In the last month of 2019, a new version of Corona disease was observed in Wuhan (China) which is known as Covid-19. Several models have been proposed to predict disease treatment. The SIR model is considered one of the simplest models for the prediction of pandemic disease. This means susceptible (S), infected (I), and recovered (R) populations. The SIRD model is yet another method that includes one more equation, i.e., the number of deaths (D). This paper proposed a control law for the first time to prevent the progression of the disease. The proposed control law is based on the SIRD model that is determined using two methods, i.e., the input-state feedback linearization method and the input-output feedback linearization method for the nonlinear modeling of Covid-19. The goal of control in this model is to reduce the percentage or number of infected people and the number of deaths due to Covid-19 disease. Simulation results show that the feedback linearization methods can have positive results in a significant reduction in unfurl of Covid-19. Delay in quarantine of infected people and constant percentage of people who should be quarantined are investigated as two important parameters. Results show that the percentage of infected people decreases 96.3 % and the percentage of deaths decreases 93.6 % when delay in quarantine equals 7 weeks.

In the last month of 2019, a new version of Coronadisease was observed in Wuhan (China) which is known asCovid-19. Several models have been proposed to predict diseasetreatment. The SIR model is considered one of the simplestmodels for the prediction of pandemic disease. This meanssusceptible (S), infected (I), and recovered (R) populations. TheSIRD model is yet another method that includes one moreequation, i.e., the number of deaths (D). This paper proposed acontrol law for the first time to prevent the progression of thedisease. The proposed control law is based on the SIRD modelthat is determined using two methods, i.e., the input-statefeedback linearization method and the input-output feedbacklinearization method for the nonlinear modeling of Covid-19.The goal of control in this model is to reduce the percentage ornumber of infected people and the number of deaths due toCovid-19 disease. Simulation results show that the feedbacklinearization methods can have positive results in a significantreduction in unfurl of Covid-19. Delay in quarantine of infectedpeople and constant percentage of people who should bequarantined are investigated as two important parameters.Results show that the percentage of infected people decreases96.3 % and the percentage of deaths decreases 93.6 % whendelay in quarantine equals 7 weeks.