SCIENCE CHINA Information Sciences, Volume 63 , Issue 6 : 164102(2020) https://doi.org/10.1007/s11432-020-2828-1

Visualization of COVID-19 spread based on spread and extinction indexes

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  • ReceivedFeb 29, 2020
  • AcceptedMar 12, 2020
  • PublishedMay 13, 2020


There is no abstract available for this article.


This work was supported by National Natural Science Foundation of China (Grant Nos. 61521002, 61772298) and Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology. We would like to thank Professor Shi-Min HU for his valuable suggestions, and thank Shao-Kui ZHANG and Wen-Yang ZHOU for their help in system development.


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[1] Zhou T, Liu Q , Yang Z , et al. Preliminary prediction of the basic reproduction number of the Wuhan novel coronavirus 2019-nCoV. 2020,. arXiv Google Scholar

[2] Wang D Q, Guo D H, Zhang H. Spatial temporal data visualization in emergency management: a view from data-driven decision. In: Proceedings of the 3rd ACM SIGSPATIAL Workshop on Emergency Management, 2017. 1--7. Google Scholar

[3] Dimara E, Perin C. What is Interaction for Data Visualization?. IEEE Trans Visual Comput Graphics, 2020, 26: 119-129 CrossRef PubMed Google Scholar

[4] Robertson G, Fernandez R, Fisher D. Effectiveness of animation in trend visualization.. IEEE Trans Visual Comput Graphics, 2008, 14: 1325-1332 CrossRef PubMed Google Scholar

[5] Kosara R. Presentation-Oriented Visualization Techniques.. IEEE Comput Grap Appl, 2016, 36: 80-85 CrossRef PubMed Google Scholar

  • Figure 1

    (Color online) (a) Line chart showing the number of confirmed new infections each day, and the spread index; (b) line chart for spread index and extinction index; (c) and (d) ThemeRivers for new confirmed infections by February 4 and 23, 2020, respectively; (e) bubble chart showing the pandemic situation for all cities in Hubei; (f) bubble chart showing the pandemic situation for all provinces in China.