Social distancing measures, such as restricting occupancy at venues, have been a primary intervention for controlling the spread of COVID-19 . However, these mobility restrictions place a significant economic burden on individuals and businesses . To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures . In this paper, we present our work motivated by our interactions with the Virginia Department of Health on a decision-support tool that utilizes large-scale data and epidemiological modeling to quantify the impact of changes in mobility on infection rates . Our model captures the spread of COVID-19 by using a fine-grained, dynamic mobility network that encodes the hourly movements of people from neighborhoods to individual places, with over 3 billion hourly edges . By perturbing the mobility network, we can simulate a wide variety of reopening plans and forecast their impact in terms of new infections and the loss in visits per sector . To deploy this model in practice, we built a robust computational infrastructure to support running millions of model realizations, and we worked with policymakers to develop an intuitive dashboard interface that communicates our model's predictions for thousands of potential policies, tailored to their jurisdiction . The resulting decision-support environment provides policymakers with much-needed analytical machinery to assess the tradeoffs between future infections and mobility restrictions.