Spatial statistical analysis of Coronavirus Disease 2019 (Covid-19) in China

  • Huling Li College of Public Health, Xinjiang Medical University, Urumqi, China.
  • Hui Li Central Laboratory of Xinjiang Medical University, Urumqi, China.
  • Zhongxing Ding Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
  • Zhibin Hu Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
  • Feng Chen Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
  • Kai Wang Department of Medical Engineering and Technology, Xinjiang, China. https://orcid.org/0000-0002-6224-8453
  • Zhihang Peng Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
  • Hongbing Shen | hbshen@njmu.edu.cn Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.

Abstract

The cluster of pneumonia cases linked to coronavirus disease 2019 (Covid-19), first reported in China in late December 2019 raised global concern, particularly as the cumulative number of cases reported between 10 January and 5 March 2020 reached 80,711. In order to better understand the spread of this new virus, we characterized the spatial patterns of Covid-19 cumulative cases using ArcGIS v.10.4.1 based on spatial autocorrelation and cluster analysis using Global Moran’s I (Moran, 1950), Local Moran’s I and Getis-Ord General G (Ord and Getis, 2001). Up to 5 March 2020, Hubei Province, the origin of the Covid-19 epidemic, had reported 67,592 Covid-19 cases, while the confirmed cases in the surrounding provinces Guangdong, Henan, Zhejiang and Hunan were 1351, 1272, 1215 and 1018, respectively. The top five regions with respect to incidence were the following provinces: Hubei (11.423/10,000), Zhejiang (0.212/10,000), Jiangxi (0.201/10,000), Beijing (0.196/10,000) and Chongqing (0.186/10,000). Global Moran’s I analysis results showed that the incidence of Covid-19 is not negatively correlated in space (p=0.407413>0.05) and the High-Low cluster analysis demonstrated that there were no high-value incidence clusters (p=0.076098>0.05), while Local Moran’s I analysis indicated that Hubei is the only province with High-Low aggregation (p<0.0001).

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Published
2020-06-15
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Original Articles
Keywords:
Coronavirus disease 2019 (COVID-19), Hubei province area, Spatial statistics, China
Statistics
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How to Cite
Li, H., Li, H., Ding, Z., Hu, Z., Chen, F., Wang, K., Peng, Z., & Shen, H. (2020). Spatial statistical analysis of Coronavirus Disease 2019 (Covid-19) in China. Geospatial Health, 15(1). https://doi.org/10.4081/gh.2020.867