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10.22038/abjs.2020.48515.2404 http://scihub22266oqcxt.onion/10.22038/abjs.2020.48515.2404 34026940!8121034!34026940     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Arch+Bone+Jt+Surg 2021 ; 9 (2): 217-223 Nephropedia Template TP {{tp|p=34026940|t=2021. Mobile Phone GPS Data and Prevalence of COVID-19 Infections: Quantifying Parameters of Social Distancing in the U S |pdf=|usr=}}{{34026940}} gab.com Text Mobile Phone GPS Data and Prevalence of COVID-19 Infections: Quantifying Parameters of Social Distancing in the U S JO: Arch Bone Jt Surg http://www.kidney.de/pget.php?&tw=1&pmid=34026940 #FOAvip BACKGROUND: To evaluate the association between social distancing quantified by mobile phone data and the current prevalence of COVID-19 infections in the U.S. per capita. METHODS: Data were accessed on April 4, 2020, from Centers for Disease Control and Prevention, Google COVID-19 Community Mobility Report, and the United States Census Bureau to report prevalence of COVID-19 infections, mobility data, and population per state, respectively. Mobility data points were defined as daily length of visit or time spent in a single location based on mobile phone users shared locations from February 7 - March 29, 2020. Multivariable linear regression was used to evaluate relationships between normalized per capita infection prevalence and six parameters of social distancing. RESULTS: Mobility data indicated the following percent changes compared to median values of baseline activity: -50% in transit stations, -45% in retail/recreation, -36% in workplaces, -23% in grocery/pharmacy, -19% in parks, and +12% in residential living areas. Multivariable linear regression revealed significant correlation between prevalence of infection per capita and parameters of social distancing (R= 0.604, P= 0.002). Time at home was not an independent predictor for prevalence of infection per capita (beta= 0.016; 95% CI, -0.003 to 0.036; P= 0.09). CONCLUSION: Based on mobility reports from mobile phone GPS data and six characteristics of social distancing, significant associations were identified between geographic activity and prevalence of COVID-19 infections in the U.S. per capita. Mobile phone data utilizing 'location history' may be warranted to monitor the effectiveness of social distancing parameters on reducing prevalence of COVID-19 in the U.S. Twit Text FOAVip Mobile Phone GPS Data and Prevalence of COVID-19 Infections: Quantifying Parameters of Social Distancing in the U S ????Arch Bone Jt Surg http://www.kidney.de/pget.php?&tw=1&pmid=34026940 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34026940 #FOAvip English Wikipedia <ref>{{Cite pmid|34026940}}</ref> Mobile Phone GPS Data and Prevalence of COVID-19 Infections: Quantifying Parameters of Social Distancing in the U S #MMPMID34026940 DePhillipo NN; Chahla J; Busler M; LaPrade RF Arch Bone Jt Surg 2021[Mar]; 9 (2): 217-223 PMID34026940show ga BACKGROUND: To evaluate the association between social distancing quantified by mobile phone data and the current prevalence of COVID-19 infections in the U.S. per capita. METHODS: Data were accessed on April 4, 2020, from Centers for Disease Control and Prevention, Google COVID-19 Community Mobility Report, and the United States Census Bureau to report prevalence of COVID-19 infections, mobility data, and population per state, respectively. Mobility data points were defined as daily length of visit or time spent in a single location based on mobile phone users shared locations from February 7 - March 29, 2020. Multivariable linear regression was used to evaluate relationships between normalized per capita infection prevalence and six parameters of social distancing. RESULTS: Mobility data indicated the following percent changes compared to median values of baseline activity: -50% in transit stations, -45% in retail/recreation, -36% in workplaces, -23% in grocery/pharmacy, -19% in parks, and +12% in residential living areas. Multivariable linear regression revealed significant correlation between prevalence of infection per capita and parameters of social distancing (R= 0.604, P= 0.002). Time at home was not an independent predictor for prevalence of infection per capita (beta= 0.016; 95% CI, -0.003 to 0.036; P= 0.09). CONCLUSION: Based on mobility reports from mobile phone GPS data and six characteristics of social distancing, significant associations were identified between geographic activity and prevalence of COVID-19 infections in the U.S. per capita. Mobile phone data utilizing 'location history' may be warranted to monitor the effectiveness of social distancing parameters on reducing prevalence of COVID-19 in the U.S. äMobile Phone GPS Data and Prevalence of COVID-19 Infections: Quantifyi(epmc).pdf DeepDyve Pubget Overpricing