Modeling and Numerical Simulation for Covering the Fractional COVID-19 Model Using Spectral Collocation-Optimization Algorithm

Abstract

A primary aim of this study is to examine and simulate a fractional Coronavirus disease model by providing an efficient method for solving numerically this important model. In the Liouville-Caputo sense, the examined model consists of five fractional-order differential equations. With the Vieta-Lucas spectral collocation method, the unknown functions can be discretized and fractional derivatives can be obtained. With the system of nonlinear algebraic equations obtained, we can simplify the examined problem. In this system, the unknown coefficients are discovered by constructing and solving it as a restricted optimization problem. Some theoretical investigations are stated to examine the convergence analysis and stability analysis of the proposed approach and model. The results produced using the fractional finite difference technique (FDM), where the fractional differentiation operator was discretized using the Grünwald-Letnikov approach, are compared. The FDM relies heavily upon accurately turning the proposed model into a system of algebraic equations. To assess the algorithm’s correctness and usefulness, a numerical simulation is included.

Keywords

• fractional COVID-19 model
• LC-fractional derivative
• Vieta-Lucas polynomials
• spectral collocation method
• optimization algorithms
• convergence analysis