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)
Description
Summary: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.