Journal of Geo-information Science ›› 2021, Vol. 23 ›› Issue (2): 236-245.doi: 10.12082/dqxxkx.2021.200470

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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;
  • 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)


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