An Urban Trajectory Data-Driven Approach for COVID-19 Simulation

doi:10.1109/TCSS.2024.3351886

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	An Urban Trajectory Data-Driven Approach for COVID-19 Simulation
	Li, Zhishuai1 ; Xiong, Gang1,2 ; Lv, Yisheng1 ; Ye, Peijun1 ; Liu, Xiaoli 3; Tarkoma, Sasu 3; Wang, Fei-Yue1
发表期刊	IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
ISSN	2329-924X
	2024-02-02
页码	10
通讯作者	Lv, Yisheng(yisheng.lv@ia.ac.cn)
摘要	The coronavirus disease 2019 (COVID-19) pandemic has changed the world deeply. Urban trajectory big data collected by wireless sensing devices provide great assistance for COVID-19 prevention. However, except for contact tracing, trajectory data are rarely employed in other preventative scenarios against the pandemic. In this article, we try to extend the application of trajectories auto-collected by wireless sensing devices and simulate the epidemic spread in a trajectory data-driven manner. After that, the effects of three nonpharmacological measures are quantified. In contrast to existing studies, additional requirements such as the complex topological networks are needless in our simulation, where the interactions between agents are derived by the intersections of their trajectories. Concretely, the dynamic of virus propagation among individuals is first modeled, and then an agent-based microsimulation environment is built as an artificial system to conduct the epidemic spread simulation. Finally, the trajectories are loaded into the agents as the reliance for their interactions, and the macroscopic changes under different interventions are revealed in a bottom-up way. As a case study, we conduct the simulation based on the trajectories in a real region, in which we find the following. 1) Among the three examined nonpharmacological interventions, community containment is more effective than keeping social distance, which can lower the deaths to nearly 1/9 compared to no action, while travel restrictions play limited roles. 2) There is a strong positive correlation between population densities and mortality. 3) The timing of community containment triggered by confirmed diagnoses is proportional to the number of deaths, thus early containment will significantly decrease mortality.
关键词	Coronavirus disease 2019 (COVID-19) data-driven epidemic simulation trajectory transportation big data
DOI	10.1109/TCSS.2024.3351886
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2020YFB2104001] ; National Natural Science Foundation of China[62271485] ; National Natural Science Foundation of China[U1909204] ; National Natural Science Foundation of China[U1811463] ; National Natural Science Foundation of China[61872365] ; National Natural Science Foundation of China[U19B2029] ; Chinese Guangdong Basic and Applied Basic Research Foundation[2020B0909050001] ; Youth Innovation Promotion Association Chinese Academy of Sciences[2021130] ; China State Railway Group Co., Ltd. (CHINA RAILWAY) under RD Project[L2022X002.Z]
项目资助者	National Key R&D Program of China ; National Natural Science Foundation of China ; Chinese Guangdong Basic and Applied Basic Research Foundation ; Youth Innovation Promotion Association Chinese Academy of Sciences ; China State Railway Group Co., Ltd. (CHINA RAILWAY) under RD Project
WOS研究方向	Computer Science
WOS类目	Computer Science, Cybernetics ; Computer Science, Information Systems
WOS记录号	WOS:001174142300001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/57895
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Lv, Yisheng
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Guangdong Engn Res Ctr Printing & Intelligent Mfg, Cloud Comp Ctr, Dongguan 523808, Peoples R China 3.Univ Helsinki, Dept Comp Sci, Helsinki 00560, Finland
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Li, Zhishuai,Xiong, Gang,Lv, Yisheng,et al. An Urban Trajectory Data-Driven Approach for COVID-19 Simulation[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2024:10.
APA	Li, Zhishuai.,Xiong, Gang.,Lv, Yisheng.,Ye, Peijun.,Liu, Xiaoli.,...&Wang, Fei-Yue.(2024).An Urban Trajectory Data-Driven Approach for COVID-19 Simulation.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,10.
MLA	Li, Zhishuai,et al."An Urban Trajectory Data-Driven Approach for COVID-19 Simulation".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2024):10.