Optimizing Symptom Based Testing Strategies for Pandemic Mitigation

In this paper, a predictive-control-based approach is proposed for pandemic mitigation with multiple control inputs. Using previous results on the dynamical modeling of symptom-based testing, the testing intensity is introduced as a new manipulable input to the control system model in addition to the stringency of non-pharmaceutical measures. The control objective is the minimization of the severity of interventions, while the main constraints are the bounds on the daily number of hospitalized people and on the total number of available tests. For the control design and simulation, a nonlinear dynamical model containing 14 compartments is used, where the effect of vaccination is also taken into consideration. The computation results clearly show that the optimization-based design of testing intensity significantly reduces the stringency of the measures to be introduced to reach the control goal and fulfill the prescribed constraints.

Convex output feedback model predictive control for mitigation of COVID-19 pandemic.

In this paper, a model predictive control approach is proposed for epidemic mitigation. The disease spreading dynamics is described by an 8-compartment smooth nonlinear model of the COVID-19 pandemic in Hungary known from the literature, where the manipulable control input is the stringency of the introduced non-pharmaceutical measures. It is assumed that only the number of hospitalized people is measured on-line, and the other state variables are computed using a state observer which is based on the dynamic inversion of a linear sub-system of the model. The objective function contains a measure of the direct harmful consequences of the restrictions, and the constraints refer to input bounds and to the capacity of the healthcare system. By exploiting the special properties of the model, the nonlinear optimization problem required by the control design is reformulated to convex tasks, allowing a computationally efficient solution. Two approaches are proposed: the first finds a suboptimal solution by geometric programming, while the second one further simplifies the problem and transforms it to a linear programming task. Simulations show that both suboptimal solutions fulfill the design specifications even in the presence of parameter uncertainties.

Control Theoretic Approach for COVID-19 Management

A control theoretic approach can efficiently support the systematic design of strategies to suppress or mitigate the effects of the COVID-19 pandemic.