State estimation-based control of COVID-19 epidemic before and after vaccine development

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).

WITHDRAWN: Control of the COVID-19 System by Vaccination Using a New Control Engineering Technique

“…Control engineering has played a valuable role in analyzing the COVID-19 outbreak and offering innovative solutions to tackle it [10]. Many control techniques have been designed in this sector, such as robust sliding mode control [11], nonlinear robust control based on the Lyapunov analysis [12], robust optimal model predictive feedback control [13], nonlinear robust control based on state estimation [14], and nonlinear adaptive control [15]. However, the prior control approaches were designed to implement mitigation and suppression measures only and have treated the COVID-19 system with constant parameter values during the study period, although in reality, such values may vary from day to day.…”

“…Then, due to the virus's rapid spread, the SIR model was updated to the Susceptible-Exposed-Infectious-Recovered (SEIR) version, which is characterized using four state variables [11,12]. Moreover, the SIR model has been expanded to define the COVID-19 epidemic with more than four state variables [13,14].…”

Fuzzy Clustering Methods to Identify the Epidemiological Situation and Its Changes in European Countries during COVID-19

“…They proposed the θ-SEIHRD model based on the Be-CoDiS model. Rajaei et al [ 37 ] proposed a different type of nonlinear model for COVID-19. They used a state-estimation-based nonlinear robust control method for state estimation, tracking control, and robustness against uncertainties.…”

Measures to assess a warning vaccination level in a stochastic SIV model with imperfect vaccine

“…[11][12][13] Literature on compartmental epidemic models includes many papers where vaccination was used as an intervention mechanism. Some of them introduce vaccination as a control or strategy on a selected model [14][15][16] but most of the studies rely on models including additional compartments for vaccinated individuals. [17][18][19][20] Models are flexible enough to match vaccine characteristics, such as efficacy, 21,22 life-long or waning protection, 23,24 eligibility, 25 number of doses, 26 vaccine uptake, 27 and so forth.…”