关键词: 新型冠状病毒肺炎, 时间特征, 空间分布, 人群活动, 网络社区划分, 聚集性传播, 相关性分析, 手机信令大数据

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 (R_t) 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 R_t, 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