ArticlePloS one2022
COVID-19 distributes socially in China: A Bayesian spatial analysis.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Analysis of COVID-19 outbreak in Hubei province based on Tencent's location big data.Frontiers in public health · 2023Article
- Polyvinylidene fluoride multi-scale nanofibrous membrane modified using N-halamine with high filtration efficiency and durable antibacterial properties for air filtration.Journal of colloid and interface science · 2022Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe 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.
methodsWe 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.
resultsGlobal Moran's I statistics of COVID-19 incidences was 0.31 (P<0.05). The areas with a high risk of COVID-19 were mainly located in the provinces around Hubei and the provinces with a high level of economic development. The relative risk of two socioeconomic factors, the per capita consumption expenditure of households and the proportion of the migrating population from Hubei, were 1.887 [95% confidence interval (CI): 1.469~2.399] and 1.099 (95% CI: 1.053~1.148), respectively. The two factors explained up to 78.2% out of 99.7% of structured spatial variations.
conclusionOur results suggested that COVID-19 risk was positively associated with the level of economic development and population movements. Blocking population movement and reducing local exposures are effective in preventing the local transmission of COVID-19.
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