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Optimizing Symptom Based Testing Strategies for Pandemic Mitigation
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DOI：10.1109/ACCESS.2022.3197587.png)
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摘要
En 中文
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.
Keyword:
Epidemics
COVID-19
Diseases
Vaccines
Mathematical models
Pandemics
Analytical models
Predictive models
Nonlinear systems
Optimization
Epidemic modeling
compartmental systems
nonlinear optimization
predictive control
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
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