Quantifying the Benefits of Targeting for Pandemic Response

To respond to pandemics such as COVID-19, policy makers have relied on interventions that target specific age groups or activities. Such targeting is potentially contentious, so rigorously quantifying its benefits and downsides is critical for designing effective and equitable pandemic control policies. We propose a flexible modeling framework and algorithms to compute optimally targeted interventions that coordinate across two dimensions of heterogeneity: age of different groups and the specific activities that individuals engage in during the course of a day. We showcase a complete implementation in a case study focused on the COVID-19 epidemic in the Île-de-France region of France, based on hospitalization, community mobility, social contacts and economic data. We find that optimized dual-targeted policies generate substantial complementarities that lead to Pareto improvements, reducing the number of deaths and the economic losses overall and reducing the time in confinement for each age group, compared to less targeted interventions. These policies have a simple and explainable structure. Since dual-targeted policies could lead to increased discrepancies in the confinements faced by distinct groups, we also quantify the impact of requirements that explicitly limit such disparities, and find that satisfactory trade-offs may be achievable through limited targeting