Nonlinear Control of Covid-19 Pandemic Based on the SIRD Model

EasyChair Preprint 8313

Abstract

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.

Keyphrases: COVID-19, Pandemic, Quarantine Management, SIRD model, feedback linearization

@booklet{EasyChair:8313,
  author    = {Seyyed Mostafa Ghadami and Seyyed Masoud Seyyedi and Rituraj Rituraj},
  title     = {Nonlinear Control of Covid-19 Pandemic Based on the SIRD Model},
  howpublished = {EasyChair Preprint 8313},
  year      = {EasyChair, 2022}}