Nonlinear controller design for a fractional extended model of COVID-19 outbreak using feedback linearization method

This paper proposes a novel fractional-order epidemic model for the COVID-19 outbreak using the Caputo derivative that incorporates various intervention policies to manage the spread of the disease. A total of eight state variables were considered in this nonlinear model, namely, susceptible, exposed, infected, quarantined, hospitalized, recovered, deceased, and insusceptible. Two possible outbreak scenarios were considered to control the disease before and after vaccine discovery. The proposed system was designed using the feedback linearization method, allowing to develop a suitable controller for reducing susceptible, exposed, and infected populations. A comparative study with previous work was conducted based on Canada’s reported cases to demonstrate the advantages and disadvantages of the proposed COVID-19 outbreak control strategy using this model. The simulation results confirmed that the proposed fractional controller could effectively track the desired goals, including an exponential decrease in infected, exposed, and susceptible populations.