Efficient Suspected Infected Crowds Detection Based on Spatio-Temporal Trajectories release_nebzqpp635hwpf5fe26x4nfb6q

by Huajun He, Ruiyuan Li, Rubin Wang, Jie Bao, Yu Zheng, Tianrui Li

Released as a article .

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.
In text/plain format

Archived Files and Locations

application/pdf  1.0 MB
file_rdvhsviphvdeddcqqrrbgbwfvq
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2020-04-11
Version   v1
Language   en ?
arXiv  2004.06653v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 646410c4-dc60-4ff3-8b54-38804002d743
API URL: JSON