热带地理

热带地理 ›› 2021, Vol. 41 ›› Issue (1): 12-24.doi: 10.13284/j.cnki.rddl.003315

所属专题： 新冠肺炎疫情

• 健康地理 • 上一篇    下一篇

基于多源数据的湖北省COVID-19疫情时空扩散影响因子分析

廖文悦1,b(), 孙美薇1,b, 余楚滢1,4, 邓应彬1,c(), 李苗b, 杨骥1,c, 李勇1,c, 许剑辉1,c, 陈裕婵1,c, 严滢伟1,c   

  1. 1.a. 广东省科学院广州地理研究所
    b.广东省地理空间信息技术与应用公共实验室
    c.广东省遥感与地理信息系统应用重点实验室，广州 510070
    2.哈尔滨师范大学 地理科学学院，哈尔滨 150025
    3.南方海洋科学与工程广东省实验室（广州），广州 511458
    4.中山大学 地理科学与规划学院，广州 510275］
  • 收稿日期:2020-05-26 修回日期:2021-01-11 出版日期:2021-01-05 发布日期:2021-02-19
  • 通讯作者: 邓应彬 E-mail:wenyue_liao@163.com;yingbin@gdas.ac.cn
  • 作者简介:廖文悦（1997—），女，四川人，硕士研究生，主要从事遥感与地理信息系统研究，（E-mail）wenyue_liao@163.com；
  • 基金资助:
    南方海洋科学与工程广东省实验室（广州）人才团队引进重大专项(GML2019ZD0301);广东省引进创新创业团队项目(2016ZT06D336)

Impact Factors of COVID-19 Epidemic Spread in Hubei Province Based on Multi-Source Data

Wenyue Liao1,b(), Meiwei Sun1,b, Chuying Yu1,4, Yingbin Deng1,c(), Miao Lib, Ji Yang1,c, Yong Li1,c, Jianhui Xu1,c, Yuchan Chen1,c, Yingwei Yan1,c   

  1. 1.a. Guangdong Open Laboratory of Geospatial Information Technology and Application
    b.Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System
    c.Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
    2.College of Geographical Science, Harbin Normal University, Harbin 150025, China
    3.Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
    4.School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
  • Received:2020-05-26 Revised:2021-01-11 Online:2021-01-05 Published:2021-02-19
  • Contact: Yingbin Deng E-mail:wenyue_liao@163.com;yingbin@gdas.ac.cn

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摘要：

利用武汉市人口迁出规模指数与武汉迁入到湖北各市人口比例的乘积（MSI）及市内交通强度（TI），建立时间回归模型，分析各市的COVID-19每日新增病例；利用交通可达性及各市GDP比例，建立空间回归模型，分析湖北省各市COVID-19的万人累计感染率。结果显示：1）基于人口迁徙数据与各市每日新增COVID-19病例构建的回归模型具有显著性（Sig.=0.00），R2达0.715；从标准化系数结果可以看出，MSI（0.85）对各市每日新增病例的影响程度更高；2）累计万人感染率与医疗机构数、GDP具有显著的正相关关系，与床位数量不相关；3）基于交通路网、社会经济数据与湖北省各市的万人累计感染率构建的空间回归模型亦具有显著性，其标准化决定系数为0.672，各市GDP比例对模型的影响更大。

关键词: 迁徙大数据, COVID-19, 疫情扩散, 交通网络, 湖北

Abstract:

With more than 26 million confirmed cases and over two million case-fatalities worldwide, the coronavirus disease (COVID-19) pandemic has transformed the dynamics of human lives globally. It has been designated as a pandemic by the World Health Organization. The COVID-19 virus can be transmitted through droplets, aerosols, or direct contact. It possesses evident characteristics of human-to-human transmission. Additionally, COVID-19 is a highly pathogenic new coronavirus, and people are prone to serious respiratory diseases resulting in high mortality after becoming infected. It has posed a great security threat to the entire human society and caused hundreds of billions of economic losses. The novel coronavirus disease 2019 (COVID-19) epidemic spread from Wuhan to all other cities in China before Spring Festival, causing serious public health issues and preventing the growth of the social economy. Analyzing the spatial-temporal spread pattern of COVID-19 can support the prevention of the epidemic. Thus, this study aims to analyze the temporal-spatial spread characteristics of COVID-19 in Hubei Province. First, a regression model with variables of migration big data (mobility scale index (MSI) and traffic intensity) is employed to explore the temporal pattern of the spread of the epidemic. Second, the spatial spread characteristics of COVID-19 are analyzed using a regression model comprising transportation information (primary and secondary road transportation networks) and social economic information (2018 GDP data). The results illustrate the following. First, the regression model based on population migration data and daily COVID-19 cases in each city was significant (Sig.=0.00), with R2 up to 0.715, indicating that the independent variable could explain the dependent variable. As indicated by the standardized coefficient results, MSI (0.85) has a greater impact on the daily new cases in each city. Second, the cumulative infection rate per 10000 people was positively correlated with the number of medical institutions and GDP with correlation coefficients of 0.689 and 0.774, respectively, Sig. was less than 0.05. However, it was not correlated with the number of beds (Sig. > 0.05). Third, the spatial regression model based on the traffic network, socio-economic data and cumulative infection rate of ten thousand people in each city of Hubei was also significant. The independent variables in the model can explain the variability of 67.2% of the dependent variables. The results of the standardized coefficient show that the GDP ratio of each city has a greater impact on the model. The results of the study are expected to provide scientific data support for the government and epidemic prevention workers to formulate efficient epidemic prevention and policy decisions. In conclusion, the model fit of multiple regression on the time scale is better than that on the spatial scale. Population migration has the greatest impact on the spread of the epidemic. That is, population mobility has a greater effect on the prevention and control of epidemic situations. The results of the study are expected to provide scientific data support for the government on formulating epidemic prevention policies.

Key words: migration big data, COVID-19, epidemic spread, transportation network, Hubei

中图分类号: 

  • R181.8