State estimation-based control of COVID-19 epidemic before and after vaccine development. (June 2021)

• Record Type: Journal Article
• Authors: Rajaei, Arman; Raeiszadeh, Mahsa; Azimi, Vahid; Sharifi, Mojtaba
• Is Part Of: Journal of process control. Volume 102(2021)
• Page Start: 1
• Page End: 14
• Publication Date: 2021-06
• Subjects: Nonlinear robust control -- COVID-19 epidemic -- Extended Kalman filter (EKF) -- Social distancing, hospitalization and vaccination -- Lyapunov stability; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2021.03.008
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: In this study, a nonlinear robust control policy is designed together with a state observer in order to manage the novel coronavirus disease (COVID-19) outbreak having an uncertain epidemiological model with unmeasurable variables. This nonlinear model for the COVID-19 epidemic includes eight state variables (susceptible, exposed, infected, quarantined, hospitalized, recovered, deceased, and insusceptible populations). Two plausible scenarios are put forward in this article to control this epidemic before and after its vaccine invention. In the first scenario, the social distancing and hospitalization rates are employed as two applicable control inputs to diminish the exposed and infected groups. However, in the second scenario after the vaccine development, the vaccination rate is taken into account as the third control input to reduce the susceptible populations, in addition to the two objectives of the first scenario. The proposed feedback control measures are defined in terms of the hospitalized and deceased populations due to the available statistical data, while other unmeasurable compartmental variables are estimated by an extended Kalman filter (EKF). In other words, the susceptible, exposed, infected, quarantined, recovered, and insusceptible individuals cannot be identified precisely because of the asymptomatic infection of COVID-19 in some cases, its incubation period, and the lack of an adequate community screening. Utilizing the Lyapunov theorem, the stability and bounded tracking convergence of the closed-loop epidemiological system are investigated in the presence of modeling uncertainties. Finally, a comprehensive simulation study is conducted based on Canada's reported cases for two defined timing plans (with different treatment rates). Obtained results demonstrate that the developed EKF-based control scheme can achieve desired epidemic goals (exponential decrease of infected, exposed, and susceptible people). Highlights: A hypothetical robust control policy to manage the COVID-19 outbreak. Two scenarios for the epidemic control before and after the vaccine development. Hospitalized and deceased populations are the only available feedback data. EKF is used to estimate other unmeasurable compartments' populations. Lyapunov theorem is employed to ensure the controlled epidemic's stability.