Vol.124, No.2, 2020, pp.477-491, doi:10.32604/cmes.2020.011121
OPEN ACCESS
ARTICLE
A Numerical Efficient Technique for the Solution of Susceptible Infected Recovered Epidemic Model
• Muhammad Shoaib Arif1,*, Ali Raza1,2, Kamaleldin Abodayeh3, Muhammad Rafiq4, Mairaj Bibi5, Amna Nazeer5
1 Stochastic Analysis and Optimization Research Group, Department of Mathematics, Air University, PAF Complex E-9, Islamabad, 44000, Pakistan
2 Department of Mathematics, National College of Business Administration and Economics, Lahore, Pakistan
3 Department of Mathematics and General Sciences, Prince Sultan University, Riyadh, Saudi Arabia
4 Faculty of Engineering, University of Central Punjab, Lahore, Pakistan
5 Department of Mathematics, Comsats University Islamabad, Islamabad, Pakistan
* Corresponding Author: Muhammad Shoaib Arif. Email: shoaib.arif@mail.au.edu.pk
Received 21 April 2020; Accepted 20 May 2020; Issue published 20 July 2020
Abstract
The essential features of the nonlinear stochastic models are positivity, dynamical consistency and boundedness. These features have a significant role in different fields of computational biology and many more. The aim of our paper, to achieve the comparison analysis of the stochastic susceptible, infected recovered epidemic model. The stochastic modelling is a realistic way to study the dynamics of compartmental modelling as compared to deterministic modelling. The effect of reproduction number has also observed in the stochastic susceptible, infected recovered epidemic model. For comparison analysis, we developed some explicit stochastic techniques, but they are the time-dependent techniques. The implicitly driven explicit technique has developed for the stochastic susceptible, infected recovered epidemic model. In the support, some theorems and graphical illustration has presented. Also, the time efficiency of this method makes it easy to find the solution of the stochastic system. The comparison with other techniques shows the efficacy and reliability of the designed technique.
Keywords
Epidemiolocal model; stochastic differential equations; stochastic techniques; convergence analysis