Open Access

ARTICLE

Bio-Inspired Numerical Analysis of COVID-19 with Fuzzy Parameters

by F. M. Allehiany¹, Fazal Dayan^2,3,*, F. F. Al-Harbi⁴, Nesreen Althobaiti⁵, Nauman Ahmed², Muhammad Rafiq⁶, Ali Raza⁷, Mawahib Elamin⁸

1 Department of Mathematical Sciences, College of Applied Sciences, Umm Al-Qura University, Makkah, 21955,Saudi Arabia
2 Department of Mathematics and Statistics, University of Lahore, Lahore, Pakistan
3 Department of Mathematics, School of Science, University of Management and Technology, Lahore, Pakistan
4 Department of Physics, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
5 Department of Mathematics and Statistics, College of Science, Taif University, Taif, 21944, Saudi Arabia
6 Department of Mathematics, Faculty of Sciences, University of Central Punjab, Lahore, Pakistan
7 Department of Mathematics, Govt. Maulana Zafar Ali Khan Graduate College Wazirabad, Punjab Higher Education Department (PHED), Lahore, 54000, Pakistan
8 Department of Mathematics, College of Science and Arts, Qassim University, Riyadh Al Khabra, Saudi Arabia

* Corresponding Author: Fazal Dayan. Email:

Computers, Materials & Continua 2022, 72(2), 3213-3229. https://doi.org/10.32604/cmc.2022.025811

Received 05 December 2021; Accepted 15 February 2022; Issue published 29 March 2022

Abstract

Fuzziness or uncertainties arise due to insufficient knowledge, experimental errors, operating conditions and parameters that provide inaccurate information. The concepts of susceptible, infectious and recovered are uncertain due to the different degrees in susceptibility, infectivity and recovery among the individuals of the population. The differences can arise, when the population groups under the consideration having distinct habits, customs and different age groups have different degrees of resistance, etc. More realistic models are needed which consider these different degrees of susceptibility infectivity and recovery of the individuals. In this paper, a Susceptible, Infected and Recovered (SIR) epidemic model with fuzzy parameters is discussed. The infection, recovery and death rates due to the disease are considered as fuzzy numbers. Fuzzy basic reproduction number and fuzzy equilibrium points have been derived for the studied model. The model is then solved numerically with three different techniques, forward Euler, Runge-Kutta fourth order method RK-4) and the nonstandard finite difference (NSFD) methods respectively. The NSFD technique becomes more efficient and reliable among the others and preserves all the essential features of a continuous dynamical system.

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Keywords

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