Research Article
SCORE: Scalable Contact Tracing over Uncertain Trajectories
@INPROCEEDINGS{10.1007/978-3-031-63989-0_4, author={Avinaba Mistry and Xichen Zhang and Suprio Ray and Sanjeev Seahra}, title={SCORE: Scalable Contact Tracing over Uncertain Trajectories}, proceedings={Mobile and Ubiquitous Systems: Computing, Networking and Services. 20th EAI International Conference, MobiQuitous 2023, Melbourne, VIC, Australia, November 14--17, 2023, Proceedings, Part I}, proceedings_a={MOBIQUITOUS}, year={2024}, month={7}, keywords={location-based-service applications mobile data multi-dimensional index Contact Tracing}, doi={10.1007/978-3-031-63989-0_4} }- Avinaba Mistry
Xichen Zhang
Suprio Ray
Sanjeev Seahra
Year: 2024
SCORE: Scalable Contact Tracing over Uncertain Trajectories
MOBIQUITOUS
Springer
DOI: 10.1007/978-3-031-63989-0_4
Abstract
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.
