During an infectious disease outbreak, policymakers face the critical challenge of balancing the healthcare burden with the socioeconomic consequences of social distancing. To facilitate this complex decision-making process, we developed an integrated framework that combines multi-objective optimization, economic evaluation, and an interactive dashboard. This dashboard allows stakeholders to input dynamic cost parameters and immediately derive cost-optimal intervention strategies. We validated this framework using data from the early COVID-19 outbreak in South Korea. Our results demonstrate that cost-optimal solutions remained remarkably consistent across a wide range of costs per infection (from 4,410 USD to 361,000 USD), exhibiting similar transmission reduction patterns. This suggests that the cost-optimal policy is relatively insensitive to variations in the estimated cost per infection, providing a robust basis for intervention planning. By synthesizing rapid optimization with rigorous economic evaluation, our framework offers a scalable tool for timely, evidence-based decision-making during the urgent situation of a pandemic.
Lee et al. (Mon,) studied this question.