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基于谱系聚类的全球各国新冠疫情时间序列特征分析
引用本文:谢聪慧,吴世新,张晨,孙文涛,何海芳,裴韬,罗格平. 基于谱系聚类的全球各国新冠疫情时间序列特征分析[J]. 地球信息科学学报, 2021, 23(2): 236-245. DOI: 10.12082/dqxxkx.2021.200470
作者姓名:谢聪慧  吴世新  张晨  孙文涛  何海芳  裴韬  罗格平
作者单位: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 8300112.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 8300113.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 8100084.青海省盐湖地质与环境重点实验室,西宁 8100085.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 1001016.中国科学院大学,北京 100049
基金项目: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)。
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...

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
收稿时间:2020-08-17

Analysis of Time Series Features of COVID-19 in Various Countries based on Pedigree Clustering
XIE Conghui,WU Shixin,ZHANG Chen,SUN Wentao,HE Haifang,PEI Tao,LUO Geping. Analysis of Time Series Features of COVID-19 in Various Countries based on Pedigree Clustering[J]. Geo-information Science, 2021, 23(2): 236-245. DOI: 10.12082/dqxxkx.2021.200470
Authors:XIE Conghui  WU Shixin  ZHANG Chen  SUN Wentao  HE Haifang  PEI Tao  LUO Geping
Affiliation:(Key Laboratory of Desert and Oasis Ecology,Institute of Ecology and Geography,Xinjiang,Chinese Academy of Sciences,Xinjiang,Xinjiang,Urumqi 830011,China;Key Laboratory of Remote Sensing and GIS Applications,Xinjiang,Xinjiang,Urumqi 830011,China;Key Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources,Qinghai Institute of Salt Lakes,Chinese Academy of Sciences,Xining 810008,China;Qinghai Provincial Key Laboratory of Geology and Environment of Salt Lakes,Xining 810008,China;State key laboratory of resource and Environmental Information Systems,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China)
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.
Keywords:COVID-19  time series  data mining  statistical structure characteristics  pedigree clustering  global public health  prevention and control measures
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