Details

Autor(en) / Beteiligte

• Li, Huling
• Li, Hui
• Ding, Zhongxing
• Hu, Zhibin
• Chen, Feng
• Wang, Kai
• Peng, Zhihang
• Shen, Hongbing

Titel

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

Ist Teil von

• Geospatial health, 2020-06, Vol.15 (1)

Ort / Verlag

Italy: PAGEPress Publications

Erscheinungsjahr

2020

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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).