Discretization, Transformation and Analysis of the COVID-19 Wave Epidemic with Fractal Fractional Operator

Abstract

Currently, it’s far widely recognized reality that fractional evaluation is an enticing area of study, particularly in modeling. This paper discusses the proposed fractional derivatives for the dynamical transmission of the Coronavirus model with the Mittag-Leffler kernel. These are the latest techniques that give useful results in analyzing and controlling COVID-19 infection in the community. Moreover, several strategies, such as fixed-point theory, linear growth, and Ulam-Hyres stability of the suggested operator, have been applied to the fractional order model. To see the effects of the model, we employ the Sumudu transform and an effective method as well. The PID and Controllability of the model is also discussed. Lastly, by creating a computational framework that integrates the stochastic technique of artificial neural networks with Levenberg-Marquardt backpropagation (ANNLMB), we provide the numerical calculations of the suggested model in four distinct variations. The model’s tools are powerful and capable of modelling theoretical conditions. They are also suggested for application in other physical models of infectious diseases.