Journal of Geo-information Science ›› 2021, Vol. 23 ›› Issue (2): 222-235.doi: 10.12082/dqxxkx.2021.200296

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Spatial-temporal Characteristics of COVID-19 in Chongqing and Its Relationship with Human Mobility

LIU Yaxi1,2(), SONG Ci1, LIU Qiyong3, ZHANG Zhixin4, WANG Xi1,2, MA Jia5, CHEN Xiao1,2, PEI Tao1,2,*()   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
    4. China-Japan Friendship Hospital, Beijing 100029, China
    5. Oriental Hospital of Beijing University of Traditional Chinese Medicine, Beijing 100078, China
  • Received:2020-06-09 Revised:2020-07-20 Online:2021-02-25 Published:2021-04-25
  • Contact: PEI Tao E-mail:liuyx@lreis.ac.cn;peit@lreis.ac.cn
  • Supported by:
    National Natural Science Foundation of China(42041001);National Natural Science Foundation of China(41525004);National Natural Science Foundation of China(41421001)

Abstract:

Based on the epidemiological investigation data of 545 COVID-19 cases and mobile phone trajectory data of 15 million users during the epidemic ( from 21 January, 2020 to 24 February, 2020 ), this paper analyzed the spatial-temporal characteristics of COVID-19 and the human mobility changes in Chongqing. Furthermore, the correlation relationship between them was explored to explain these characteristics and changes. The results show that: (1) The epidemic pattern in Chongqing can be divided into three stages ( i.e. imported cases stage, imported cases plus local cases stage, and local cases stage ) and the real time reproduction number (Rt) was high at early stage, but declined significantly after prevention and control measures were taken; The spatial distribution of cases presented a significant clustering, and the high clustering areas were mainly distributed in the northeastern and the southwestern of Chongqing; (2) After the epidemic, the total amount of human mobility decreased to 53.20% and the decrease was mainly concentrated in the main urban area, while that of in the suburbs and rural areas did not change, or even increased; (3) The relationship between human mobility and case occurrence lies in two aspects: The correlation coefficient between daily human mobility and Rt, daily increased number of cases after an average incubation period (7 d) were 0.98, 0.87, revealing the time correlation between human mobility and case growth; The correlation coefficient between total amount of human mobility and total number of cases, number of local cases in each street (township) were 0.40, 0.35, revealing the correlation between human mobility and spatial distribution of cases. The cases clustering area corresponds to the network community of human mobility, revealing the local clustering transmission is the major transmission model. By aggregating the big data and the epidemic data, we suggests that cutting off the connection between different human mobility network communities and blocking the local transmission inside the high risk communities is an effective measure for the prevention and control of epidemics in cities.

Key words: COVID-19, temporal characteristics, spatial distribution, human mobility, network community detection, clustering transmission, correlation analysis, mobile phone big data