地理学报 ›› 2020, Vol. 75 ›› Issue (11): 2521-2534.doi: 10.11821/dlxb202011018

• COVID-19疫情影响分析 • 上一篇    下一篇

基于人口流动的广东省COVID-19疫情风险时空分析

叶玉瑶1,2(), 王长建1,2(), 张虹鸥1,2, 杨骥1,2,3, 刘郑倩1,2,4, 吴康敏1,2, 邓应彬1,2   

  1. 1. 广东省科学院广州地理研究所 广东省地理空间信息技术与应用公共实验室,广州 510070
    2. 广东省科学院广州地理研究所 广东省遥感与地理信息系统应用重点实验室,广州 510070
    3. 南方海洋科学与工程广东省实验室,广州 5 114583
    4. 广东工业大学建筑与城市规划学院,广州 510090
  • 收稿日期:2020-03-09 修回日期:2020-10-26 出版日期:2020-11-25 发布日期:2021-01-25
  • 通讯作者: 王长建
  • 作者简介:叶玉瑶(1980-), 女, 四川乐山人, 博士, 研究员, 硕导, 研究方向为城市地理。E-mail: yeyuyao@gdas.ac.cn
  • 基金资助:
    国家重点研发计划(2019YFB2103101);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);广东省科学院建设国内一流研究机构行动专项资金项目(2020GDASYL-20200102002);广东省科学院建设国内一流研究机构行动专项资金项目(2020GDASYL-20200301003)

Spatio-temporal analysis of COVID-19 epidemic risk in Guangdong Province based on population migration

YE Yuyao1,2(), WANG Changjian1,2(), ZHANG Hong'ou1,2, YANG Ji1,2,3, LIU Zhengqian1,2,4, WU Kangmin1,2, DENG Yingbin1,2   

  1. 1. Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
    2. Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
    3. Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou 511458, China
    4. School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, China
  • Received:2020-03-09 Revised:2020-10-26 Online:2020-11-25 Published:2021-01-25
  • Contact: WANG Changjian
  • Supported by:
    National Key R&D Program of China(2019YFB2103101);Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)(GML2019ZD0301);GDAS Special Project of Science and Technology Development(2020GDASYL-20200102002);GDAS Special Project of Science and Technology Development(2020GDASYL-20200301003)

摘要:

人口流动,特别是来自疫源区的人口输入,COVID-19疫情传播的关键风险来源。本文以广东省为例,利用人口迁移大数据与地理空间分析技术,综合考虑人口来源地风险差异与流入地社会经济属性差异,并依据输入性病例发病的间隔时间分布引入时滞过程,构建了包含时滞效应的疫情风险时空分析模型,理论上可以根据现状人口流动研判未来一定时期的疫情风险的变化趋势及空间格局,为提前谋划和精准防控提供决策参考。分析结果表明:① 广东省新型冠状病毒肺炎疫情在2020年1月29日拐点初现,随后呈现逐步下降的趋势。② 基于输入性病例发病的时滞过程分析,输入性病例从输入到发病普遍存在间隔时间,且间隔时间为1~14 d的病例比重较高。③ 疫情风险存在明显的空间差异,各地疫情风险依据输入风险、易感风险以及抵御风险能力的不同而存在较大的差异。④ 广东省各地市与疫源区城市之间的联系程度、人口流动规模及其交通区位因素,显著影响省内疫情风险的分级。深圳、广州等一线城市是高风险区,东莞、佛山、惠州、珠海、中山等邻近深圳和广州的珠三角城市是中风险区,珠三角城市群外围的粤东西北地区是低风险区。应根据疫情潜在风险,制定基于分区分级的防控措施,促进局地精准防控与社会整体良性运转。

关键词: 人口流动, 新型冠状病毒肺炎, 疫情风险, 时滞过程, 时空分析

Abstract:

Population migration, especially population input from epidemic area, is a key source of the risk related to the COVID-19 epidemic. Taking Guangdong Province as an example, this paper utilizes big data on population migration and the geospatial analysis technique to develop a model to conduct spatiotemporal analysis of COVID-19 risk. The model considers the risk differences among the source cities of population migration as well as the heterogeneity in the socioeconomic characteristics of the destination cities. It further incorporates a time-lag process based on the time distribution of the onset of the imported cases. The model can predict the evolutional trend and spatial distribution of the COVID-19 risk for a certain time period in the future and support the future planning and targeted prevention measures. The research findings indicate that: (1) The COVID-19 epidemic in Guangdong reached a inflection point on January 29, 2020, and then it showed a gradual decline. (2) Based on the time-lag analysis of the onset of the imported cases, there is a time interval between the case importation and the illness onset, and the cases with an interval of 1-14 days account for a high proportion. (3) There are obvious spatial differences in the risk of epidemics, based on their imported risk, susceptibility risk, and risk resisting ability. (4) The connection and the scale of population migration as well as the transportation and location factors of the cities in Guangdong's prefecture-level cities and the source regions of the epidemic, all have significant impacts on the risk classification of the cities in the province. The first-tier cities such as Shenzhen and Guangzhou are the high-risk areas. The cities in the Pearl River Delta that are adjacent to Shenzhen and Guangzhou, including Dongguan, Foshan, Huizhou, Zhuhai and Zhongshan, are the medium-risk cities. The eastern, northern, and western parts of Guangdong, which are outside the metropolitan areas of the Pearl River Delta, are classed into low-risk areas. Therefore, the government should take targeted prevention and control measures in different regions based on local conditions and risk classification so as to ensure people's daily life and wellbeing to the greatest possible extent.

Key words: population migration, COVID-19, epidemic risk, time delay process, spatiotemporal analysis