Tags

Type your tag names separated by a space and hit enter

Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China.
Int J Environ Res Public Health. 2020 May 31; 17(11)IJ

Abstract

The coronavirus disease 2019 (COVID-19) epidemic has had a crucial influence on people's lives and socio-economic development throughout China and across the globe since December 2019. An understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic on multiple scales could benefit the control of the outbreak. Therefore, we used spatial autocorrelation and Spearman's rank correlation methods to investigate these two topics, respectively. The COVID-19 epidemic data reported publicly and relevant open data in Hubei province were analyzed. The results showed that (1) at both prefecture and county levels, the global spatial autocorrelation was extremely significant for the cumulative confirmed COVID-19 cases (CCC) in Hubei province from 30 January to 18 February 2020. Further, (2) at both levels, the significant hotspot and cluster/outlier area was observed solely in Wuhan city and most of its districts/sub-cities from 30 January to 18 February 2020. (3) At the prefecture level in Hubei province, the number of CCC had a positive and extremely significant correlation (p < 0.01) with the registered population (RGP), resident population (RSP), Baidu migration index (BMI), regional gross domestic production (GDP), and total retail sales of consumer goods (TRS), respectively, from 29 January to 18 February 2020 and had a negative and significant correlation (p < 0.05) with minimum elevation (MINE) from 2 February to 18 February 2020, but no association with the land area (LA), population density (PD), maximum elevation (MAXE), mean elevation (MNE), and range of elevation (RAE) from 23 January to 18 February 2020. (4) At the county level, the number of CCC in Hubei province had a positive and extremely significant correlation (p < 0.01) with PD, RGP, RSP, GDP, and TRS, respectively, from 27 January to 18 February 2020, and was negatively associated with MINE, MAXE, MNE, and RAE, respectively, from 26 January to 18 February 2020, and negatively associated with LA from 30 January to 18 February 2020. It suggested that (1) the COVID-19 epidemic at both levels in Hubei province had evident characteristics of significant global spatial autocorrelations and significant centralized high-risk outbreaks, and had an extremely significant association with social and economic factors. (2) The COVID-19 epidemics were significantly associated with the natural factors, such as LA, MAXE, MNE, and RAE, -only at the county level, not at the prefecture level, from 2 February to 18 February 2020. (3) The COVID-19 epidemics were significantly related to the socioeconomic factors, such as RGP, RSP, TRS, and GDP, at both levels from 26 January to 18 February 2020. It is desired that this study enrich our understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic and benefit classified prevention and control of the COVID-19 epidemic for policymakers.

Authors+Show Affiliations

School of Geography and Tourism, Jiaying University, Meizhou 514015, China.Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China.College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China. Big Data Institute of Digital Natural Disaster Monitoring in Fujian, Xiamen University of Technology, Xiamen 361024, China.School of Geography and Tourism, Jiaying University, Meizhou 514015, China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32486403

Citation

Xiong, Yongzhu, et al. "Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China." International Journal of Environmental Research and Public Health, vol. 17, no. 11, 2020.
Xiong Y, Wang Y, Chen F, et al. Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China. Int J Environ Res Public Health. 2020;17(11).
Xiong, Y., Wang, Y., Chen, F., & Zhu, M. (2020). Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China. International Journal of Environmental Research and Public Health, 17(11). https://doi.org/10.3390/ijerph17113903
Xiong Y, et al. Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China. Int J Environ Res Public Health. 2020 May 31;17(11) PubMed PMID: 32486403.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China. AU - Xiong,Yongzhu, AU - Wang,Yunpeng, AU - Chen,Feng, AU - Zhu,Mingyong, Y1 - 2020/05/31/ PY - 2020/04/28/received PY - 2020/05/22/revised PY - 2020/05/29/accepted PY - 2020/6/4/entrez PY - 2020/6/4/pubmed PY - 2020/6/18/medline KW - COVID-19 KW - Spearman’s rank correlation KW - Wuhan city KW - influencing factor KW - spatial autocorrelation KW - spatial scale JF - International journal of environmental research and public health JO - Int J Environ Res Public Health VL - 17 IS - 11 N2 - The coronavirus disease 2019 (COVID-19) epidemic has had a crucial influence on people's lives and socio-economic development throughout China and across the globe since December 2019. An understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic on multiple scales could benefit the control of the outbreak. Therefore, we used spatial autocorrelation and Spearman's rank correlation methods to investigate these two topics, respectively. The COVID-19 epidemic data reported publicly and relevant open data in Hubei province were analyzed. The results showed that (1) at both prefecture and county levels, the global spatial autocorrelation was extremely significant for the cumulative confirmed COVID-19 cases (CCC) in Hubei province from 30 January to 18 February 2020. Further, (2) at both levels, the significant hotspot and cluster/outlier area was observed solely in Wuhan city and most of its districts/sub-cities from 30 January to 18 February 2020. (3) At the prefecture level in Hubei province, the number of CCC had a positive and extremely significant correlation (p < 0.01) with the registered population (RGP), resident population (RSP), Baidu migration index (BMI), regional gross domestic production (GDP), and total retail sales of consumer goods (TRS), respectively, from 29 January to 18 February 2020 and had a negative and significant correlation (p < 0.05) with minimum elevation (MINE) from 2 February to 18 February 2020, but no association with the land area (LA), population density (PD), maximum elevation (MAXE), mean elevation (MNE), and range of elevation (RAE) from 23 January to 18 February 2020. (4) At the county level, the number of CCC in Hubei province had a positive and extremely significant correlation (p < 0.01) with PD, RGP, RSP, GDP, and TRS, respectively, from 27 January to 18 February 2020, and was negatively associated with MINE, MAXE, MNE, and RAE, respectively, from 26 January to 18 February 2020, and negatively associated with LA from 30 January to 18 February 2020. It suggested that (1) the COVID-19 epidemic at both levels in Hubei province had evident characteristics of significant global spatial autocorrelations and significant centralized high-risk outbreaks, and had an extremely significant association with social and economic factors. (2) The COVID-19 epidemics were significantly associated with the natural factors, such as LA, MAXE, MNE, and RAE, -only at the county level, not at the prefecture level, from 2 February to 18 February 2020. (3) The COVID-19 epidemics were significantly related to the socioeconomic factors, such as RGP, RSP, TRS, and GDP, at both levels from 26 January to 18 February 2020. It is desired that this study enrich our understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic and benefit classified prevention and control of the COVID-19 epidemic for policymakers. SN - 1660-4601 UR - https://www.unboundmedicine.com/medline/citation/32486403/Spatial_Statistics_and_Influencing_Factors_of_the_COVID_19_Epidemic_at_Both_Prefecture_and_County_Levels_in_Hubei_Province_China_ L2 - https://www.mdpi.com/resolver?pii=ijerph17113903 DB - PRIME DP - Unbound Medicine ER -