New Caputo-Fabrizio fractional order SEIASqEqHR model for COVID-19 epidemic transmission with genetic algorithm based control strategy

Fractional derivative has a memory and non-localization features that make it very useful in modelling epidemics’ transition. The kernel of Caputo-Fabrizio fractional derivative has many features such as non-singularity, non-locality and an exponential form. Therefore, it is preferred for modeling d...

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Autores:
Tipo de recurso:
Article of investigation
Fecha de publicación:
2020
Institución:
Universidad de Bogotá Jorge Tadeo Lozano
Repositorio:
Expeditio: repositorio UTadeo
Idioma:
eng
OAI Identifier:
oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/13814
Acceso en línea:
https://doi.org/10.1016/j.aej.2020.08.034
http://hdl.handle.net/20.500.12010/13814
Palabra clave:
COVID-19
Fractional derivative
Caputo-Fabrizio fractional order differential operator
The existence and uniqueness
Genetic algorithm
Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
Rights
License
Abierto (Texto Completo)
id UTADEO2_81945e34a3497baa2e254a63092eb09b
oai_identifier_str oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/13814
network_acronym_str UTADEO2
network_name_str Expeditio: repositorio UTadeo
repository_id_str
dc.title.spa.fl_str_mv New Caputo-Fabrizio fractional order SEIASqEqHR model for COVID-19 epidemic transmission with genetic algorithm based control strategy
title New Caputo-Fabrizio fractional order SEIASqEqHR model for COVID-19 epidemic transmission with genetic algorithm based control strategy
spellingShingle New Caputo-Fabrizio fractional order SEIASqEqHR model for COVID-19 epidemic transmission with genetic algorithm based control strategy
COVID-19
Fractional derivative
Caputo-Fabrizio fractional order differential operator
The existence and uniqueness
Genetic algorithm
Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
title_short New Caputo-Fabrizio fractional order SEIASqEqHR model for COVID-19 epidemic transmission with genetic algorithm based control strategy
title_full New Caputo-Fabrizio fractional order SEIASqEqHR model for COVID-19 epidemic transmission with genetic algorithm based control strategy
title_fullStr New Caputo-Fabrizio fractional order SEIASqEqHR model for COVID-19 epidemic transmission with genetic algorithm based control strategy
title_full_unstemmed New Caputo-Fabrizio fractional order SEIASqEqHR model for COVID-19 epidemic transmission with genetic algorithm based control strategy
title_sort New Caputo-Fabrizio fractional order SEIASqEqHR model for COVID-19 epidemic transmission with genetic algorithm based control strategy
dc.subject.spa.fl_str_mv COVID-19
Fractional derivative
Caputo-Fabrizio fractional order differential operator
The existence and uniqueness
Genetic algorithm
topic COVID-19
Fractional derivative
Caputo-Fabrizio fractional order differential operator
The existence and uniqueness
Genetic algorithm
Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
dc.subject.lemb.spa.fl_str_mv Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
description Fractional derivative has a memory and non-localization features that make it very useful in modelling epidemics’ transition. The kernel of Caputo-Fabrizio fractional derivative has many features such as non-singularity, non-locality and an exponential form. Therefore, it is preferred for modeling disease spreading systems. In this work, we suggest to formulate COVID-19 epidemic transmission via SEIASqEqHR paradigm using the Caputo-Fabrizio fractional derivation method. In the suggested fractional order COVID-19 SEIASqEqHR paradigm, the impact of changing quarantining and contact rates are examined. The stability of the proposed fractional order COVID-19 SEIASqEqHR paradigm is studied and a parametric rule for the fundamental reproduction number formula is given. The existence and uniqueness of stable solution of the proposed fractional order COVID-19 SEIASqEqHR paradigm are proved. Since the genetic algorithm is a common powerful optimization method, we propose an optimum control strategy based on the genetic algorithm. By this strategy, the peak values of the infected population classes are to be minimized. The results show that the proposed fractional model is epidemiologically well-posed and is a proper elect.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-09-25T17:28:14Z
dc.date.available.none.fl_str_mv 2020-09-25T17:28:14Z
dc.date.created.none.fl_str_mv 2020
dc.type.local.spa.fl_str_mv Artículo
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
format http://purl.org/coar/resource_type/c_2df8fbb1
dc.identifier.issn.spa.fl_str_mv 1110-0168
dc.identifier.other.spa.fl_str_mv https://doi.org/10.1016/j.aej.2020.08.034
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12010/13814
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1016/j.aej.2020.08.034
identifier_str_mv 1110-0168
url https://doi.org/10.1016/j.aej.2020.08.034
http://hdl.handle.net/20.500.12010/13814
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.local.spa.fl_str_mv Abierto (Texto Completo)
rights_invalid_str_mv Abierto (Texto Completo)
http://purl.org/coar/access_right/c_abf2
dc.format.extent.spa.fl_str_mv 18 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Alexandria Engineering Journal
dc.source.spa.fl_str_mv reponame:Expeditio Repositorio Institucional UJTL
instname:Universidad de Bogotá Jorge Tadeo Lozano
instname_str Universidad de Bogotá Jorge Tadeo Lozano
institution Universidad de Bogotá Jorge Tadeo Lozano
reponame_str Expeditio Repositorio Institucional UJTL
collection Expeditio Repositorio Institucional UJTL
bitstream.url.fl_str_mv https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/13814/1/New-Caputo-Fabrizio-fractional-order-SEIASqEqHR-model-for-_2020_Alexandria-E.pdf
https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/13814/2/license.txt
https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/13814/3/New-Caputo-Fabrizio-fractional-order-SEIASqEqHR-model-for-_2020_Alexandria-E.pdf.jpg
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repository.name.fl_str_mv Repositorio Institucional - Universidad Jorge Tadeo Lozano
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spelling 2020-09-25T17:28:14Z2020-09-25T17:28:14Z20201110-0168https://doi.org/10.1016/j.aej.2020.08.034http://hdl.handle.net/20.500.12010/13814https://doi.org/10.1016/j.aej.2020.08.034Fractional derivative has a memory and non-localization features that make it very useful in modelling epidemics’ transition. The kernel of Caputo-Fabrizio fractional derivative has many features such as non-singularity, non-locality and an exponential form. Therefore, it is preferred for modeling disease spreading systems. In this work, we suggest to formulate COVID-19 epidemic transmission via SEIASqEqHR paradigm using the Caputo-Fabrizio fractional derivation method. In the suggested fractional order COVID-19 SEIASqEqHR paradigm, the impact of changing quarantining and contact rates are examined. The stability of the proposed fractional order COVID-19 SEIASqEqHR paradigm is studied and a parametric rule for the fundamental reproduction number formula is given. The existence and uniqueness of stable solution of the proposed fractional order COVID-19 SEIASqEqHR paradigm are proved. Since the genetic algorithm is a common powerful optimization method, we propose an optimum control strategy based on the genetic algorithm. By this strategy, the peak values of the infected population classes are to be minimized. The results show that the proposed fractional model is epidemiologically well-posed and is a proper elect.18 páginasapplication/pdfengAlexandria Engineering Journalreponame:Expeditio Repositorio Institucional UJTLinstname:Universidad de Bogotá Jorge Tadeo LozanoCOVID-19Fractional derivativeCaputo-Fabrizio fractional order differential operatorThe existence and uniquenessGenetic algorithmSíndrome respiratorio agudo graveCOVID-19SARS-CoV-2CoronavirusNew Caputo-Fabrizio fractional order SEIASqEqHR model for COVID-19 epidemic transmission with genetic algorithm based control strategyArtículohttp://purl.org/coar/resource_type/c_2df8fbb1Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2Higazy, M.Ahmed Alyam, MaryamORIGINALNew-Caputo-Fabrizio-fractional-order-SEIASqEqHR-model-for-_2020_Alexandria-E.pdfNew-Caputo-Fabrizio-fractional-order-SEIASqEqHR-model-for-_2020_Alexandria-E.pdfVer artículoapplication/pdf4407565https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/13814/1/New-Caputo-Fabrizio-fractional-order-SEIASqEqHR-model-for-_2020_Alexandria-E.pdfaf0a95c7a6b6ebdf6d14c8975b9adeedMD51open accessLICENSElicense.txtlicense.txttext/plain; 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