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An AHP-based regional COVID-19 vulnerability model and its application in China

Since the COVID-19 outbreak, four cities—Wuhan, Beijing, Urumqi and Dalian—have experienced the process from outbreak to stabilization. According to the China Statistical Yearbook and China Center for Disease Control records, regional, pathological, medical and response attributes were selected as r...

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Autores principales: Gao, Zekun, Jiang, Yutong, He, Junyu, Wu, Jiaping, Xu, Jian, Christakos, George
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8317685/
https://www.ncbi.nlm.nih.gov/pubmed/34341768
http://dx.doi.org/10.1007/s40808-021-01244-y
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author Gao, Zekun
Jiang, Yutong
He, Junyu
Wu, Jiaping
Xu, Jian
Christakos, George
author_facet Gao, Zekun
Jiang, Yutong
He, Junyu
Wu, Jiaping
Xu, Jian
Christakos, George
author_sort Gao, Zekun
collection PubMed
description Since the COVID-19 outbreak, four cities—Wuhan, Beijing, Urumqi and Dalian—have experienced the process from outbreak to stabilization. According to the China Statistical Yearbook and China Center for Disease Control records, regional, pathological, medical and response attributes were selected as regional vulnerability factors of infectious diseases. Then the Analytic Hierarchy Process (AHP) method was used to build a regional vulnerability index model for the infectious disease. The influence of the COVID-19 outbreak at a certain place was assessed computationally in terms of the number of days of epidemic duration and cumulative number of infections, and then fitted to the city data. The resulting correlation coefficient was 0.999952. The range of the regional vulnerability index for COVID-19 virus was from 0.0513 to 0.9379. The vulnerability indexes of Wuhan, Urumqi, Beijing and Dalian were 0.8733, 0.1951, 0.1566 and 0.1119, respectively. The lack of understanding of the virus became the biggest breakthrough point for the rapid spread of the virus in Wuhan. Due to inadequate prevention and control measures, the city of Urumqi was unable to trace the source of infection and close contacts, resulting in a relatively large impact. Beijing has both high population density and migration rate, which imply that the disease outbreak in this city had a great impact. Dalian has perfect prevention and good regional attributes. In addition, the regional vulnerability index model was used to analyze other Chinese cities. Accordingly, the regional vulnerability index and the prevention and control suggestions for them were discussed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40808-021-01244-y.
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spelling pubmed-83176852021-07-29 An AHP-based regional COVID-19 vulnerability model and its application in China Gao, Zekun Jiang, Yutong He, Junyu Wu, Jiaping Xu, Jian Christakos, George Model Earth Syst Environ Original Article Since the COVID-19 outbreak, four cities—Wuhan, Beijing, Urumqi and Dalian—have experienced the process from outbreak to stabilization. According to the China Statistical Yearbook and China Center for Disease Control records, regional, pathological, medical and response attributes were selected as regional vulnerability factors of infectious diseases. Then the Analytic Hierarchy Process (AHP) method was used to build a regional vulnerability index model for the infectious disease. The influence of the COVID-19 outbreak at a certain place was assessed computationally in terms of the number of days of epidemic duration and cumulative number of infections, and then fitted to the city data. The resulting correlation coefficient was 0.999952. The range of the regional vulnerability index for COVID-19 virus was from 0.0513 to 0.9379. The vulnerability indexes of Wuhan, Urumqi, Beijing and Dalian were 0.8733, 0.1951, 0.1566 and 0.1119, respectively. The lack of understanding of the virus became the biggest breakthrough point for the rapid spread of the virus in Wuhan. Due to inadequate prevention and control measures, the city of Urumqi was unable to trace the source of infection and close contacts, resulting in a relatively large impact. Beijing has both high population density and migration rate, which imply that the disease outbreak in this city had a great impact. Dalian has perfect prevention and good regional attributes. In addition, the regional vulnerability index model was used to analyze other Chinese cities. Accordingly, the regional vulnerability index and the prevention and control suggestions for them were discussed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40808-021-01244-y. Springer International Publishing 2021-07-28 2022 /pmc/articles/PMC8317685/ /pubmed/34341768 http://dx.doi.org/10.1007/s40808-021-01244-y Text en © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Article
Gao, Zekun
Jiang, Yutong
He, Junyu
Wu, Jiaping
Xu, Jian
Christakos, George
An AHP-based regional COVID-19 vulnerability model and its application in China
title An AHP-based regional COVID-19 vulnerability model and its application in China
title_full An AHP-based regional COVID-19 vulnerability model and its application in China
title_fullStr An AHP-based regional COVID-19 vulnerability model and its application in China
title_full_unstemmed An AHP-based regional COVID-19 vulnerability model and its application in China
title_short An AHP-based regional COVID-19 vulnerability model and its application in China
title_sort ahp-based regional covid-19 vulnerability model and its application in china
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8317685/
https://www.ncbi.nlm.nih.gov/pubmed/34341768
http://dx.doi.org/10.1007/s40808-021-01244-y
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