TY - GEN
T1 - Optimal Control Policies to Address the Pandemic Health-Economy Dilemma
AU - Salgotra, Rohit
AU - Moshaiov, Amiram
AU - Seidelmann, Thomas
AU - Fischer, Dominik
AU - Mostaghim, Sanaz
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Non-pharmaceutical interventions (NPIs) are effective measures to contain a pandemic. Yet, such control measures commonly have a negative effect on the economy. Here, we propose a macro-level approach to support resolving this Health-Economy Dilemma (HED). First, an extension to the well-known SEIR model is suggested which includes an economy model. Second, a bi-objective optimization problem is defined to study optimal control policies in view of the HED problem. Third, four multi-objective evolutionary algorithms are applied to perform a study on the health-economy performance trade-offs that are inherent to the obtained optimal policies. Finally, the results from the applied algorithms are compared to select a preferred algorithm for future studies. As expected, for the proposed models and strategies, a clear conflict between the health and economy performances is found. Furthermore, the results suggest that the guided usage of NPIs is preferable as compared to refraining from employing such strategies at all. This study contributes to pandemic modeling and simulation by providing a novel concept that elaborates on integrating economic aspects while exploring the optimal moment to enable NPIs.
AB - Non-pharmaceutical interventions (NPIs) are effective measures to contain a pandemic. Yet, such control measures commonly have a negative effect on the economy. Here, we propose a macro-level approach to support resolving this Health-Economy Dilemma (HED). First, an extension to the well-known SEIR model is suggested which includes an economy model. Second, a bi-objective optimization problem is defined to study optimal control policies in view of the HED problem. Third, four multi-objective evolutionary algorithms are applied to perform a study on the health-economy performance trade-offs that are inherent to the obtained optimal policies. Finally, the results from the applied algorithms are compared to select a preferred algorithm for future studies. As expected, for the proposed models and strategies, a clear conflict between the health and economy performances is found. Furthermore, the results suggest that the guided usage of NPIs is preferable as compared to refraining from employing such strategies at all. This study contributes to pandemic modeling and simulation by providing a novel concept that elaborates on integrating economic aspects while exploring the optimal moment to enable NPIs.
KW - COVID-19
KW - Control policies
KW - Economic model
KW - Multi-objective optimization
KW - Pandemic model
KW - SARS-CoV-2
UR - http://www.scopus.com/inward/record.url?scp=85114638636&partnerID=8YFLogxK
U2 - 10.1109/CEC45853.2021.9504758
DO - 10.1109/CEC45853.2021.9504758
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AN - SCOPUS:85114638636
T3 - 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings
SP - 720
EP - 727
BT - 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Congress on Evolutionary Computation, CEC 2021
Y2 - 28 June 2021 through 1 July 2021
ER -