Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText.
Kick-your-searchterm to multiple Engines kick-your-query now !>
A dictionary by aggregated review articles of nephrology, medicine and the life sciences
Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

suck abstract from ncbi


10.1016/j.scs.2020.102418

http://scihub22266oqcxt.onion/10.1016/j.scs.2020.102418
suck pdf from google scholar
32834939!7395296!32834939
unlimited free pdf from europmc32834939    free
PDF from PMC    free
html from PMC    free

Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32834939&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215

suck abstract from ncbi

pmid32834939      Sustain+Cities+Soc 2020 ; 62 (?): 102418
Nephropedia Template TP

gab.com Text

Twit Text FOAVip

Twit Text #

English Wikipedia


  • Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach #MMPMID32834939
  • Sannigrahi S; Pilla F; Basu B; Basu AS; Molter A
  • Sustain Cities Soc 2020[Nov]; 62 (?): 102418 PMID32834939show ga
  • 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.
  • ?


  • DeepDyve
  • Pubget Overpricing
  • suck abstract from ncbi

    Linkout box