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10.1073/pnas.2013906118 http://scihub22266oqcxt.onion/10.1073/pnas.2013906118 33441450!7865174!33441450     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Proc+Natl+Acad+Sci+U+S+A 2021 ; 118 (5): ä Nephropedia Template TP {{tp|p=33441450|t=2021. Bayesian estimation of SARS-CoV-2 prevalence in Indiana by random testing |pdf=|usr=}}{{33441450}} gab.com Text Bayesian estimation of SARS-CoV-2 prevalence in Indiana by random testing JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=33441450 #FOAvip From 25 to 29 April 2020, the state of Indiana undertook testing of 3,658 randomly chosen state residents for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, the agent causing COVID-19 disease. This was the first statewide randomized study of COVID-19 testing in the United States. Both PCR and serological tests were administered to all study participants. This paper describes statistical methods used to address nonresponse among various demographic groups and to adjust for testing errors to reduce bias in the estimates of the overall disease prevalence in Indiana. These adjustments were implemented through Bayesian methods, which incorporated all available information on disease prevalence and test performance, along with external data obtained from census of the Indiana statewide population. Both adjustments appeared to have significant impact on the unadjusted estimates, mainly due to upweighting data in study participants of non-White races and Hispanic ethnicity and anticipated false-positive and false-negative test results among both the PCR and antibody tests utilized in the study. Twit Text FOAVip Bayesian estimation of SARS-CoV-2 prevalence in Indiana by random testing ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=33441450 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33441450 #FOAvip English Wikipedia <ref>{{Cite pmid|33441450}}</ref> Bayesian estimation of SARS-CoV-2 prevalence in Indiana by random testing #MMPMID33441450 Yiannoutsos CT; Halverson PK; Menachemi N Proc Natl Acad Sci U S A 2021[Feb]; 118 (5): ä PMID33441450show ga From 25 to 29 April 2020, the state of Indiana undertook testing of 3,658 randomly chosen state residents for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, the agent causing COVID-19 disease. This was the first statewide randomized study of COVID-19 testing in the United States. Both PCR and serological tests were administered to all study participants. This paper describes statistical methods used to address nonresponse among various demographic groups and to adjust for testing errors to reduce bias in the estimates of the overall disease prevalence in Indiana. These adjustments were implemented through Bayesian methods, which incorporated all available information on disease prevalence and test performance, along with external data obtained from census of the Indiana statewide population. Both adjustments appeared to have significant impact on the unadjusted estimates, mainly due to upweighting data in study participants of non-White races and Hispanic ethnicity and anticipated false-positive and false-negative test results among both the PCR and antibody tests utilized in the study. |Bayes Theorem[MESH] |COVID-19 Testing/statistics & numerical data[MESH] |COVID-19/*diagnosis/*epidemiology/ethnology/virology[MESH] |Hispanic or Latino/statistics & numerical data[MESH] |Humans[MESH] |Indiana/epidemiology/ethnology[MESH] |Polymerase Chain Reaction[MESH] |Prevalence[MESH] |SARS-CoV-2/genetics/*isolation & purification[MESH]Bayesian estimation of SARS-CoV-2 prevalence in Indiana by random test(epmc).pdf DeepDyve Pubget Overpricing