地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (11): 1894-1909.doi: 10.12082/dqxxkx.2021.210091

• 专栏:全球新型冠状病毒肺炎(COVID-19)疫情时空建模与决策分析 • 上一篇    下一篇


尹凌1,*(), 刘康1, 张浩1,2, 奚桂锴1,2, 李璇1,2, 李子垠1,2, 薛建章1,3   

  1. 1. 中国科学院深圳先进技术研究院,深圳 518055
    2. 中国科学院大学,北京 100049
    3. 中国科学技术大学,合肥 230026
  • 收稿日期:2021-02-02 修回日期:2021-09-15 出版日期:2021-11-25 发布日期:2022-01-25
  • 作者简介:尹凌(1981- ),女,重天人,博 上,研究员,研究方向为时空大数据空间传染病模型交通GIS。E-mail: yinling@siat.ac.cn
  • 基金资助:

Integrating Human Mobility into the Epidemiological Models of COVID-19: Progress and Challenges

YIN Ling1,*(), LIU Kang1, ZHANG Hao1,2, XI Guikai1,2, LI Xuan1,2, LI Ziyin1,2, XUE Jianzhang1,3   

  1. 1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    2. University of Chinese Academy of Sciences, Beijing 100049
    3. University of Science and Technology of China, Hefei 230026, China
  • Received:2021-02-02 Revised:2021-09-15 Online:2021-11-25 Published:2022-01-25
  • Contact: YIN Ling
  • Supported by:
    National Key Research and Development Project, No(2019YFB2102500);National Natural Science Foundation of China, No(41771441);National Natural Science Foundation of China, No(41901391);Major science and technology projects of Xinjiang Uygur Autonomous Region, No(2020A03004-4);Natu-ral Science Foundation of Guangdong Province, No(2021A1515011191);Fundamental Research Project of Shenzhen City, No(JCYJ20190807163001783);Bill & Melinda Gates Foundation, No(INV-005834)



关键词: 新冠肺炎, COVID-19, 传染病, 人群移动, 仓室模型, 个体模型, 智能体模型, 机器学习, 轨迹数据, 时空数据挖掘


The spread of infectious diseases is usually a highly nonlinear space-time diffusion process. Epidemiological models can not only be used to predict the epidemic trend, but also be used to systematically and scientifically study the transmission mechanism of the complex processes under different hypothetical intervention scenarios, which provide crucial analytical and planning tools for public health studies and policy-making. Since host behavior is one of the critical driven factors for the dynamics of infectious diseases, it is important to effectively integrate human spatiotemporal behavior into the epidemiological models for human-hosted infectious diseases. Due to the rapid development of human mobility research and applications aided by big trajectory data, many of the epidemiological models for Coronavirus Disease 2019 (COVID-19) have already coupled human mobility. By incorporating real trajectory data such as mobile phone location data at an individual or aggregated level, researchers are working towards the direction of accurately depicting the real world, so as to improve the effectiveness of the model in guiding actual epidemic prevention and control. The epidemic trend prediction, Non-pharmaceutical Interventions (NPIs) evaluation, vaccination strategy design, and transmission driven factors have been studied by the epidemiological models coupled with human mobility, which provides scientific decision-making aid for controlling epidemic in different countries and regions. In order to systematically understand this important progress of epidemiological models, this study collected and summarized relevant literatures. First, the interactions between the COVID-19 epidemic and human mobility were analyzed, which demonstrated the necessity of integrating the complex spatiotemporal behavior, such as population-based or individual-based mobility, activity, and contact interaction, into the epidemiological models. Then, according to the modeling purpose and mechanism, the models integrated with human mobility were discussed by two types: short-term epidemic prediction models and process simulation models. Among them, based on the coupling methods of human mobility, short-term epidemic prediction models can further be divided into models coupled with first-order and second-order human mobility, while process simulation models can be divided into models coupled with population-based mobility and individual-based mobility. Finally, we concluded that epidemiological models integrating human mobility should be developed towards more complex human spatiotemporal behaviors with a fine spatial granularity. Besides, it is in urgent need to improve the model capability to better understand the disease spread processes over space and time, break through the bottleneck of the huge computational cost of fine-grained models, cooperate cutting-edge artificial intelligence approaches, and develop more universal and accessible modeling data sets and tools for general users.

Key words: coronavirus disease, COVID-19, epidemic, human mobility, compartment model, individual-based model, agent-based model, machine learning, trajectory data, spatiotemporal data mining