Discrete-Time Modeling of COVID-19 Propagation in Argentina with Explicit Delays

Autores
Bergonzi, Mariana; Pecker Marcosig, Ezequiel; Kofman, Ernesto Javier; Castro, Rodrigo Daniel
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We present a new deterministic discrete-Time compartmental model of COVID-19 that explicitly takes into account relevant delays related to the stages of the disease, its diagnosis and report system, allowing to represent the presence of imported cases. In addition to developing the model equations, we describe an automatic parameter fitting mechanism using official data on the spread of the virus in Argentina. The result consistently reflects the behavior of the disease with respect to characteristic times: latency, infectious period, report of cases (confirmed and dead), and allows for detecting automatically changes in the reproductive number and in the mortality factor. We also analyse the model´s prediction capability and present simulation results assuming different future scenarios. We discuss usage of the model in a closed-loop control scheme, where the explicit presence of delays plays a key role in projecting more realistic dynamics than that of classic continuous-Time models.
Fil: Bergonzi, Mariana. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Pecker Marcosig, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
Fil: Kofman, Ernesto Javier. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Castro, Rodrigo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
Materia
MATHEMATICAL MODEL
DISCRETE-TIME SYSTEMS
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:11336/135185
Acceder
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spelling Discrete-Time Modeling of COVID-19 Propagation in Argentina with Explicit DelaysBergonzi, MarianaPecker Marcosig, EzequielKofman, Ernesto JavierCastro, Rodrigo DanielMATHEMATICAL MODELDISCRETE-TIME SYSTEMSCOVID-19https://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1We present a new deterministic discrete-Time compartmental model of COVID-19 that explicitly takes into account relevant delays related to the stages of the disease, its diagnosis and report system, allowing to represent the presence of imported cases. In addition to developing the model equations, we describe an automatic parameter fitting mechanism using official data on the spread of the virus in Argentina. The result consistently reflects the behavior of the disease with respect to characteristic times: latency, infectious period, report of cases (confirmed and dead), and allows for detecting automatically changes in the reproductive number and in the mortality factor. We also analyse the model´s prediction capability and present simulation results assuming different future scenarios. We discuss usage of the model in a closed-loop control scheme, where the explicit presence of delays plays a key role in projecting more realistic dynamics than that of classic continuous-Time models.Fil: Bergonzi, Mariana. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Pecker Marcosig, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Kofman, Ernesto Javier. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Castro, Rodrigo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaIEEE Computer Society2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/135185Bergonzi, Mariana; Pecker Marcosig, Ezequiel; Kofman, Ernesto Javier; Castro, Rodrigo Daniel; Discrete-Time Modeling of COVID-19 Propagation in Argentina with Explicit Delays; IEEE Computer Society; Computing In Science & Engineering; 23; 1; 1-1-2021; 35-451521-9615CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/9272834info:eu-repo/semantics/altIdentifier/doi/10.1109/MCSE.2020.3040700info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-06-10T10:04:09Zoai:ri.conicet.gov.ar:11336/135185instacron: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:34982026-06-10 10:04:10.16CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Discrete-Time Modeling of COVID-19 Propagation in Argentina with Explicit Delays
title Discrete-Time Modeling of COVID-19 Propagation in Argentina with Explicit Delays
spellingShingle Discrete-Time Modeling of COVID-19 Propagation in Argentina with Explicit Delays
Bergonzi, Mariana
MATHEMATICAL MODEL
DISCRETE-TIME SYSTEMS
COVID-19
title_short Discrete-Time Modeling of COVID-19 Propagation in Argentina with Explicit Delays
title_full Discrete-Time Modeling of COVID-19 Propagation in Argentina with Explicit Delays
title_fullStr Discrete-Time Modeling of COVID-19 Propagation in Argentina with Explicit Delays
title_full_unstemmed Discrete-Time Modeling of COVID-19 Propagation in Argentina with Explicit Delays
title_sort Discrete-Time Modeling of COVID-19 Propagation in Argentina with Explicit Delays
dc.creator.none.fl_str_mv Bergonzi, Mariana
Pecker Marcosig, Ezequiel
Kofman, Ernesto Javier
Castro, Rodrigo Daniel
author Bergonzi, Mariana
author_facet Bergonzi, Mariana
Pecker Marcosig, Ezequiel
Kofman, Ernesto Javier
Castro, Rodrigo Daniel
author_role author
author2 Pecker Marcosig, Ezequiel
Kofman, Ernesto Javier
Castro, Rodrigo Daniel
author2_role author
author
author
dc.subject.none.fl_str_mv MATHEMATICAL MODEL
DISCRETE-TIME SYSTEMS
COVID-19
topic MATHEMATICAL MODEL
DISCRETE-TIME SYSTEMS
COVID-19
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We present a new deterministic discrete-Time compartmental model of COVID-19 that explicitly takes into account relevant delays related to the stages of the disease, its diagnosis and report system, allowing to represent the presence of imported cases. In addition to developing the model equations, we describe an automatic parameter fitting mechanism using official data on the spread of the virus in Argentina. The result consistently reflects the behavior of the disease with respect to characteristic times: latency, infectious period, report of cases (confirmed and dead), and allows for detecting automatically changes in the reproductive number and in the mortality factor. We also analyse the model´s prediction capability and present simulation results assuming different future scenarios. We discuss usage of the model in a closed-loop control scheme, where the explicit presence of delays plays a key role in projecting more realistic dynamics than that of classic continuous-Time models.
Fil: Bergonzi, Mariana. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Pecker Marcosig, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
Fil: Kofman, Ernesto Javier. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Castro, Rodrigo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
description We present a new deterministic discrete-Time compartmental model of COVID-19 that explicitly takes into account relevant delays related to the stages of the disease, its diagnosis and report system, allowing to represent the presence of imported cases. In addition to developing the model equations, we describe an automatic parameter fitting mechanism using official data on the spread of the virus in Argentina. The result consistently reflects the behavior of the disease with respect to characteristic times: latency, infectious period, report of cases (confirmed and dead), and allows for detecting automatically changes in the reproductive number and in the mortality factor. We also analyse the model´s prediction capability and present simulation results assuming different future scenarios. We discuss usage of the model in a closed-loop control scheme, where the explicit presence of delays plays a key role in projecting more realistic dynamics than that of classic continuous-Time models.
publishDate 2021
dc.date.none.fl_str_mv 2021-01-01
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/11336/135185
Bergonzi, Mariana; Pecker Marcosig, Ezequiel; Kofman, Ernesto Javier; Castro, Rodrigo Daniel; Discrete-Time Modeling of COVID-19 Propagation in Argentina with Explicit Delays; IEEE Computer Society; Computing In Science & Engineering; 23; 1; 1-1-2021; 35-45
1521-9615
CONICET Digital
CONICET
url http://hdl.handle.net/11336/135185
identifier_str_mv Bergonzi, Mariana; Pecker Marcosig, Ezequiel; Kofman, Ernesto Javier; Castro, Rodrigo Daniel; Discrete-Time Modeling of COVID-19 Propagation in Argentina with Explicit Delays; IEEE Computer Society; Computing In Science & Engineering; 23; 1; 1-1-2021; 35-45
1521-9615
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://ieeexplore.ieee.org/document/9272834
info:eu-repo/semantics/altIdentifier/doi/10.1109/MCSE.2020.3040700
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
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv IEEE Computer Society
publisher.none.fl_str_mv IEEE Computer Society
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 12.98848

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