Using Non-Standard Finite Difference Scheme to Study Classical and Fractional Order SEIVR Model

Author:

Din Rahim Ud1,Khan Khalid Ali23ORCID,Aloqaily Ahmad45ORCID,Mlaiki Nabil4ORCID,Alrabaiah Hussam67ORCID

Affiliation:

1. Department of Mathematics, University of Malakand, Chakdra Dir (L) 18800, Pakistan

2. Unit of Bee Research and Honey Production, Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha 61413, Saudi Arabia

3. Applied College, King Khalid University, Abha 61413, Saudi Arabia

4. Department of Mathematics and Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia

5. School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2150, Australia

6. College of Engineering, Al Ain University, Al Ain P.O. Box 64141, United Arab Emirates

7. Department of Mathematics, Tafila Technical University, Tafila P.O. Box 66110, Jordan

Abstract

In this study, we considered a model for novel COVID-19 consisting on five classes, namely S, susceptible; E, exposed; I, infected; V, vaccinated; and R, recovered. We derived the expression for the basic reproductive rate R0 and studied disease-free and endemic equilibrium as well as local and global stability. In addition, we extended the nonstandard finite difference scheme to simulate our model using some real data. Moreover, keeping in mind the importance of fractional order derivatives, we also attempted to extend our numerical results for the fractional order model. In this regard, we considered the proposed model under the concept of a fractional order derivative using the Caputo concept. We extended the nonstandard finite difference scheme for fractional order and simulated our results. Moreover, we also compared the numerical scheme with the traditional RK4 both in CPU time as well as graphically. Our results have close resemblance to those of the RK4 method. Also, in the case of the infected class, we compared our simulated results with the real data.

Funder

King Khalid University

Publisher

MDPI AG

Subject

Statistics and Probability,Statistical and Nonlinear Physics,Analysis

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