Pandemic Resilient Urban Mobility: Learning Spatiotemporal Models for Testing, Contact Tracing, and Reopening Decisions

This project focuses on the design of actionable information and effective intervention strategies to support safe mobilization of economic activity and reopening of mobility services in urban systems. We develop a spatiotemporal modeling, inference, and stochastic control framework to: 1) capture the dynamic interplay between the epidemiological state of different populations in an urban region and their mobility patterns; 2) estimate the local exposure rates and fractions of infections within populations using fine-grained contact tracing data, testing data, and occupancy measurements of key facilities/services; 3) compare and evaluate different response strategies (testing rates, partial capacity or access restrictions, and social distancing guidelines) in selected urban regions, in particular, Boston and San Francisco Bay Area; 4) design of stochastic control strategies for increasing the mobility in a phased manner by adaptively adjusting the operational capacity and testing rates. Our methodological approach is grounded in inference and learning-based control of Marked Temporal Point Processes (MTPPs) defined on a mobility network, and enables both qualitative and quantitative evaluation of contact tracing, testing, and response strategies. We will advance the inference algorithms for MTPPs by investigating both likelihood-based and likelihood-free approaches to learn model parameters from heterogeneous data. Our approach is useful for predicting the intensities of exposure and infections in different regions, conditioned on specific response strategies. Furthermore, we will design learning-based control algorithms for MTPPs to compute the optimal recovery and testing rates, which maximize the gains from sectoral reopening while limiting the risk of exposure.