Optimal Control Policies to Address the Pandemic Health-Economy Dilemma

TL;DR

This paper develops an extended SEIR model incorporating economic factors and uses multi-objective optimization to identify optimal pandemic control policies that balance health and economic outcomes.

Contribution

It introduces a macro-level model combining epidemiological and economic dynamics and applies multi-objective evolutionary algorithms to find optimal intervention strategies.

Findings

Clear conflict between health and economic performance in optimal policies

Guided NPIs are more effective than no intervention

Multi-objective algorithms help identify balanced strategies

Abstract

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.