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.

10.1016/j.ehb.2021.100988 http://scihub22266oqcxt.onion/10.1016/j.ehb.2021.100988 33636583!8054145!33636583     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Econ+Hum+Biol 2021 ; 41 (ä): 100988 Nephropedia Template TP {{tp|p=33636583|t=2021. Early warning of vulnerable counties in a pandemic using socio-economic variables |pdf=|usr=}}{{33636583}} gab.com Text Early warning of vulnerable counties in a pandemic using socio-economic variables JO: Econ Hum Biol http://www.kidney.de/pget.php?&tw=1&pmid=33636583 #FOAvip In the U.S. in early 2020, heterogenous and incomplete county-scale data on COVID-19 hindered effective interventions in the pandemic. While numbers of deaths can be used to estimate actual number of infections after a time lag, counties with low death counts early on have considerable uncertainty about true numbers of cases in the future. Here we show that supplementing county-scale mortality statistics with socioeconomic data helps estimate true numbers of COVID-19 infections in low-data counties, and hence provide an early warning of future concern. We fit a LASSO negative binomial regression to select a parsimonious set of five predictive variables from thirty-one county-level covariates. Of these, population density, public transportation use, voting patterns and % African-American population are most predictive of higher COVID-19 death rates. To test the model, we show that counties identified as under-estimating COVID-19 on an early date (April 17) have relatively higher deaths later (July 1) in the pandemic. Twit Text FOAVip Early warning of vulnerable counties in a pandemic using socio-economic variables ????Econ Hum Biol http://www.kidney.de/pget.php?&tw=1&pmid=33636583 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33636583 #FOAvip English Wikipedia <ref>{{Cite pmid|33636583}}</ref> Early warning of vulnerable counties in a pandemic using socio-economic variables #MMPMID33636583 Ruck DJ; Bentley RA; Borycz J Econ Hum Biol 2021[May]; 41 (ä): 100988 PMID33636583show ga In the U.S. in early 2020, heterogenous and incomplete county-scale data on COVID-19 hindered effective interventions in the pandemic. While numbers of deaths can be used to estimate actual number of infections after a time lag, counties with low death counts early on have considerable uncertainty about true numbers of cases in the future. Here we show that supplementing county-scale mortality statistics with socioeconomic data helps estimate true numbers of COVID-19 infections in low-data counties, and hence provide an early warning of future concern. We fit a LASSO negative binomial regression to select a parsimonious set of five predictive variables from thirty-one county-level covariates. Of these, population density, public transportation use, voting patterns and % African-American population are most predictive of higher COVID-19 death rates. To test the model, we show that counties identified as under-estimating COVID-19 on an early date (April 17) have relatively higher deaths later (July 1) in the pandemic. |*Socioeconomic Factors[MESH] |Black or African American/*statistics & numerical data[MESH] |COVID-19/*epidemiology/mortality[MESH] |Humans[MESH] |Pandemics[MESH] |SARS-CoV-2[MESH] |Small-Area Analysis[MESH]Early warning of vulnerable counties in a pandemic using socio-economi(epmc).pdf DeepDyve Pubget Overpricing