COVID-19 distributes socially in China: A Bayesian spatial analysis

The ongoing coronavirus disease 2019 (COVID-19) epidemic increasingly threatens the public health security worldwide. We aimed to identify high-risk areas of COVID-19 and understand how socioeconomic factors are associated with the spatial distribution of COVID-19 in China, which may help other coun...

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Bibliographic Details
Published in:PloS one 2022-04, Vol.17 (4), p.e0267001-e0267001
Main Authors: Peng, Di, Qian, Jian, Wei, Luyi, Luo, Caiying, Zhang, Tao, Zhou, Lijun, Liu, Yuanyuan, Ma, Yue, Yin, Fei
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Bayesian statistical decision theory
China - epidemiology
Cold
Computer and Information Sciences
Confidence intervals
Coronaviruses
COVID-19
COVID-19 - epidemiology
Disease control
Disease transmission
Earth Sciences
Economic analysis
Economic development
Economic models
Epidemics
Evaluation
Funding
Health risks
Hospitals
Households
Humans
Mathematical models
Medicine and Health Sciences
Pandemics
People and Places
Population
Provinces
Public health
Risk
Risk factors
SARS-CoV-2
Security
Social factors
Social Sciences
Socioeconomic factors
Socioeconomics
Spatial Analysis
Spatial distribution
Spatial variations
Statistical analysis
Variables
Viral diseases
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Summary:The ongoing coronavirus disease 2019 (COVID-19) epidemic increasingly threatens the public health security worldwide. We aimed to identify high-risk areas of COVID-19 and understand how socioeconomic factors are associated with the spatial distribution of COVID-19 in China, which may help other countries control the epidemic. We analyzed the data of COVID-19 cases from 30 provinces in mainland China (outside of Hubei) from 16 January 2020 to 31 March 2020, considering the data of demographic, economic, health, and transportation factors. Global autocorrelation analysis and Bayesian spatial models were used to present the spatial pattern of COVID-19 and explore the relationship between COVID-19 risk and various factors. Global Moran's I statistics of COVID-19 incidences was 0.31 (P
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0267001