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

Abstract

Purpose 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. Methods 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. Results Global 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. Conclusion Our 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.

Polyvinylidene fluoride multi-scale nanofibrous membrane modified using N-halamine with high filtration efficiency and durable antibacterial properties for air filtration

“…Statistics published by the World Health Organization (WHO) show that more than four million premature human deaths are caused annually by the inhalation of fine particles [1] , [2] , [3] , [4] . In addition, more than six million people have died owing to the ongoing coronavirus (COVID-19) pandemic [5] , [6] , [7] , [8] . Personal protective masks offer the most direct and effective protection in daily life [9] , [10] .…”

Analysis of the Spatiotemporal Variations and Influencing Factors of COVID-19 in Various Cities in East China: Bayesian Spatio-Temporal Model

“…For example, Peng et al employed global autocorrelation analysis to elucidate the spatial pattern of COVID-19. Furthermore, they investigated the association between COVID-19 and diverse factors [2] . Ever since the onset of the COVID-19 pandemic, multiple studies have shown that various influencing factors have different effects on the spread and spatiotemporal distribution of COVID-19.…”

The Interplay Between Clusters, Covariates, and Spatial Priors in Spatial Modelling of Covid-19 in South Sulawesi Province, Indonesia

“…A number of previous studies have modelled Covid-19 using a Bayesian spatial Conditional Autoregressive (CAR) considering socioeconomic influences. The association of socioeconomic, and environmental variables with the incidence of Covid-19 in the 30 provinces in mainland China has been identified using a Bayesian spatial CAR Besag, York & Mollié (BYM) model (Peng et al, 2022). Their results suggested that the risk of Covid-19 was positively correlated with the economic development level and population movements.…”