Exploring Dynamic Pandemic Containment Strategies Using Multi-Objective Optimization [Research Frontier]

The SARS-CoV-2 pandemic demonstrates the vulnerability of societies in a globalized world. As pathogens spread at exponential rates, rapid development of appropriate medical treatments and distribution of vaccinations are major challenges. Under these circumstances, authorities employ non-pharmaceutical interventions (NPIs) against the spread, which can impact the economy strongly. Hence, there is a need for strategies that help minimize infection without sacrificing the economy's wellbeing. This study explores the inherent trade-off characteristics of optimal control strategies by utilizing the well-known SEIR (susceptible, exposed, infectious, recovered) pandemic model with an integrated economic compartment. The health economy dilemma (HED) qualifies as a multi-objective optimization (MOO) problem and the goal is to find strategies which are optimal regarding concurrent infections, economic growth, and required intensity of employed interventions. The major contribution of this paper is to propose a new methodology for containment strategy exploration using MOO. The experiments show that the resulting solutions can contribute towards solving the HED by supporting the identification of optimal strategies. Specific characteristics of pandemics are highlighted in this novel tri-objective optimization approach.