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Mathematical modelling to predict the effect of vaccination on delay and rise of COVID-19 cases management

In this paper, a mathematical model based on COVID-19 is developed to study and manage disease outbreaks. The effect of vaccination with regard to its efficacy and percentage of population vaccinated in a closed population is investigated. To study virus transmission, the system employs six nonlinea...

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Main Authors:	Charu Arora, Charu Arora, Poras Khetarpal, Poras Khetarpal, Saket Gupta, Saket Gupta, Nuzhat Fatema, Nuzhat Fatema, Malik, Hasmat, Afthanorhan, Asyraf
Format:	Article
Language:	English
Published:	MDPI 2023
Subjects:	Q Science (General) T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/105670/1/HasmatMalik2023_MathematicalModellingtoPredicttheEffect.pdf http://eprints.utm.my/105670/ http://dx.doi.org/10.3390/math11040821
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id	my.utm.105670
record_format	eprints
spelling	my.utm.1056702024-05-13T07:00:29Z http://eprints.utm.my/105670/ Mathematical modelling to predict the effect of vaccination on delay and rise of COVID-19 cases management Charu Arora, Charu Arora Poras Khetarpal, Poras Khetarpal Saket Gupta, Saket Gupta Nuzhat Fatema, Nuzhat Fatema Malik, Hasmat Afthanorhan, Asyraf Q Science (General) T Technology (General) TK Electrical engineering. Electronics Nuclear engineering In this paper, a mathematical model based on COVID-19 is developed to study and manage disease outbreaks. The effect of vaccination with regard to its efficacy and percentage of population vaccinated in a closed population is investigated. To study virus transmission, the system employs six nonlinear ordinary differential equations with susceptible–exposed–asymptomatic–infected–vaccinated–recovered populations and the basic reproduction number are calculated. The proposed model describes for highly infectious diseases (such as COVID-19) in a closed containment area with no migration. This paper considers that the percentage of vaccinated population has a significant impact on the number of COVID-19 positive cases during the pandemic wave and examines how the pandemic rise time is delayed. Numerical simulation to investigate disease outbreaks when the community is undergoing vaccination is performed, taking the efficacy rate of the vaccine into account. Sensitivity Index values are calculated for the reproduction number and their relations with few other parameters are depicted. MDPI 2023-02 Article PeerReviewed application/pdf en http://eprints.utm.my/105670/1/HasmatMalik2023_MathematicalModellingtoPredicttheEffect.pdf Charu Arora, Charu Arora and Poras Khetarpal, Poras Khetarpal and Saket Gupta, Saket Gupta and Nuzhat Fatema, Nuzhat Fatema and Malik, Hasmat and Afthanorhan, Asyraf (2023) Mathematical modelling to predict the effect of vaccination on delay and rise of COVID-19 cases management. Mathematics, 11 (4). pp. 1-15. ISSN 2227-7390 http://dx.doi.org/10.3390/math11040821 DOI:10.3390/math11040821
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	Q Science (General) T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	Q Science (General) T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Charu Arora, Charu Arora Poras Khetarpal, Poras Khetarpal Saket Gupta, Saket Gupta Nuzhat Fatema, Nuzhat Fatema Malik, Hasmat Afthanorhan, Asyraf Mathematical modelling to predict the effect of vaccination on delay and rise of COVID-19 cases management
description	In this paper, a mathematical model based on COVID-19 is developed to study and manage disease outbreaks. The effect of vaccination with regard to its efficacy and percentage of population vaccinated in a closed population is investigated. To study virus transmission, the system employs six nonlinear ordinary differential equations with susceptible–exposed–asymptomatic–infected–vaccinated–recovered populations and the basic reproduction number are calculated. The proposed model describes for highly infectious diseases (such as COVID-19) in a closed containment area with no migration. This paper considers that the percentage of vaccinated population has a significant impact on the number of COVID-19 positive cases during the pandemic wave and examines how the pandemic rise time is delayed. Numerical simulation to investigate disease outbreaks when the community is undergoing vaccination is performed, taking the efficacy rate of the vaccine into account. Sensitivity Index values are calculated for the reproduction number and their relations with few other parameters are depicted.
format	Article
author	Charu Arora, Charu Arora Poras Khetarpal, Poras Khetarpal Saket Gupta, Saket Gupta Nuzhat Fatema, Nuzhat Fatema Malik, Hasmat Afthanorhan, Asyraf
author_facet	Charu Arora, Charu Arora Poras Khetarpal, Poras Khetarpal Saket Gupta, Saket Gupta Nuzhat Fatema, Nuzhat Fatema Malik, Hasmat Afthanorhan, Asyraf
author_sort	Charu Arora, Charu Arora
title	Mathematical modelling to predict the effect of vaccination on delay and rise of COVID-19 cases management
title_short	Mathematical modelling to predict the effect of vaccination on delay and rise of COVID-19 cases management
title_full	Mathematical modelling to predict the effect of vaccination on delay and rise of COVID-19 cases management
title_fullStr	Mathematical modelling to predict the effect of vaccination on delay and rise of COVID-19 cases management
title_full_unstemmed	Mathematical modelling to predict the effect of vaccination on delay and rise of COVID-19 cases management
title_sort	mathematical modelling to predict the effect of vaccination on delay and rise of covid-19 cases management
publisher	MDPI
publishDate	2023
url	http://eprints.utm.my/105670/1/HasmatMalik2023_MathematicalModellingtoPredicttheEffect.pdf http://eprints.utm.my/105670/ http://dx.doi.org/10.3390/math11040821
_version_	1800082645938339840
score	13.244404

Mathematical modelling to predict the effect of vaccination on delay and rise of COVID-19 cases management

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