Optimal Control Policies to Address the Pandemic Health-Economy Dilemma
This work addresses the challenge of balancing health and economic impacts during pandemics for policymakers, but it is incremental as it builds on existing SEIR models and optimization methods.
The authors tackled the pandemic health-economy dilemma by proposing a macro-level approach that integrates an economic model with an SEIR model and uses multi-objective evolutionary algorithms to study optimal control policies, finding a clear conflict between health and economy performances and suggesting guided usage of non-pharmaceutical interventions is preferable.
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. Next, several 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.