Towards Controlling the Transmission of Diseases: Continuous Exposure Discovery over Massive-Scale Moving Objects
Ke Li, Lisi Chen, Shuo Shang, Haiyan Wang, Yang Liu, Panos Kalnis, Bin Yao
Abstract
Infectious diseases have been recognized as major public health concerns for decades. Close contact discovery is playing an indispensable role in preventing epidemic transmission. In this light, we study the continuous exposure search problem: Given a collection of moving objects and a collection of moving queries, we continuously discover all objects that have been directly and indirectly exposed to at least one query over a period of time. Our problem targets a variety of applications, including but not limited to disease control, epidemic pre-warning, information spreading, and co-movement mining. To answer this problem, we develop an exact group processing algorithm with optimization strategies. Further, we propose an approximate algorithm that substantially improves the efficiency without false dismissal. Extensive experiments offer insight into effectiveness and efficiency of our proposed algorithms.
BibTeX
@inproceedings{ijcai2022p540,
title = {Towards Controlling the Transmission of Diseases: Continuous Exposure Discovery over Massive-Scale Moving Objects},
author = {Li, Ke and Chen, Lisi and Shang, Shuo and Wang, Haiyan and Liu, Yang and Kalnis, Panos and Yao, Bin},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {3891--3897},
year = {2022},
month = {7},
note = {Main Track},
doi = {10.24963/ijcai.2022/540},
url = {https://doi.org/10.24963/ijcai.2022/540},
}