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.

References 62

1. 1.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1016/j.jue.2020.103293

2. 2.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

3. 3.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

4. 4.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1016/j.scitotenv.2020.138835

5. 5.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1302/0301-620x.83b1.10699

6. 6.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.2139/ssrn.3572329

7. 7.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1111/j.1538-4632.1996.tb00936.x

8. 8.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

9. 9.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

10. 10.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

11. 11.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1007/s11113-007-9051-8

12. 12.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

13. 13.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1089/vbz.2006.6.283

14. 14.
    Demographic science aids in understanding the spread and fatality rates of COVID-19 · 2020

    No notice
15. 15.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1016/s2215-0366(20)30073-0

16. 16.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.3390/su71115570

17. 17.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

18. 18.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

19. 19.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.2139/ssrn.3551430

20. 20.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

21. 21.
    The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2 · 2020

    No notice
22. 22.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

23. 23.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

24. 24.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1016/j.jum.2019.09.004

25. 25.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1038/s41586-020-2284-y

26. 26.
    The Spatial Distribution of Hepatitis C Virus Infections and Associated Determinants--An Application of a Geographically Weighted Poisson Regression for Evidence-Based Screening Interventions in Hotspots. · 2015

    Not checked
27. 27.
    The effect of human mobility and control measures on the COVID-19 epidemic in China · 2020

    No notice
28. 28.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1101/2020.04.16.20067991

29. 29.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1016/j.jpainsymman.2020.03.041

30. 30.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1186/1742-7622-10-4

31. 31.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1186/1476-072x-6-15

32. 32.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1002/jmv.25860

33. 33.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.14744/ejmo.2019.81104

34. 34.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

35. 35.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

36. 36.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1016/j.apgeog.2018.11.001

37. 37.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1016/j.scitotenv.2020.138226

38. 38.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1111/tbed.13553

39. 39.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1101/2020.04.09.20056341

40. 40.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1093/aje/152.10.908

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