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Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach.
Sannigrahi, Srikanta; Pilla, Francesco; Basu, Bidroha; Basu, Arunima Sarkar; Molter, Anna.
Afiliação
  • Sannigrahi S; School of Architecture, Planning and Environmental Policy, University College Dublin, Richview, Clonskeagh, Dublin, D14 E099, Ireland.
  • Pilla F; School of Architecture, Planning and Environmental Policy, University College Dublin, Richview, Clonskeagh, Dublin, D14 E099, Ireland.
  • Basu B; School of Architecture, Planning and Environmental Policy, University College Dublin, Richview, Clonskeagh, Dublin, D14 E099, Ireland.
  • Basu AS; School of Architecture, Planning and Environmental Policy, University College Dublin, Richview, Clonskeagh, Dublin, D14 E099, Ireland.
  • Molter A; School of Architecture, Planning and Environmental Policy, University College Dublin, Richview, Clonskeagh, Dublin, D14 E099, Ireland.
Sustain Cities Soc ; 62: 102418, 2020 Nov.
Article em En | MEDLINE | ID: mdl-32834939
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.
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Texto completo: 1 Bases de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sustain Cities Soc Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Irlanda

Texto completo: 1 Bases de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sustain Cities Soc Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Irlanda