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10.1093/jamiaopen/ooaa049 http://scihub22266oqcxt.onion/10.1093/jamiaopen/ooaa049 33619465!7665534!33619465     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       JAMIA+Open 2020 ; 3 (4): 496-499 Nephropedia Template TP {{tp|p=33619465|t=2020. Bayesian estimation of the seroprevalence of antibodies to SARS-CoV-2 |pdf=|usr=}}{{33619465}} gab.com Text Bayesian estimation of the seroprevalence of antibodies to SARS-CoV-2 JO: JAMIA Open http://www.kidney.de/pget.php?&tw=1&pmid=33619465 #FOAvip Accurate estimations of the seroprevalence of antibodies to severe acute respiratory syndrome coronavirus 2 need to properly consider the specificity and sensitivity of the antibody tests. In addition, prior knowledge of the extent of viral infection in a population may also be important for adjusting the estimation of seroprevalence. For this purpose, we have developed a Bayesian approach that can incorporate the variabilities of specificity and sensitivity of the antibody tests, as well as the prior probability distribution of seroprevalence. We have demonstrated the utility of our approach by applying it to a recently published large-scale dataset from the US CDC, with our results providing entire probability distributions of seroprevalence instead of single-point estimates. Our Bayesian code is freely available at https://github.com/qunfengdong/AntibodyTest. Twit Text FOAVip Bayesian estimation of the seroprevalence of antibodies to SARS-CoV-2 ????JAMIA Open http://www.kidney.de/pget.php?&tw=1&pmid=33619465 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33619465 #FOAvip English Wikipedia <ref>{{Cite pmid|33619465}}</ref> Bayesian estimation of the seroprevalence of antibodies to SARS-CoV-2 #MMPMID33619465 Dong Q; Gao X JAMIA Open 2020[Dec]; 3 (4): 496-499 PMID33619465show ga Accurate estimations of the seroprevalence of antibodies to severe acute respiratory syndrome coronavirus 2 need to properly consider the specificity and sensitivity of the antibody tests. In addition, prior knowledge of the extent of viral infection in a population may also be important for adjusting the estimation of seroprevalence. For this purpose, we have developed a Bayesian approach that can incorporate the variabilities of specificity and sensitivity of the antibody tests, as well as the prior probability distribution of seroprevalence. We have demonstrated the utility of our approach by applying it to a recently published large-scale dataset from the US CDC, with our results providing entire probability distributions of seroprevalence instead of single-point estimates. Our Bayesian code is freely available at https://github.com/qunfengdong/AntibodyTest. äBayesian estimation of the seroprevalence of antibodies to SARS-CoV-2 (epmc).pdf DeepDyve Pubget Overpricing