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

doi:10.1109/TCSS.2024.3351886

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	An Urban Trajectory Data-Driven Approach for COVID-19 Simulation
	Li, Zhishuai 1; Xiong, Gang 1,2; Lv, Yisheng 1; Ye, Peijun 1; Liu, Xiaoli 3; Tarkoma, Sasu 3; Wang, Fei-Yue 1
刊名	IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
	2024-02-02
页码	10
关键词	Coronavirus disease 2019 (COVID-19) data-driven epidemic simulation trajectory transportation big data
ISSN号	2329-924X
DOI	10.1109/TCSS.2024.3351886
通讯作者	Lv, Yisheng(yisheng.lv@ia.ac.cn)
英文摘要	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.
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]
WOS研究方向	Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001174142300001
资助机构	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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/57894]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
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
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.