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10.1016/j.ijid.2021.03.014 http://scihub22266oqcxt.onion/10.1016/j.ijid.2021.03.014 33711521!7942191!33711521     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33711521&cmd=llinks): Failed to open stream: HTTP request failed! 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Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors |pdf=|usr=}}{{33711521}} gab.com Text Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors JO: Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=33711521 #FOAvip 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. Twit Text FOAVip Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors ????Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=33711521 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33711521 #FOAvip English Wikipedia <ref>{{Cite pmid|33711521}}</ref> Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors #MMPMID33711521 Wang Q; Dong W; Yang K; Ren Z; Huang D; Zhang P; Wang J Int J Infect Dis 2021[Apr]; 105 (ä): 675-685 PMID33711521show ga 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. |Air Pollution[MESH] |COVID-19/*transmission[MESH] |China/epidemiology[MESH] |Humans[MESH] |Risk Factors[MESH] |SARS-CoV-2[MESH] |Spatio-Temporal Analysis[MESH]Temporal and spatial analysis of COVID-19 transmission in China and it(epmc).pdf DeepDyve Pubget Overpricing