Optimized ANFIS-based Control Design Using Genetic Algorithm to Obtain the Vaccination and Isolation Rates for the COVID-19

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® software to show the capability of the controller in overcoming the outbreak.