Model based on COVID-19 evidence to predict and improve pandemic control

Autores
González, Rafael I.; Moya, Pablo S.; Bringa, Eduardo Marcial; Bacigalupe, Gonzalo; Ramírez Santana, Muriel; Kiwi, Miguel
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Based on the extensive data accumulated during the COVID-19 pandemic, we put forward simple to implement indicators, that should alert authorities and provide early warnings of an impending sanitary crisis. In fact, Testing, Tracing, and Isolation (TTI) in conjunction with disciplined social distancing and vaccination were expected to achieve negligible COVID-19 contagion levels; however, they proved to be insufficient, and their implementation has led to controversial social, economic and ethical challenges. This paper focuses on the development of simple indicators, based on the experience gained by COVID-19 data, which provide a sort of yellow light as to when an epidemic might expand, despite some short term decrements. We show that if case growth is not stopped during the 7 to 14 days after onset, the growth risk increases considerably, and warrants immediate attention. Our model examines not only the COVID contagion propagation speed, but also how it accelerates as a function of time. We identify trends that emerge under the various policies that were applied, as well as their differences among countries. The data for all countries was obtained from ourworldindata.org. Our main conclusion is that if the reduction spread is lost during one, or at most two weeks, urgent measures should be implemented to avoid scenarios in which the epidemic gains strong impetus.
Fil: González, Rafael I.. Universidad Mayor; Chile
Fil: Moya, Pablo S.. Universidad de Chile; Chile
Fil: Bringa, Eduardo Marcial. Universidad de Mendoza. Facultad de Ingenieria; Argentina. Universidad Mayor; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Bacigalupe, Gonzalo. Massachusetts Institute of Technology; Estados Unidos
Fil: Ramírez Santana, Muriel. Universidad Católica del Norte; Chile
Fil: Kiwi, Miguel. Universidad de Chile; Chile
Materia
PANDEMIC CONTROL
MODEL
COVID-19
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:11336/240158
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Model based on COVID-19 evidence to predict and improve pandemic controlGonzález, Rafael I.Moya, Pablo S.Bringa, Eduardo MarcialBacigalupe, GonzaloRamírez Santana, MurielKiwi, MiguelPANDEMIC CONTROLMODELCOVID-19https://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3Based on the extensive data accumulated during the COVID-19 pandemic, we put forward simple to implement indicators, that should alert authorities and provide early warnings of an impending sanitary crisis. In fact, Testing, Tracing, and Isolation (TTI) in conjunction with disciplined social distancing and vaccination were expected to achieve negligible COVID-19 contagion levels; however, they proved to be insufficient, and their implementation has led to controversial social, economic and ethical challenges. This paper focuses on the development of simple indicators, based on the experience gained by COVID-19 data, which provide a sort of yellow light as to when an epidemic might expand, despite some short term decrements. We show that if case growth is not stopped during the 7 to 14 days after onset, the growth risk increases considerably, and warrants immediate attention. Our model examines not only the COVID contagion propagation speed, but also how it accelerates as a function of time. We identify trends that emerge under the various policies that were applied, as well as their differences among countries. The data for all countries was obtained from ourworldindata.org. Our main conclusion is that if the reduction spread is lost during one, or at most two weeks, urgent measures should be implemented to avoid scenarios in which the epidemic gains strong impetus.Fil: González, Rafael I.. Universidad Mayor; ChileFil: Moya, Pablo S.. Universidad de Chile; ChileFil: Bringa, Eduardo Marcial. Universidad de Mendoza. Facultad de Ingenieria; Argentina. Universidad Mayor; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Bacigalupe, Gonzalo. Massachusetts Institute of Technology; Estados UnidosFil: Ramírez Santana, Muriel. Universidad Católica del Norte; ChileFil: Kiwi, Miguel. Universidad de Chile; ChilePublic Library of Science2023-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/240158González, Rafael I.; Moya, Pablo S.; Bringa, Eduardo Marcial; Bacigalupe, Gonzalo; Ramírez Santana, Muriel; et al.; Model based on COVID-19 evidence to predict and improve pandemic control; Public Library of Science; Plos One; 18; 6; 6-2023; 1-161932-6203CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://dx.plos.org/10.1371/journal.pone.0286747info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0286747info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-17T11:39:36Zoai:ri.conicet.gov.ar:11336/240158instacron: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:34982025-09-17 11:39:37.227CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Model based on COVID-19 evidence to predict and improve pandemic control
title Model based on COVID-19 evidence to predict and improve pandemic control
spellingShingle Model based on COVID-19 evidence to predict and improve pandemic control
González, Rafael I.
PANDEMIC CONTROL
MODEL
COVID-19
title_short Model based on COVID-19 evidence to predict and improve pandemic control
title_full Model based on COVID-19 evidence to predict and improve pandemic control
title_fullStr Model based on COVID-19 evidence to predict and improve pandemic control
title_full_unstemmed Model based on COVID-19 evidence to predict and improve pandemic control
title_sort Model based on COVID-19 evidence to predict and improve pandemic control
dc.creator.none.fl_str_mv González, Rafael I.
Moya, Pablo S.
Bringa, Eduardo Marcial
Bacigalupe, Gonzalo
Ramírez Santana, Muriel
Kiwi, Miguel
author González, Rafael I.
author_facet González, Rafael I.
Moya, Pablo S.
Bringa, Eduardo Marcial
Bacigalupe, Gonzalo
Ramírez Santana, Muriel
Kiwi, Miguel
author_role author
author2 Moya, Pablo S.
Bringa, Eduardo Marcial
Bacigalupe, Gonzalo
Ramírez Santana, Muriel
Kiwi, Miguel
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv PANDEMIC CONTROL
MODEL
COVID-19
topic PANDEMIC CONTROL
MODEL
COVID-19
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.3
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Based on the extensive data accumulated during the COVID-19 pandemic, we put forward simple to implement indicators, that should alert authorities and provide early warnings of an impending sanitary crisis. In fact, Testing, Tracing, and Isolation (TTI) in conjunction with disciplined social distancing and vaccination were expected to achieve negligible COVID-19 contagion levels; however, they proved to be insufficient, and their implementation has led to controversial social, economic and ethical challenges. This paper focuses on the development of simple indicators, based on the experience gained by COVID-19 data, which provide a sort of yellow light as to when an epidemic might expand, despite some short term decrements. We show that if case growth is not stopped during the 7 to 14 days after onset, the growth risk increases considerably, and warrants immediate attention. Our model examines not only the COVID contagion propagation speed, but also how it accelerates as a function of time. We identify trends that emerge under the various policies that were applied, as well as their differences among countries. The data for all countries was obtained from ourworldindata.org. Our main conclusion is that if the reduction spread is lost during one, or at most two weeks, urgent measures should be implemented to avoid scenarios in which the epidemic gains strong impetus.
Fil: González, Rafael I.. Universidad Mayor; Chile
Fil: Moya, Pablo S.. Universidad de Chile; Chile
Fil: Bringa, Eduardo Marcial. Universidad de Mendoza. Facultad de Ingenieria; Argentina. Universidad Mayor; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Bacigalupe, Gonzalo. Massachusetts Institute of Technology; Estados Unidos
Fil: Ramírez Santana, Muriel. Universidad Católica del Norte; Chile
Fil: Kiwi, Miguel. Universidad de Chile; Chile
description Based on the extensive data accumulated during the COVID-19 pandemic, we put forward simple to implement indicators, that should alert authorities and provide early warnings of an impending sanitary crisis. In fact, Testing, Tracing, and Isolation (TTI) in conjunction with disciplined social distancing and vaccination were expected to achieve negligible COVID-19 contagion levels; however, they proved to be insufficient, and their implementation has led to controversial social, economic and ethical challenges. This paper focuses on the development of simple indicators, based on the experience gained by COVID-19 data, which provide a sort of yellow light as to when an epidemic might expand, despite some short term decrements. We show that if case growth is not stopped during the 7 to 14 days after onset, the growth risk increases considerably, and warrants immediate attention. Our model examines not only the COVID contagion propagation speed, but also how it accelerates as a function of time. We identify trends that emerge under the various policies that were applied, as well as their differences among countries. The data for all countries was obtained from ourworldindata.org. Our main conclusion is that if the reduction spread is lost during one, or at most two weeks, urgent measures should be implemented to avoid scenarios in which the epidemic gains strong impetus.
publishDate 2023
dc.date.none.fl_str_mv 2023-06
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/11336/240158
González, Rafael I.; Moya, Pablo S.; Bringa, Eduardo Marcial; Bacigalupe, Gonzalo; Ramírez Santana, Muriel; et al.; Model based on COVID-19 evidence to predict and improve pandemic control; Public Library of Science; Plos One; 18; 6; 6-2023; 1-16
1932-6203
CONICET Digital
CONICET
url http://hdl.handle.net/11336/240158
identifier_str_mv González, Rafael I.; Moya, Pablo S.; Bringa, Eduardo Marcial; Bacigalupe, Gonzalo; Ramírez Santana, Muriel; et al.; Model based on COVID-19 evidence to predict and improve pandemic control; Public Library of Science; Plos One; 18; 6; 6-2023; 1-16
1932-6203
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://dx.plos.org/10.1371/journal.pone.0286747
info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0286747
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 Public Library of Science
publisher.none.fl_str_mv Public Library of Science
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 13.001348

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