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.

Symptom-Based Testing in a Compartmental Model of Covid-19

Testing and isolation of cases is an important component of our strategies to fight SARS-CoV-2. In this work, we consider a compartmental model for Covid-19 including a nonlinear term representing symptom-based testing. We analyze how the considered clinical spectrum of symptoms and the testing rate affect the outcome and the severity of the outbreak. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.