  The demographic factors have a substantial impact on the overall casualties
caused by the COVID-19. In this study, the spatial association between the key
demographic variables and COVID-19 cases and deaths were analyzed using the
spatial regression models. Total 13 (for COVID-19 case factor) and 8 (for
COVID-19 death factor) key variables were considered for the modelling. Total
five spatial regression models such as Geographically weighted regression
(GWR), Spatial Error Model (SEM), Spatial Lag Model (SLM), Spatial Error_Lag
model (SEM_SLM), and Ordinary Least Square (OLS) were performed for the spatial
modelling and mapping of model estimates. The local R2 values, which suggesting
the influences of the selected demographic variables on overall casualties
caused by COVID-19, was found highest in Italy and the UK. The moderate local
R2 was observed for France, Belgium, Netherlands, Ireland, Denmark, Norway,
Sweden, Poland, Slovakia, and Romania. The lowest local R2 value for COVID-19
cases was accounted for Latvia and Lithuania. Among the 13 variables, the
highest local R2 was calculated for total population (R2 = 0.92), followed by
death crude death rate (R2 = 0.9), long time illness (R2 = 0.84), population
with age >80 (R2 = 0.59), employment (R2 = 0.46), life expectancy at 65 (R2 =
0.34), crude birth rate (R2 = 0.31), life expectancy (R2 = 0.31), Population
with age 65-80 (R2 = 0.29), Population with age 15-24 (R2 = 0.27), Population
with age 25-49 (R2 = 0.27), Population with age 0-14 (R2 = 0.23), and
Population with age 50-65 (R2 = 0.23), respectively.
