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. 2022 Mar 6;7(3):45.
doi: 10.3390/tropicalmed7030045.

The Geographical Distribution and Influencing Factors of COVID-19 in China

Weiwei Li  1 , Ping Zhang  2   3 , Kaixu Zhao  4 , Sidong Zhao  5
Affiliations

The Geographical Distribution and Influencing Factors of COVID-19 in China

Weiwei Li et al. Trop Med Infect Dis. .

Abstract

The study of the spatial differentiation of COVID-19 in cities and its driving mechanism is helpful to reveal the spatial distribution pattern, transmission mechanism and diffusion model, and evolution mechanism of the epidemic and can lay the foundation for constructing the spatial dynamics model of the epidemic and provide theoretical basis for the policy design, spatial planning and implementation of epidemic prevention and control and social governance. Geodetector (Origin version, Beijing, China) is a great tool for analysis of spatial differentiation and its influencing factors, and it provides decision support for differentiated policy design and its implementation in executing the city-specific policies. Using factor detection and interaction analysis of Geodetector, 15 indicators of economic, social, ecological, and environmental dimensions were integrated, and 143 cities were selected for the empirical research in China. The research shows that, first of all, risks of both infection and death show positive spatial autocorrelation, but the geographical distribution of local spatial autocorrelation differs significantly between the two. Secondly, the inequalities in urban economic, social, and residential environments interact with COVID-19 spatial heterogeneity, with stronger explanatory power especially when multidimensional inequalities are superimposed. Thirdly, the spatial distribution and spread of COVID-19 are highly spatially heterogeneous and correlated due to the complex influence of multiple factors, with factors such as Area of Urban Construction Land, GDP, Industrial Smoke and Dust Emission, and Expenditure having the strongest influence, the factors such as Area of Green, Number of Hospital Beds and Parks, and Industrial NOx Emissions having unignorable influence, while the factors such as Number of Free Parks and Industrial Enterprises, Per-GDP, and Population Density play an indirect role mainly by means of interaction. Fourthly, the factor interaction effect from the infected person's perspective mainly shows a nonlinear enhancement effect, that is, the joint influence of the two factors is greater than the sum of their direct influences; but from the perspective of the dead, it mainly shows a two-factor enhancement effect, that is, the joint influence of the two factors is greater than the maximum of their direct influences but less than their sum. Fifthly, some suggestions are put forward from the perspectives of building a healthy, resilient, safe, and smart city, providing valuable reference and decision basis for city governments to carry out differentiated policy design.

Keywords: COVID-19; China; infectious diseases; spatial distribution; urban inequalities.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Study Area.
Figure 2
Figure 2
Research framework and steps.
Figure 3
Figure 3
Spatial heterogeneity analysis of COVID-19 in China cities. Note: CV stands for coefficient of variation and GI stands for Gini index.
Figure 4
Figure 4
Cluster analysis of COVID-19 in China cities.
Figure 5
Figure 5
Spatial autocorrelation analysis of COVID-19 in China cities.
Figure 6
Figure 6
Standard deviation ellipse analysis of COVID-19 in China cities.
Figure 7
Figure 7
Analysis of interaction detector. (Y1), (Y2): Number of Patients and Deaths.
Figure 8
Figure 8
Driving mechanism of spatial heterogeneity.
See this image and copyright information in PMC

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References

    1. Holmager T.L.F., Lynge E., Kann C.E., St-Martin G. Geography of COVID-19 in Denmark. Scand. J. Public Health. 2021;49:88–95. doi: 10.1177/1403494820975607. - DOI - PMC - PubMed
    1. Giuliani D., Dickson M.M., Espa G., Santi F. Modelling and predicting the spatio-temporal spread of COVID-19 in Italy. BMC Infect. Dis. 2020;20:700. doi: 10.1186/s12879-020-05415-7. - DOI - PMC - PubMed
    1. Thakar V. Unfolding Events in Space and Time: Geospatial Insights into COVID-19 Diffusion in Washington State during the Initial Stage of the Outbreak. ISPRS Int. J. Geo-Inf. 2020;9:382. doi: 10.3390/ijgi9060382. - DOI
    1. Hagenaars T.J., Donnelly C.A., Ferguson N.M. Spatial heterogeneity and the persistence of infectious diseases. J. Theor. Biol. 2004;229:349–359. doi: 10.1016/j.jtbi.2004.04.002. - DOI - PubMed
    1. Guliyev H. Determining the spatial effects of COVID-19 using the spatial panel data model. Spat. Stat. 2020;38 doi: 10.1016/j.spasta.2020.100443. - DOI - PMC - PubMed

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