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 population groups or activities. Because targeting is operationally challenging and contentious, rigorously quantifying its benefits and designing practically implementable policies that achieve some of these benefits is critical for effective and equitable pandemic control. We propose a flexible framework that leverages publicly available data and a novel optimization algorithm based on model predictive control and trust region methods to compute optimized interventions that can target two dimensions of heterogeneity: age groups and the specific activities that individuals normally engage in. We showcase a complete implementation focused on the Ile-de-France region of France and use this case study to quantify the benefits of dual targeting and to propose practically implementable policies. We find that dual targeting can lead to Pareto improvements, reducing the number of deaths and the economic losses. Additionally, dual targeting allows maintaining higher activity levels for most age groups and, importantly, for those groups that are most confined, thus leading to confinements that are arguably more equitable. We then fit decision trees to explain the decisions and gains of dual-targeted policies and find that they prioritize confinements intuitively, by allowing increased activity levels for group-activity pairs with high marginal economic value prorated by social contacts, which generates important complementarities. Because dual targeting can face significant implementation challenges, we introduce two practical proposals inspired by real-world interventions -- based on curfews and recommendations -- that achieve a significant portion of the benefits without explicitly discriminating based on age.