Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach. (November 2020)

• Record Type: Journal Article
• Authors: Sannigrahi, Srikanta; Pilla, Francesco; Basu, Bidroha; Basu, Arunima Sarkar; Molter, Anna
• Is Part Of: Sustainable cities and society. Volume 62(2020)
• Publication Date: 2020-11
• Subjects: COVID-19 -- Virus -- Outbreak -- Pandemic -- Spatial regression -- Demography; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2020.102418
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: The spatial association between socio-demographic composition and COVID-19 deaths and cases were evaluated. Four spatial regression models were implemented for spatial regression modelling. Socio-demographic composition significantly impacting the overall casualties caused by COVID-19. The spatially predicted COVID-19 cases and deaths were found highly consistent with actual estimates. 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 R 2 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 R 2 was observed for Luxembourg, Poland, Denmark, Croatia, Belgium, Slovakia. The lowest local R 2 value for COVID-19 cases was accounted for Ireland, Portugal, United Kingdom, Spain, Cyprus, Romania. Among the 2 variables, the highest local R 2 was calculated for income (R 2 = 0.71), followed by poverty (R 2 = 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.