Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors

International Journal of Infectious Diseases

Dongqing Huang¹  Zhongda Ren²  Jie Wang²  Wen Dong²  Kun Yang³  Peng Zhang³  Qian Wang⁴ 

[1] Corresponding authors at: GIS Technology Engineering Research Centre for West-China Resources and Environment of Educational Ministry, Yunnan Normal University, Kunming, 650500, China.;GIS Technology Engineering Research Centre for West-China Resources and Environment of Educational Ministry, Yunnan Normal University, Kunming, 650500, China;Faculty Of Geography, Yunnan Normal University, Kunming, 650500, China;School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China;

关键词: COVID-19;    Spatio-temporal;    Migration index;    Environment temperature;    Air pollution concentration;    Government response strictness index;   

DOI  :  

来源: DOAJ

【 摘 要 】

Objectives: The purpose of this study was to explore the temporal and spatial characteristics of COVID-19 transmission and its influencing factors in China, from January to October 2020. Methods: About 81,000 COVID-19 confirmed case data, Baidu migration index data, air pollutants, meteorological data, and government response strictness index data were collected from 31 provincial-level regions (excluding Hong Kong, Macao, and Taiwan) and 337 prefecture-level cities. The spatio-temporal characteristics of COVID-19 were explored using spatial autocorrelation, hot spot, and spatio-temporal scanning statistics. At the same time, Spearman rank correlation analysis and multiple linear regression were used to explore the relationship between influencing factors and confirmed COVID-19 cases. Results: The distribution of COVID-19 in China tends to be stable over time, with spatial correlation and prominent clustering regions. Spatio-temporal scanning analysis showed that most COVID-19 high-incidence months were from January to March at the beginning of the epidemic, and the area with the highest aggregation risk was Hubei Province (RR = 491.57) which was 491.57 times the aggregation risk of other regions. Among the meteorological variables, the daily average temperature, wind speed, precipitation, and new COVID-19 cases were negatively correlated. The air pollution concentration and migration index were positively correlated with new confirmed cases, and the government response strict index was strongly negatively correlated with confirmed COVID-19 cases. Conclusions: Environmental temperature has a certain inhibitory effect on the transmission of COVID-19; the air pollution concentration and migration index have a certain promoting effect on the transmission of COVID-19. The strict government response index indicates that the greater the intensity of government intervention, the fewer COVID-19 cases will occur.

【 授权许可】

Unknown