Journal of Geographical Sciences ›› 2021, Vol. 31 ›› Issue (7): 1039-1058.doi: 10.1007/s11442-021-1884-2

• Research Articles • Previous Articles     Next Articles

Spatiotemporal patterns of the daily relative risk of COVID-19 in China

CHENG Changxiu1,2,3,4(), JIANG Yifan1,2,3, SONG Changqing1,3,*(), SHEN Shi1,2,3, WU Yunfeng3, ZHANG Tianyuan1,2,3   

  1. 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
    2. Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China
    3. Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    4. National Tibetan Plateau Data Center, Beijing 100101, China
  • Received:2021-03-25 Accepted:2021-05-14 Online:2021-07-25 Published:2021-09-25
  • Contact: SONG Changqing E-mail:chengcx@bnu.edu.cn;songcq@bnu.edu.cn
  • About author:Cheng Changxiu, Professor, specialized in spatiotemporal data analysis. E-mail: chengcx@bnu.edu.cn
  • Supported by:
    National Key Research and Development Plan of China(2019YFA0606901);The Second Tibetan Plateau Scientific Expedition and Research Program (STEP)(2019QZKK0608);The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23100303);The Fundamental Research Funds for the Central Universities(2019NTST01)

Abstract:

The coronavirus disease 2019 (COVID-19) pandemic continues to threaten lives and the economy around the world. Estimating the risk of COVID-19 can help in predicting spreading trends, identifying risk areas, and making public health decisions. In this study, we proposed a comparative risk assessment method to estimate comprehensive and dynamic COVID-19 risks by considering the pandemic severity and the healthcare system pressure and then employing the z-order curve and fractal theory. We took the COVID-19 cases from January 19-March 10, 2020 in China as our research object. The results and analysis revealed that (1) the proposed method demonstrated its feasibility to assess and illustrate pandemic risk; (2) the temporal patterns of the daily relative risk indices of 31 provinces were clustered into four groups (high-value, fluctuating-increase, inverted U-shaped, and low-stable); (3) the spatial distribution of the relative pandemic risk indicated a significant circular pattern centered on Hubei Province; and (4) healthcare system capacity is the key to reducing relative pandemic risk, and cases imported from abroad should be given more attention. The methods and results of this study will provide a methodological basis and practical guidance for regional pandemic risk assessment and public health decision-making.

Key words: COVID-19, risk assessment, spatiotemporal patterns, z-order curve, fractal