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Correlation Analysis between Urban Elements and COVID-19 Transmission Using Social Media Data

The outbreak of the COVID-19 has become a worldwide public health challenge for contemporary cities during the background of globalization and planetary urbanization. However, spatial factors affecting the transmission of the disease in urban spaces remain unclear. Based on geotagged COVID-19 cases...

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Detalles Bibliográficos
Autores principales: Wang, Ru, Liu, Lingbo, Wu, Hao, Peng, Zhenghong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101567/
https://www.ncbi.nlm.nih.gov/pubmed/35564606
http://dx.doi.org/10.3390/ijerph19095208
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author Wang, Ru
Liu, Lingbo
Wu, Hao
Peng, Zhenghong
author_facet Wang, Ru
Liu, Lingbo
Wu, Hao
Peng, Zhenghong
author_sort Wang, Ru
collection PubMed
description The outbreak of the COVID-19 has become a worldwide public health challenge for contemporary cities during the background of globalization and planetary urbanization. However, spatial factors affecting the transmission of the disease in urban spaces remain unclear. Based on geotagged COVID-19 cases from social media data in the early stage of the pandemic, this study explored the correlation between different infectious outcomes of COVID-19 transmission and various factors of the urban environment in the main urban area of Wuhan, utilizing the multiple regression model. The result shows that most spatial factors were strongly correlated to case aggregation areas of COVID-19 in terms of population density, human mobility and environmental quality, which provides urban planners and administrators valuable insights for building healthy and safe cities in an uncertain future.
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spelling pubmed-91015672022-05-14 Correlation Analysis between Urban Elements and COVID-19 Transmission Using Social Media Data Wang, Ru Liu, Lingbo Wu, Hao Peng, Zhenghong Int J Environ Res Public Health Article The outbreak of the COVID-19 has become a worldwide public health challenge for contemporary cities during the background of globalization and planetary urbanization. However, spatial factors affecting the transmission of the disease in urban spaces remain unclear. Based on geotagged COVID-19 cases from social media data in the early stage of the pandemic, this study explored the correlation between different infectious outcomes of COVID-19 transmission and various factors of the urban environment in the main urban area of Wuhan, utilizing the multiple regression model. The result shows that most spatial factors were strongly correlated to case aggregation areas of COVID-19 in terms of population density, human mobility and environmental quality, which provides urban planners and administrators valuable insights for building healthy and safe cities in an uncertain future. MDPI 2022-04-25 /pmc/articles/PMC9101567/ /pubmed/35564606 http://dx.doi.org/10.3390/ijerph19095208 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Ru
Liu, Lingbo
Wu, Hao
Peng, Zhenghong
Correlation Analysis between Urban Elements and COVID-19 Transmission Using Social Media Data
title Correlation Analysis between Urban Elements and COVID-19 Transmission Using Social Media Data
title_full Correlation Analysis between Urban Elements and COVID-19 Transmission Using Social Media Data
title_fullStr Correlation Analysis between Urban Elements and COVID-19 Transmission Using Social Media Data
title_full_unstemmed Correlation Analysis between Urban Elements and COVID-19 Transmission Using Social Media Data
title_short Correlation Analysis between Urban Elements and COVID-19 Transmission Using Social Media Data
title_sort correlation analysis between urban elements and covid-19 transmission using social media data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101567/
https://www.ncbi.nlm.nih.gov/pubmed/35564606
http://dx.doi.org/10.3390/ijerph19095208
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