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 th...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.84934-84945
Main Authors: Peni, Tamas, Csutak, Balazs, Bartha, Ferenc A., Rost, Gergely, Szederkenyi, Gabor
Format: Article
Language:English
Subjects:
Analytical models
compartmental systems
COVID-19
Design optimization
Diseases
Dynamic models
Epidemic modeling
Epidemics
Mathematical models
nonlinear optimization
Nonlinear systems
Optimization
Pandemics
Predictive control
Predictive models
Vaccines
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3197587