Simulation is a useful tool for evaluating behavioral interventions when the adoption rate among a population is uncertain. Individual agent models are often prohibitively expensive, but, unlike stochastic models, allow studying compliance heterogeneity. In this paper we demonstrate the feasibility of evaluating behavioral intervention policies using large-scale data-driven agent-based simulations. We explain how the simulation is calibrated with respect to real-world data, and demonstrate the utility of our approach by studying the effectiveness of interventions used in Virginia in early 2020 through counterfactual simulations.