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The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level
Coronavirus disease 2019 (COVID-19) has become a worldwide public health threat. Many associated factors including population movement, meteorological parameters, air quality and socioeconomic conditions can affect COVID-19 transmission. However, no study has combined these various factors in a comp...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier B.V.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7896114/ http://dx.doi.org/10.1016/j.scitotenv.2021.145992 |
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author | Liu, Mengyang Liu, Mengmeng Li, Zhiwei Zhu, Yingxuan Liu, Yue Wang, Xiaonan Tao, Lixin Guo, Xiuhua |
author_facet | Liu, Mengyang Liu, Mengmeng Li, Zhiwei Zhu, Yingxuan Liu, Yue Wang, Xiaonan Tao, Lixin Guo, Xiuhua |
author_sort | Liu, Mengyang |
collection | PubMed |
description | Coronavirus disease 2019 (COVID-19) has become a worldwide public health threat. Many associated factors including population movement, meteorological parameters, air quality and socioeconomic conditions can affect COVID-19 transmission. However, no study has combined these various factors in a comprehensive analysis. We collected data on COVID-19 cases and the factors of interest in 340 prefectures of mainland China from 1 December 2019 to 30 April 2020. Moran's I statistic, Getis-Ord Gi(⁎) statistic and Kulldorff's space-time scan statistics were used to identify spatial clusters of COVID-19, and the geographically weighted regression (GWR) model was applied to investigate the effects of the associated factors on COVID-19 incidence. A total of 67,449 laboratory-confirmed cases were reported during the study period. Wuhan city as well as its surrounding areas were the cluster areas, and January 25 to February 21, 2020, was the clustering time of COVID-19. The population outflow from Wuhan played a significant role in COVID-19 transmission, with the local coefficients varying from 14.87 to 15.02 in the 340 prefectures. Among the meteorological parameters, relative humidity and precipitation were positively associated with COVID-19 incidence, while the average wind speed showed a negative correlation, but the relationship of average temperature with COVID-19 incidence inconsistent between northern and southern China. NO(2) was positively associated, and O(3) was negatively associated, with COVID-19 incidence. Environment with high levels of inbound migration or travel, poor ventilation, high humidity or heavy rainfall, low temperature, and high air pollution may be favorable for the growth, reproduction and spread of SARS-CoV-2. Therefore, applying appropriate lockdown measures and travel restrictions, strengthening the ventilation of living and working environments, controlling air pollution and making sufficient preparations for a possible second wave in the relatively cold autumn and winter months may be helpful for the control and prevention of COVID-19. |
format | Online Article Text |
id | pubmed-7896114 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78961142021-02-22 The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level Liu, Mengyang Liu, Mengmeng Li, Zhiwei Zhu, Yingxuan Liu, Yue Wang, Xiaonan Tao, Lixin Guo, Xiuhua Sci Total Environ Article Coronavirus disease 2019 (COVID-19) has become a worldwide public health threat. Many associated factors including population movement, meteorological parameters, air quality and socioeconomic conditions can affect COVID-19 transmission. However, no study has combined these various factors in a comprehensive analysis. We collected data on COVID-19 cases and the factors of interest in 340 prefectures of mainland China from 1 December 2019 to 30 April 2020. Moran's I statistic, Getis-Ord Gi(⁎) statistic and Kulldorff's space-time scan statistics were used to identify spatial clusters of COVID-19, and the geographically weighted regression (GWR) model was applied to investigate the effects of the associated factors on COVID-19 incidence. A total of 67,449 laboratory-confirmed cases were reported during the study period. Wuhan city as well as its surrounding areas were the cluster areas, and January 25 to February 21, 2020, was the clustering time of COVID-19. The population outflow from Wuhan played a significant role in COVID-19 transmission, with the local coefficients varying from 14.87 to 15.02 in the 340 prefectures. Among the meteorological parameters, relative humidity and precipitation were positively associated with COVID-19 incidence, while the average wind speed showed a negative correlation, but the relationship of average temperature with COVID-19 incidence inconsistent between northern and southern China. NO(2) was positively associated, and O(3) was negatively associated, with COVID-19 incidence. Environment with high levels of inbound migration or travel, poor ventilation, high humidity or heavy rainfall, low temperature, and high air pollution may be favorable for the growth, reproduction and spread of SARS-CoV-2. Therefore, applying appropriate lockdown measures and travel restrictions, strengthening the ventilation of living and working environments, controlling air pollution and making sufficient preparations for a possible second wave in the relatively cold autumn and winter months may be helpful for the control and prevention of COVID-19. Published by Elsevier B.V. 2021-07-10 2021-02-20 /pmc/articles/PMC7896114/ http://dx.doi.org/10.1016/j.scitotenv.2021.145992 Text en © 2021 Published by Elsevier B.V. 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 Liu, Mengyang Liu, Mengmeng Li, Zhiwei Zhu, Yingxuan Liu, Yue Wang, Xiaonan Tao, Lixin Guo, Xiuhua The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level |
title | The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level |
title_full | The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level |
title_fullStr | The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level |
title_full_unstemmed | The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level |
title_short | The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level |
title_sort | spatial clustering analysis of covid-19 and its associated factors in mainland china at the prefecture level |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7896114/ http://dx.doi.org/10.1016/j.scitotenv.2021.145992 |
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