ABSTRACT
In this work, an Adaptive-Network-based Fuzzy Inference System (ANFIS) control is designed and optimized with the Genetic Algorithm (GA) to control the COVID-19 described by the SEIAR (Susceptible - Exposed - Infected - Asymptomatic - Recovered) epidemic model. This work aims to reduce the number of infected and susceptible people by isolation and vaccination, respectively. In this regard, the ANFIS-based controller is designed. The GA is employed to generate an optimal data set by minimizing the appropriate objective function to train the ANFIS algorithm. The obtained results are evaluated via simulation in MATLAB (R) software to show the capability of the controller in overcoming the outbreak.
ABSTRACT
In this paper, a new switched SEIAR-Vac-Iso (Susceptible, Exposed, Infected, Asymptomatic, Recovered, Vaccinated, Isolated) epidemic model is introduced and investigated with application to COVID-19 for the first time. Two theorems concerning the positivity and boundedness of the solutions are proved. Then, the basic reproduction number (R-0) and the equilibrium points of the new model are calculated. The stability of the switched system is also investigated by developing a Lyapunov function and using the switching invariance principle, then the stability conditions of the systems are obtained. Numerical simulations are presented to verify the accuracy of theoretical results.