Muhammad Shoaib Arif^1,2,*, Kamaleldin Abodayeh¹, Yasir Nawaz²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3417-3434, 2024, DOI:10.32604/cmes.2024.046944

Abstract This study focuses on the urgent requirement for improved accuracy in disease modeling by introducing a new computational framework called the Hybrid SIR-Fuzzy Model. By integrating the traditional Susceptible-Infectious-Recovered (SIR) model with fuzzy logic, our method effectively addresses the complex nature of epidemic dynamics by accurately accounting for uncertainties and imprecisions in both data and model parameters. The main aim of this research is to provide a model for disease transmission using fuzzy theory, which can successfully address uncertainty in mathematical modeling. Our main emphasis is on the imprecise transmission rate parameter, utilizing a three-part description of its membership level.… More >

Ibtehal Alazman¹, Kholoud Saad Albalawi¹, Pranay Goswami^2,*, Kuldeep Malik²

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2409-2425, 2023, DOI:10.32604/cmes.2023.028674

Abstract This paper presents a restricted SIR mathematical model to analyze the evolution of a contagious infectious disease outbreak (COVID-19) using available data. The new model focuses on two main concepts: first, it can present multiple waves of the disease, and second, it analyzes how far an infection can be eradicated with the help of vaccination. The stability analysis of the equilibrium points for the suggested model is initially investigated by identifying the matching equilibrium points and examining their stability. The basic reproduction number is calculated, and the positivity of the solutions is established. Numerical simulations are performed to determine if… More >

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

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3213-3229, 2022, DOI:10.32604/cmc.2022.025811

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… More >