Efficient Suspected Infected Crowds Detection Based on Spatio-Temporal Trajectories
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by
Huajun He, Ruiyuan Li, Rubin Wang, Jie Bao, Yu Zheng, Tianrui Li
2020
Abstract
Virus transmission from person to person is an emergency event facing the
global public. Early detection and isolation of potentially susceptible crowds
can effectively control the epidemic of its disease. Existing metrics can not
correctly address the infected rate on trajectories. To solve this problem, we
propose a novel spatio-temporal infected rate (IR) measure based on human
moving trajectories that can adequately describe the risk of being infected by
a given query trajectory of a patient. Then, we manage source data through an
efficient spatio-temporal index to make our system more scalable, and can
quickly query susceptible crowds from massive trajectories. Besides, we design
several pruning strategies that can effectively reduce calculations. Further,
we design a spatial first time (SFT) index, which enables us to quickly query
multiple trajectories without much I/O consumption and data redundancy. The
performance of the solutions is demonstrated in experiments based on real and
synthetic trajectory datasets that have shown the effectiveness and efficiency
of our solutions.
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