地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (2): 236-245.doi: 10.12082/dqxxkx.2021.200470

• 疫情时空分析 • 上一篇    下一篇

基于谱系聚类的全球各国新冠疫情时间序列特征分析

谢聪慧1,2,6(), 吴世新1,2,*(), 张晨1,2,6, 孙文涛1,6, 何海芳3,4,6, 裴韬5,6, 罗格平1,2   

  1. 1.中国科学院新疆生态与地理研究所荒漠与绿洲生态国家重点实验室,乌鲁木齐 830011
    2.新疆维吾尔自治区遥感与地理信息系统应用重点实验室,乌鲁木齐 830011
    3.中国科学院青海盐湖研究所,中国科学院盐湖资源综合高效利用重点实验室,西宁 810008
    4.青海省盐湖地质与环境重点实验室,西宁 810008
    5.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    6.中国科学院大学,北京 100049
  • 收稿日期:2020-08-17 修回日期:2020-11-21 出版日期:2021-02-25 发布日期:2021-04-25
  • 通讯作者: 吴世新 E-mail:xieconghui19@mails.ucas.ac.cn;wushixin@ms.xjb.ac.cn
  • 作者简介:谢聪慧(1997— ),女,硕士生,主要从事遥感与地理信息系统应用。E-mail: xieconghui19@mails.ucas.ac.cn
  • 基金资助:
    中国科学院战略性先导科技专项(A类)(XDA23100000);国家科技基础资源调查专项(2017FY101004);国家自然科学基金项目(42041001)

Analysis of Time Series Features of COVID-19 in Various Countries based on Pedigree Clustering

XIE Conghui1,2,6(), WU Shixin1,2,*(), ZHANG Chen1,2,6, SUN Wentao1,6, HE Haifang3,4,6, PEI Tao5,6, LUO Geping1,2   

  1. 1. Key Laboratory of Desert and Oasis Ecology, Institute of Ecology and Geography, Xinjiang, Chinese Academy of Sciences, Urumqi 830011, China
    2. Key Laboratory of Remote Sensing and GIS Applications, Xinjiang, Xinjiang, Urumqi 830011, China
    3. Key Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources, Qinghai Institute of Salt Lakes, Chinese Academy of Sciences, Xining 810008, China
    4. Qinghai Provincial Key Laboratory of Geology and Environment of Salt Lakes, Xining 810008, China
    5. State key laboratory of resource and Environmental Information Systems, Chinese Academy of Sciences, Beijing 100101, China
    6. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-08-17 Revised:2020-11-21 Online:2021-02-25 Published:2021-04-25
  • Contact: WU Shixin E-mail:xieconghui19@mails.ucas.ac.cn;wushixin@ms.xjb.ac.cn
  • Supported by:
    Chinese Academy of Sciences strategic leading science and technology project (Class A)(XDA23100000);National Science and technology basic resources survey project(2017FY101004);National Natural Science Foundation of China(42041001)

摘要:

COVID-19暴发以来,世界各国疫情呈现出不同的时序特点,研究不同国家疫情发展模式的特点,揭示其背后的主导因素,可为未来防控策略提供参考。为了揭示不同国家疫情时间序列之间的异同,本文提取了主要疫情国家每日新增病例时间序列的标准差、Hurst指数、治愈率、增长时长、平均增长率、防控效率进行谱系聚类,并从经济、医疗、人文冲突方面对聚类结果进行了成因分析。结果表明,全球疫情发展模式可分为3大类:C型、S型和I型。C型国家时间序列的特点是持续波动上涨,治愈率较低,原因是其人文冲突不利于疫情防控,经济医疗资源经过长时间大量消耗已趋于匮乏,建议在防控中加强宣传疏导,改变观念,统筹分配经济、医疗资源;S型国家时间序列的特点是快速上升后立即下降,并最终保持稳定趋势,总体治愈率较高,其原因是这类国家国内稳定,经济医疗水平较高,以及防控措施及时,建议加强国际合作和科学研究,并为可能到来的二次疫情做好准备;I型国家时间序列特点是缓慢上涨,整体发展趋势不稳定,治愈率较低,原因是其暴发比较晚,程度较小,大部分经济医疗水平以及人文冲突不利于疫情防控,建议汲取较好的防控经验,实施严格的隔离措施,尽量满足疫情期间物资需求,优化治疗方法。

关键词: COVID-19, 时间序列, 数据挖掘, 统计结构特征, 谱系聚类, 全球公共卫生, 防控措施

Abstract:

Since the outbreak of COVID-19, countries around the world have shown different time-series characteristics. Studying the characteristics of the development patterns of different countries and revealing the dominant factors behind them can provide references for future prevention and control strategies. In order to reveal the similarities and differences between the epidemic time series in different countries, this article extracts the standard deviation, Hurst index, cure rate, growth time, average growth rate, and prevention and control efficiency of the daily time series of new cases in the main epidemic countries for pedigree clustering. We also analyzes the causes of clustering results from the aspects of economics, medical treatment, and humanistic conflicts. The results show that the global epidemic development model can be divided into three categories: C-type, S-type, and I-type. The time series of C-type countries are characterized by continuous fluctuations and rising, and the cure rate is low. The reason is that humanistic conflicts are not conducive to epidemic prevention and control. Economic and medical resources have become scarce after a long period of large consumption. It is recommended to strengthen publicity and guidance in prevention and control, change concepts, and coordinate the allocation of economic and medical resources. The time series of S-type countries is characterized by a rapid rise and then an immediate decline, and eventually maintains a stable trend. The overall cure rate is relatively high. The reason is that these countries have domestic stability, high economic and medical standards, and timely prevention and control measures. It is recommended to strengthen international cooperation and scientific research, and prepare for the possible second epidemic. The time series of I-shaped countries is characterized by a slow rise, the overall development trend is unstable, and the cure rate is low. The reason is that its outbreak is relatively late and less severe. Most of the economic and medical levels and humanistic conflicts are not conducive to epidemic prevention and control. It is recommended to learn better prevention and control experience, implement strict isolation measures, try to meet the material needs during the epidemic, and optimize treatment methods.

Key words: COVID-19, time series, data mining, statistical structure characteristics, pedigree clustering, global public health, prevention and control measures