriel
Mininni, Pablo Daniel
author2_role author
author
author
author
dc.subject.none.fl_str_mv COVID-19
EPIDEMIOLOGY
DYNAMICS
PANDEMIA
topic COVID-19
EPIDEMIOLOGY
DYNAMICS
PANDEMIA
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We present results of different approaches to model the evolution of the COVID-19 epidemic in Argentina, with a special focus onthe megacity conformed by the city of Buenos Aires and its metropolitan area, including a total of 41 districts with over 13 millioninhabitants. We first highlight the relevance of interpreting the early stage of the epidemic in light of incoming infectious travelersfrom abroad. Next, we critically evaluate certain proposed solutions to contain the epidemic based on instantaneous modificationsof the reproductive number. Finally, we build increasingly complex and realistic models, ranging from simple homogeneous modelsused to estimate local reproduction numbers, to fully coupled inhomogeneous (deterministic or stochastic) models incorporatingmobility estimates from cell phone location data. The models are capable of producing forecasts highly consistent with the officialnumber of cases with minimal parameter fitting and fine-tuning.  We discuss the strengths and limitations of the proposed models,focusing on the validity of different necessary first approximations, and caution future modeling efforts to exercise great care in theinterpretation of long-term forecasts, and in the adoption of non-pharmaceutical interventions backed by numerical simulations.
Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Balenzuela, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Travizano, M.. Grandata Labs; Estados Unidos
Fil: Mindlin, Bernardo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Mininni, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
description We present results of different approaches to model the evolution of the COVID-19 epidemic in Argentina, with a special focus onthe megacity conformed by the city of Buenos Aires and its metropolitan area, including a total of 41 districts with over 13 millioninhabitants. We first highlight the relevance of interpreting the early stage of the epidemic in light of incoming infectious travelersfrom abroad. Next, we critically evaluate certain proposed solutions to contain the epidemic based on instantaneous modificationsof the reproductive number. Finally, we build increasingly complex and realistic models, ranging from simple homogeneous modelsused to estimate local reproduction numbers, to fully coupled inhomogeneous (deterministic or stochastic) models incorporatingmobility estimates from cell phone location data. The models are capable of producing forecasts highly consistent with the officialnumber of cases with minimal parameter fitting and fine-tuning.  We discuss the strengths and limitations of the proposed models,focusing on the validity of different necessary first approximations, and caution future modeling efforts to exercise great care in theinterpretation of long-term forecasts, and in the adoption of non-pharmaceutical interventions backed by numerical simulations.
publishDate 2020
dc.date.none.fl_str_mv 2020-08
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/11336/109068
Tagliazucchi, Enzo Rodolfo; Balenzuela, Pablo; Travizano, M.; Mindlin, Bernardo Gabriel; Mininni, Pablo Daniel; Lessons from being challenged by COVID-19; Pergamon-Elsevier Science Ltd; Chaos, Solitons And Fractals; 137; 8-2020; 1-15; 109923
0960-0779
CONICET Digital
CONICET
url http://hdl.handle.net/11336/109068
identifier_str_mv Tagliazucchi, Enzo Rodolfo; Balenzuela, Pablo; Travizano, M.; Mindlin, Bernardo Gabriel; Mininni, Pablo Daniel; Lessons from being challenged by COVID-19; Pergamon-Elsevier Science Ltd; Chaos, Solitons And Fractals; 137; 8-2020; 1-15; 109923
0960-0779
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chaos.2020.109923
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0960077920303180
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7245296/
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 Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
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 12.982451

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