Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach

Abstract

The socio-demographic factors have a substantial impact on the overall casualties caused by the Coronavirus (COVID-19). In this study, the global and local spatial association between the key socio-demographic variables and COVID-19 cases and deaths in the European regions were analyzed using the spatial regression models. A total of 31 European countries were selected for modelling and subsequent analysis. From the initial 28 socio-demographic variables, a total of 2 (for COVID-19 cases) and 3 (for COVID-19 deaths) key variables were filtered out for the regression modelling. The spatially explicit regression modelling and mapping were done using four spatial regression models such as Geographically Weighted Regression (GWR), Spatial Error Model (SEM), Spatial Lag Model (SLM), and Ordinary Least Square (OLS). Additionally, Partial Least Square (PLS) and Principal Component Regression (PCR) was performed to estimate the overall explanatory power of the regression models. For the COVID cases, the local R² values, which suggesting the influences of the selected socio-demographic variables on COVID cases and death, were found highest in Germany, Austria, Slovenia, Switzerland, Italy. The moderate local R² was observed for Luxembourg, Poland, Denmark, Croatia, Belgium, Slovakia. The lowest local R² value for COVID-19 cases was accounted for Ireland, Portugal, United Kingdom, Spain, Cyprus, Romania. Among the 2 variables, the highest local R² was calculated for income (R² = 0.71), followed by poverty (R² = 0.45). For the COVID deaths, the highest association was found in Italy, Croatia, Slovenia, Austria. The moderate association was documented for Hungary, Greece, Switzerland, Slovakia, and the lower association was found in the United Kingdom, Ireland, Netherlands, Cyprus. This suggests that the selected demographic and socio-economic components, including total population, poverty, income, are the key factors in regulating overall casualties of COVID-19 in the European region. In this study, the influence of the other controlling factors, such as environmental conditions, socio-ecological status, climatic extremity, etc. have not been considered. This could be the scope for future research.

Keywords: COVID-19; Demography; Outbreak; Pandemic; Spatial regression; Virus.

Fig. 1

The spatial distribution of COVID-19 cases and deaths across Europe.

Fig. 2

The distribution of median age of population, COVID case intensity, COVID death intensity in different European countries.

Fig. 3

The individual effect of the demographic variables on the COVID-19 case derived from the GWR model.

Fig. 4

The spatial varying local regression coefficient estimated for the COVID case factor.

Fig. 5

The individual effect of the demographic variables on COVID-19 death derived from the GWR model.

Fig. 6

The spatial varying local regression coefficient estimated for the COVID death factor.

Fig. 7

Spatial distribution of local correlation coefficient, local condition number, local variation decomposition proportions, and variance inflation factors, estimated from local GWR model.

Fig. 8

The predicted values of COVID-19 case and death derived from the GWR model.

Fig. 9

The linear association between socio-demographic components and COVID case/death counts.

Fig. 10

The linear association between 3 socio-demographic variables and COVID-19 case/death.

Fig. 11

The correlation between the selected socio-demographic variables and COVID-19 case/death. TotPop - total population.

Fig. 12

Global Moran's I spatial dependency test for evaluating the statistical significance of spatial clusters of the distribution.