Nonlinear control of infection spread based on a deterministic SEIR model

In this study, a mathematical model (SEIR model) with a restriction parameter is used to explore the dynamic of the COVID-19 pandemic. This work presents a nonlinear and robust control algorithm based on variable structure control (VSC) to control the transmission of coronavirus disease (COVID-19). The VSC algorithm is a control gain switching technique in which is necessary to define a switching surface. Three switching surfaces are proposed based on rules that depend on: (i) exposed and infected population, (ii) susceptible and infected population, and (iii) susceptible and total population. In case (iii) a model-based state estimator is presented based on the extended Kalman filter (EKF) and the estimator is used in combination with the VSC. Numerical results demonstrate that the proposed control strategies have the ability to flatten the infection curve. In addition, the simulations show that the success of lowering and flattening the epidemic peak is strongly dependent on the chosen switching surfaces. A comparison between the VSC and sliding mode control (SMC) is presented showing that the VSC control can provide better performance taking into account two aspects: time duration of pandemic and the flattened curve peak with respect to SMC.