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Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China

Few study has revealed spatial transmission characteristics of COVID-19 in Wuhan, China. We aimed to analyze the spatiotemporal spread of COVID-19 in Wuhan and its influence factors. Information of 32,682 COVID-19 cases reported through March 18 were extracted from the national infectious disease su...

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Autores principales: Liu, Wei, Wang, Dongming, Hua, Shuiqiong, Xie, Cong, Wang, Bin, Qiu, Weihong, Xu, Tao, Ye, Zi, Yu, Linling, Yang, Meng, Xiao, Yang, Feng, Xiaobing, Shi, Tingming, Li, Mingyan, Chen, Weihong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8249501/
https://www.ncbi.nlm.nih.gov/pubmed/34211038
http://dx.doi.org/10.1038/s41598-021-93020-2
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author Liu, Wei
Wang, Dongming
Hua, Shuiqiong
Xie, Cong
Wang, Bin
Qiu, Weihong
Xu, Tao
Ye, Zi
Yu, Linling
Yang, Meng
Xiao, Yang
Feng, Xiaobing
Shi, Tingming
Li, Mingyan
Chen, Weihong
author_facet Liu, Wei
Wang, Dongming
Hua, Shuiqiong
Xie, Cong
Wang, Bin
Qiu, Weihong
Xu, Tao
Ye, Zi
Yu, Linling
Yang, Meng
Xiao, Yang
Feng, Xiaobing
Shi, Tingming
Li, Mingyan
Chen, Weihong
author_sort Liu, Wei
collection PubMed
description Few study has revealed spatial transmission characteristics of COVID-19 in Wuhan, China. We aimed to analyze the spatiotemporal spread of COVID-19 in Wuhan and its influence factors. Information of 32,682 COVID-19 cases reported through March 18 were extracted from the national infectious disease surveillance system. Geographic information system methods were applied to analysis transmission of COVID-19 and its influence factors in different periods. We found decrease in effective reproduction number (Rt) and COVID-19 related indicators through taking a series of effective public health measures including restricting traffic, centralized quarantine and strict stay-at home policy. The distribution of COVID-19 cases number in Wuhan showed obvious global aggregation and local aggregation. In addition, the analysis at streets-level suggested population density and the number of hospitals were associated with COVID-19 cases number. The epidemic situation showed obvious global and local spatial aggregations. High population density with larger number of hospitals may account for the aggregations. The epidemic in Wuhan was under control in a short time after strong quarantine measures and restrictions on movement of residents were implanted.
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spelling pubmed-82495012021-07-06 Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China Liu, Wei Wang, Dongming Hua, Shuiqiong Xie, Cong Wang, Bin Qiu, Weihong Xu, Tao Ye, Zi Yu, Linling Yang, Meng Xiao, Yang Feng, Xiaobing Shi, Tingming Li, Mingyan Chen, Weihong Sci Rep Article Few study has revealed spatial transmission characteristics of COVID-19 in Wuhan, China. We aimed to analyze the spatiotemporal spread of COVID-19 in Wuhan and its influence factors. Information of 32,682 COVID-19 cases reported through March 18 were extracted from the national infectious disease surveillance system. Geographic information system methods were applied to analysis transmission of COVID-19 and its influence factors in different periods. We found decrease in effective reproduction number (Rt) and COVID-19 related indicators through taking a series of effective public health measures including restricting traffic, centralized quarantine and strict stay-at home policy. The distribution of COVID-19 cases number in Wuhan showed obvious global aggregation and local aggregation. In addition, the analysis at streets-level suggested population density and the number of hospitals were associated with COVID-19 cases number. The epidemic situation showed obvious global and local spatial aggregations. High population density with larger number of hospitals may account for the aggregations. The epidemic in Wuhan was under control in a short time after strong quarantine measures and restrictions on movement of residents were implanted. Nature Publishing Group UK 2021-07-01 /pmc/articles/PMC8249501/ /pubmed/34211038 http://dx.doi.org/10.1038/s41598-021-93020-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Liu, Wei
Wang, Dongming
Hua, Shuiqiong
Xie, Cong
Wang, Bin
Qiu, Weihong
Xu, Tao
Ye, Zi
Yu, Linling
Yang, Meng
Xiao, Yang
Feng, Xiaobing
Shi, Tingming
Li, Mingyan
Chen, Weihong
Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China
title Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China
title_full Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China
title_fullStr Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China
title_full_unstemmed Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China
title_short Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China
title_sort spatiotemporal analysis of covid-19 outbreaks in wuhan, china
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8249501/
https://www.ncbi.nlm.nih.gov/pubmed/34211038
http://dx.doi.org/10.1038/s41598-021-93020-2
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