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A fractional order control model for Diabetes and COVID-19 co-dynamics with Mittag-Leffler function()
The aim of this paper is to present and analyze the fractional optimal control model for COVID-19 and diabetes co-dynamics, using the Atangana-Baleanu derivative. The positivity and boundedness of the solutions was shown by the method of Laplace transform. The existence and uniqueness of the solutio...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8739033/ http://dx.doi.org/10.1016/j.aej.2022.01.012 |
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author | Omame, Andrew Nwajeri, Ugochukwu K. Abbas, M. Onyenegecha, Chibueze P. |
author_facet | Omame, Andrew Nwajeri, Ugochukwu K. Abbas, M. Onyenegecha, Chibueze P. |
author_sort | Omame, Andrew |
collection | PubMed |
description | The aim of this paper is to present and analyze the fractional optimal control model for COVID-19 and diabetes co-dynamics, using the Atangana-Baleanu derivative. The positivity and boundedness of the solutions was shown by the method of Laplace transform. The existence and uniqueness of the solutions of the proposed model were established using Banach fixed point Theorem and Leray–Schauder alternative Theorem. The fractional model was also shown to be Hyers-Ulam stable. The model was fitted to the cumulative confirmed daily COVID-19 cases for Indonesia. The simulations of the total number of hospitalized individuals co-infected with COVID-19 and diabetes, at different face-mask compliance levels, when vaccination strategy is maintained reveals that the total number of hospitalized co-infection cases decreases with increase in face-mask compliance levels, while maintaining COVID-19 vaccination. The simulation results show that to curtail COVID-19 and diabetes co-infections, policies and measures to enforce mass COVID-19 vaccination and strict face-mask usage in the public must be put in place. To further cut down the spread of COVID-19 and diabetes co-infection, time dependent controls are added into the fractional model, and the obtained optimal control problem investigated via the Pontryagin’s Maximum Principle. |
format | Online Article Text |
id | pubmed-8739033 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87390332022-01-07 A fractional order control model for Diabetes and COVID-19 co-dynamics with Mittag-Leffler function() Omame, Andrew Nwajeri, Ugochukwu K. Abbas, M. Onyenegecha, Chibueze P. Alexandria Engineering Journal Article The aim of this paper is to present and analyze the fractional optimal control model for COVID-19 and diabetes co-dynamics, using the Atangana-Baleanu derivative. The positivity and boundedness of the solutions was shown by the method of Laplace transform. The existence and uniqueness of the solutions of the proposed model were established using Banach fixed point Theorem and Leray–Schauder alternative Theorem. The fractional model was also shown to be Hyers-Ulam stable. The model was fitted to the cumulative confirmed daily COVID-19 cases for Indonesia. The simulations of the total number of hospitalized individuals co-infected with COVID-19 and diabetes, at different face-mask compliance levels, when vaccination strategy is maintained reveals that the total number of hospitalized co-infection cases decreases with increase in face-mask compliance levels, while maintaining COVID-19 vaccination. The simulation results show that to curtail COVID-19 and diabetes co-infections, policies and measures to enforce mass COVID-19 vaccination and strict face-mask usage in the public must be put in place. To further cut down the spread of COVID-19 and diabetes co-infection, time dependent controls are added into the fractional model, and the obtained optimal control problem investigated via the Pontryagin’s Maximum Principle. THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. 2022-10 2022-01-07 /pmc/articles/PMC8739033/ http://dx.doi.org/10.1016/j.aej.2022.01.012 Text en © 2022 THE AUTHORS Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Omame, Andrew Nwajeri, Ugochukwu K. Abbas, M. Onyenegecha, Chibueze P. A fractional order control model for Diabetes and COVID-19 co-dynamics with Mittag-Leffler function() |
title | A fractional order control model for Diabetes and COVID-19 co-dynamics with Mittag-Leffler function() |
title_full | A fractional order control model for Diabetes and COVID-19 co-dynamics with Mittag-Leffler function() |
title_fullStr | A fractional order control model for Diabetes and COVID-19 co-dynamics with Mittag-Leffler function() |
title_full_unstemmed | A fractional order control model for Diabetes and COVID-19 co-dynamics with Mittag-Leffler function() |
title_short | A fractional order control model for Diabetes and COVID-19 co-dynamics with Mittag-Leffler function() |
title_sort | fractional order control model for diabetes and covid-19 co-dynamics with mittag-leffler function() |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8739033/ http://dx.doi.org/10.1016/j.aej.2022.01.012 |
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