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

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 da...

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Autores principales: Wang, Qian, Dong, Wen, Yang, Kun, Ren, Zhongda, Huang, Dongqing, Zhang, Peng, Wang, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7942191/
https://www.ncbi.nlm.nih.gov/pubmed/33711521
http://dx.doi.org/10.1016/j.ijid.2021.03.014
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author Wang, Qian
Dong, Wen
Yang, Kun
Ren, Zhongda
Huang, Dongqing
Zhang, Peng
Wang, Jie
author_facet Wang, Qian
Dong, Wen
Yang, Kun
Ren, Zhongda
Huang, Dongqing
Zhang, Peng
Wang, Jie
author_sort Wang, Qian
collection PubMed
description 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.
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spelling pubmed-79421912021-03-11 Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors Wang, Qian Dong, Wen Yang, Kun Ren, Zhongda Huang, Dongqing Zhang, Peng Wang, Jie Int J Infect Dis Article 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. The Authors. Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. 2021-04 2021-03-09 /pmc/articles/PMC7942191/ /pubmed/33711521 http://dx.doi.org/10.1016/j.ijid.2021.03.014 Text en © 2021 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Wang, Qian
Dong, Wen
Yang, Kun
Ren, Zhongda
Huang, Dongqing
Zhang, Peng
Wang, Jie
Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors
title Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors
title_full Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors
title_fullStr Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors
title_full_unstemmed Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors
title_short Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors
title_sort temporal and spatial analysis of covid-19 transmission in china and its influencing factors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7942191/
https://www.ncbi.nlm.nih.gov/pubmed/33711521
http://dx.doi.org/10.1016/j.ijid.2021.03.014
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