Modeling, estimation, and model predictive control for Covid-19 pandemic with finite security duration vaccine

Spreading Covid19 has significantly impacted humans’ affairs worldwide, either economically or in a sanitary manner. Besides social distance and hospitalization, making and introducing different vaccines help us ameliorate infection and mortality rates. In this research, we use a nonlinear dynamic model for Covid19, with eight states named susceptible, exposed, infected, quarantined, hospitalized, recovered, deceased, and insusceptible populations. Also, we use social distancing, hospitalization, and vaccination rate as three control inputs. This research aims to stop the Covid-19 from spreading worldwide and minimize exposed, infected and deceased populations using model predictive control. Meanwhile, the measurements data defined in terms of the hospitalized and deceased populations are used to estimate other unmeasurable states by an unscented Kalman filter. In other words, the insusceptible, exposed, infected, quarantined, recovered, and susceptible individuals cannot be identified precisely because of the asymptomatic infection of the Covid-19 in some cases, its incubation period, and the lack of an adequate community screening. Finally, experimental results show that the proposed algorithm is feasible and efficient to decrease infection and mortality rates compared to the uncontrolled scenario.