THE SELF-PROTECTION BEHAVIOR CHANGES EFFECT IN SEIR-DM OF COVID-19 PANDEMIC MODEL WITH NUMERICAL SIMULATION AND CONTROLLING STRATEGIES PRESENTATION

In mathematical modeling, particularly with fractional-order models, significant interest arises across diverse fields. This paper aims to design and analyze a COVID-19 model incorporating self-protection dynamics, utilizing the Caputo–Fabrizio fractional derivative (CFFD) to capture the system’s behavior. The Banach fixed point theorem (FPT) is employed to establish the existence and uniqueness of solutions to the model. The results demonstrate enhanced accuracy and effectiveness compared to classical models, highlighting the applicability and advantages of fractional derivatives in disease modeling. This approach offers improved modeling of both long- and short-term memory effects, contributing to a better understanding of control strategies in dynamic systems. Additionally, a numerical scheme is implemented to support the theoretical findings. The results of this work are also plotted in various graphs.