Evidence map›Paper›PMID 34341768›Full record

ArticleModeling earth systems and environment2022

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

Zekun Gao, Yutong Jiang, Junyu He, Jiaping Wu, Jian Xu, George Christakos

Abstract read
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Article in Modeling earth systems and environment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

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9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zekun GaoOcean College, Zhejiang University, Zhoushan, 316021 China.
Yutong JiangOcean College, Zhejiang University, Zhoushan, 316021 China.
Junyu HeOcean College, Zhejiang University, Zhoushan, 316021 China.
Jiaping WuOcean College, Zhejiang University, Zhoushan, 316021 China.
Jian XuDepartment of Geography, San Diego State University, San Diego, CA 92182 USA.
George ChristakosOcean College, Zhejiang University, Zhoushan, 316021 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AHPCorrelation coefficientCOVID-19Regional disease vulnerability index

Identifiers

PMID34341768
PMCPMC8317685

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.