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Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach
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 sp...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7395296/ https://www.ncbi.nlm.nih.gov/pubmed/32834939 http://dx.doi.org/10.1016/j.scs.2020.102418 |
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author | Sannigrahi, Srikanta Pilla, Francesco Basu, Bidroha Basu, Arunima Sarkar Molter, Anna |
author_facet | Sannigrahi, Srikanta Pilla, Francesco Basu, Bidroha Basu, Arunima Sarkar Molter, Anna |
author_sort | Sannigrahi, Srikanta |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7395296 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Author(s). Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73952962020-08-03 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 Sustain Cities Soc Article 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. The Author(s). Published by Elsevier Ltd. 2020-11 2020-08-01 /pmc/articles/PMC7395296/ /pubmed/32834939 http://dx.doi.org/10.1016/j.scs.2020.102418 Text en © 2020 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Sannigrahi, Srikanta Pilla, Francesco Basu, Bidroha Basu, Arunima Sarkar Molter, Anna Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach |
title | Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach |
title_full | Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach |
title_fullStr | Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach |
title_full_unstemmed | Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach |
title_short | Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach |
title_sort | examining the association between socio-demographic composition and covid-19 fatalities in the european region using spatial regression approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7395296/ https://www.ncbi.nlm.nih.gov/pubmed/32834939 http://dx.doi.org/10.1016/j.scs.2020.102418 |
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