SCORE: Scalable Contact Tracing over Uncertain Trajectories

In the context of a global pandemic, mitigating contagion risk requires an integrated analysis of global positioning data from location-based services and complex disease dynamics varying across geography and demography. However, the mobility datasets have inherent issues of imprecision and of being high volume. This is compounded by the challenges of changing pharmacological and non-pharmacological context of contagion behaviour, geography, demography, public health strategies across the globe. In this paper, we propose a comprehensive framework, SCORE, to provide new analytical tools for public health strategy and planning. We also propose a novel data structure, DisCoUnt, which serves as a distributed uncertain trajectory index for moving objects as well as infection event data. We conduct extensive experiments to demonstrate the scalability of our query workflow for an infection risk measure over uncertain trajectories.