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10.1038/s41467-020-18272-4 http://scihub22266oqcxt.onion/10.1038/s41467-020-18272-4 32908126!7481226!32908126     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Commun 2020 ; 11 (1): 4507 Nephropedia Template TP {{tp|p=32908126|t=2020. Substantial underestimation of SARS-CoV-2 infection in the United States |pdf=|usr=}}{{32908126}} gab.com Text Substantial underestimation of SARS-CoV-2 infection in the United States JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=32908126 #FOAvip Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response. Confirmed COVID-19 case counts in the U.S. do not capture the total burden of the pandemic because testing has been primarily restricted to individuals with moderate to severe symptoms due to limited test availability. Here, we use a semi-Bayesian probabilistic bias analysis to account for incomplete testing and imperfect diagnostic accuracy. We estimate 6,454,951 cumulative infections compared to 721,245 confirmed cases (1.9% vs. 0.2% of the population) in the United States as of April 18, 2020. Accounting for uncertainty, the number of infections during this period was 3 to 20 times higher than the number of confirmed cases. 86% (simulation interval: 64-99%) of this difference is due to incomplete testing, while 14% (0.3-36%) is due to imperfect test accuracy. The approach can readily be applied in future studies in other locations or at finer spatial scale to correct for biased testing and imperfect diagnostic accuracy to provide a more realistic assessment of COVID-19 burden. Twit Text FOAVip Substantial underestimation of SARS-CoV-2 infection in the United States ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=32908126 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32908126 #FOAvip English Wikipedia <ref>{{Cite pmid|32908126}}</ref> Substantial underestimation of SARS-CoV-2 infection in the United States #MMPMID32908126 Wu SL; Mertens AN; Crider YS; Nguyen A; Pokpongkiat NN; Djajadi S; Seth A; Hsiang MS; Colford JM Jr; Reingold A; Arnold BF; Hubbard A; Benjamin-Chung J Nat Commun 2020[Sep]; 11 (1): 4507 PMID32908126show ga Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response. Confirmed COVID-19 case counts in the U.S. do not capture the total burden of the pandemic because testing has been primarily restricted to individuals with moderate to severe symptoms due to limited test availability. Here, we use a semi-Bayesian probabilistic bias analysis to account for incomplete testing and imperfect diagnostic accuracy. We estimate 6,454,951 cumulative infections compared to 721,245 confirmed cases (1.9% vs. 0.2% of the population) in the United States as of April 18, 2020. Accounting for uncertainty, the number of infections during this period was 3 to 20 times higher than the number of confirmed cases. 86% (simulation interval: 64-99%) of this difference is due to incomplete testing, while 14% (0.3-36%) is due to imperfect test accuracy. The approach can readily be applied in future studies in other locations or at finer spatial scale to correct for biased testing and imperfect diagnostic accuracy to provide a more realistic assessment of COVID-19 burden. |Bayes Theorem[MESH] |Betacoronavirus/isolation & purification[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Clinical Laboratory Techniques/methods[MESH] |Coronavirus Infections/diagnosis/*epidemiology/virology[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/diagnosis/*epidemiology/virology[MESH] |SARS-CoV-2[MESH]Substantial underestimation of SARS-CoV-2 infection in the United Stat(epmc).pdf DeepDyve Pubget Overpricing