Spatial Statistics and Influencing Factors of the Epidemic of Novel Coronavirus Pneumonia 2019 in Hubei Province, China [post]

Yongzhu Xiong, Yunpeng Wang, Feng Chen, Mingyong Zhu
2020 unpublished
An in-depth understanding of spatiotemporal dynamic characteristics of infectious diseases could be helpful to an epidemic prevention and control. Based on the novel coronavirus pneumonia (NCP) data published on official websites, GIS spatial statistics and Pearson correlation methods were used to analyze the spatial autocorrelation and influencing factors of the 2019 NCP epidemic from January 30, 2020 to February 18, 2020. The results of the study showed that: (1) During the study period,
more » ... study period, Hubei Province was the only significant cluster area and hot spot of the cumulative cases confirmed with the NCP infection in China on the provincial scale; (2) The epidemic of the NCP infection in China on the prefecture-city scale had a very significant global spatial autocorrelation, and Wuhan had always been the significant hot spot and cluster city of the cumulative cases confirmed with the NCP infection in the whole country; (3) The cumulative cases confirmed with the NCP infection in Hubei Province on the county scale had a very significant global spatial autocorrelation, and the county-level districts under the jurisdiction of Wuhan and its neighboring Huangzhou district in Huanggang City were the significant hot spots and spatial clusters of the cumulative cases confirmed with the NCP infection; (4) Based on Pearson correlation analysis, the number of the accumulative cases confirmed with the NCP infection in Hubei Province on the prefecture-city scale and also on the county scale had very significant and positive correlations (p < 0.01) with the four indexes of population of registration population, resident population, regional GDP and total retail sales of consumer goods, respectively, during the study period; (5) The number of the cumulative cases confirmed with the NCP infection in Hubei Province on the prefecture-city scale also had a very significant and positive correlation (p < 0.01) with Baidu migration index and population density, respectively, but not with land area, whereas that in Hubei Province on the county scale had a significant and positive correlation (p < 0.05) with land area, but not with population density from January 30, 2020 to February 18, 2020. It is found that the NCP epidemic in Hubei Province has the distinctive characteristics of significantly centralized outbreak, significantly spatial autocorrelation and complex influencing factors and that the spatial scale has a significant effect on the global spatial autocorrelation of the NCP epidemic. The findings help to deepen the understanding of spatial distribution patterns and transmission trends of the NCP epidemic and may also benefit scientific prevention and control of epidemics such as NCP 2019.
doi:10.21203/rs.3.rs-16858/v1 fatcat:lkb3dakcfvdrrf5eml7cfs45eu