tetano
Editor, Senior Moderator
BMC Infect Dis
. 2021 May 7;21(1):428.
doi: 10.1186/s12879-021-06128-1.
The effect of sociodemographic factors on COVID-19 incidence of 342 cities in China: a geographically weighted regression model analysis
Huihui Zhang[SUP] 1 [/SUP], Yini Liu[SUP] 1 [/SUP], Fangyao Chen[SUP] 1 [/SUP], Baibing Mi[SUP] 1 [/SUP], Lingxia Zeng[SUP] 1 [/SUP], Leilei Pei[SUP] 2 [/SUP]
Affiliations
Abstract
Background: Since December 2019, the coronavirus disease 2019 (COVID-19) has spread quickly among the population and brought a severe global impact. However, considerable geographical disparities in the distribution of COVID-19 incidence existed among different cities. In this study, we aimed to explore the effect of sociodemographic factors on COVID-19 incidence of 342 cities in China from a geographic perspective.
Methods: Official surveillance data about the COVID-19 and sociodemographic information in China's 342 cities were collected. Local geographically weighted Poisson regression (GWPR) model and traditional generalized linear models (GLM) Poisson regression model were compared for optimal analysis.
Results: Compared to that of the GLM Poisson regression model, a significantly lower corrected Akaike Information Criteria (AICc) was reported in the GWPR model (61953.0 in GLM vs. 43218.9 in GWPR). Spatial auto-correlation of residuals was not found in the GWPR model (global Moran's I = - 0.005, p = 0.468), inferring the capture of the spatial auto-correlation by the GWPR model. Cities with a higher gross domestic product (GDP), limited health resources, and shorter distance to Wuhan, were at a higher risk for COVID-19. Furthermore, with the exception of some southeastern cities, as population density increased, the incidence of COVID-19 decreased.
Conclusions: There are potential effects of the sociodemographic factors on the COVID-19 incidence. Moreover, our findings and methodology could guide other countries by helping them understand the local transmission of COVID-19 and developing a tailored country-specific intervention strategy.
Keywords: COVID-19; Sociodemographic factor; Spatial distribution; Spatial modeling.
. 2021 May 7;21(1):428.
doi: 10.1186/s12879-021-06128-1.
The effect of sociodemographic factors on COVID-19 incidence of 342 cities in China: a geographically weighted regression model analysis
Huihui Zhang[SUP] 1 [/SUP], Yini Liu[SUP] 1 [/SUP], Fangyao Chen[SUP] 1 [/SUP], Baibing Mi[SUP] 1 [/SUP], Lingxia Zeng[SUP] 1 [/SUP], Leilei Pei[SUP] 2 [/SUP]
Affiliations
- PMID: 33962576
- DOI: 10.1186/s12879-021-06128-1
Abstract
Background: Since December 2019, the coronavirus disease 2019 (COVID-19) has spread quickly among the population and brought a severe global impact. However, considerable geographical disparities in the distribution of COVID-19 incidence existed among different cities. In this study, we aimed to explore the effect of sociodemographic factors on COVID-19 incidence of 342 cities in China from a geographic perspective.
Methods: Official surveillance data about the COVID-19 and sociodemographic information in China's 342 cities were collected. Local geographically weighted Poisson regression (GWPR) model and traditional generalized linear models (GLM) Poisson regression model were compared for optimal analysis.
Results: Compared to that of the GLM Poisson regression model, a significantly lower corrected Akaike Information Criteria (AICc) was reported in the GWPR model (61953.0 in GLM vs. 43218.9 in GWPR). Spatial auto-correlation of residuals was not found in the GWPR model (global Moran's I = - 0.005, p = 0.468), inferring the capture of the spatial auto-correlation by the GWPR model. Cities with a higher gross domestic product (GDP), limited health resources, and shorter distance to Wuhan, were at a higher risk for COVID-19. Furthermore, with the exception of some southeastern cities, as population density increased, the incidence of COVID-19 decreased.
Conclusions: There are potential effects of the sociodemographic factors on the COVID-19 incidence. Moreover, our findings and methodology could guide other countries by helping them understand the local transmission of COVID-19 and developing a tailored country-specific intervention strategy.
Keywords: COVID-19; Sociodemographic factor; Spatial distribution; Spatial modeling.