Study of COVID-19 SEWIR Model with Memory Effect of Fractal Derivative on Infectious Reaction Outbreak

Abstract

The COVID-19 epidemic was a significant occurrence that had a significant influence on the global economic and health care systems. Machine learning techniques and mathematical models are being used to study the behaviour of the virus and make long and short term forecasts about the daily new cases. In this work, we construct a SEWIR epidemic model in this paper using the Mittag Lefler Kernel in terms of fractal fractional operator. The control rate and infectious force in this model are at their peak during the latent phase. We demonstrate the presence and originality of solutions and determine the model’s fundamental reproductive number R0. For the first and second derivative tests, a global stability investigation is started using the Lyapunov function. Quantitative analysis of the collapse of second derivative equilibrium points to demonstrate the impact of another wave of dynamical transmission. The model’s parameters are subjected to sensitivity analysis in order to the specific factors with the greatest effects on the propagation rate. Infections point analysis was thoroughly explained, and a Mittag Lefler Kernel-based mathematical framework was used to develop the model’s numerical solution.