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10.1126/scitranslmed.abf1568 http://scihub22266oqcxt.onion/10.1126/scitranslmed.abf1568 33619080!8099195!33619080     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Transl+Med 2021 ; 13 (589): ä Nephropedia Template TP {{tp|p=33619080|t=2021. Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings |pdf=|usr=}}{{33619080}} gab.com Text Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings JO: Sci Transl Med http://www.kidney.de/pget.php?&tw=1&pmid=33619080 #FOAvip Virological testing is central to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) containment, but many settings face severe limitations on testing. Group testing offers a way to increase throughput by testing pools of combined samples; however, most proposed designs have not yet addressed key concerns over sensitivity loss and implementation feasibility. Here, we combined a mathematical model of epidemic spread and empirically derived viral kinetics for SARS-CoV-2 infections to identify pooling designs that are robust to changes in prevalence and to ratify sensitivity losses against the time course of individual infections. We show that prevalence can be accurately estimated across a broad range, from 0.02 to 20%, using only a few dozen pooled tests and using up to 400 times fewer tests than would be needed for individual identification. We then exhaustively evaluated the ability of different pooling designs to maximize the number of detected infections under various resource constraints, finding that simple pooling designs can identify up to 20 times as many true positives as individual testing with a given budget. Crucially, we confirmed that our theoretical results can be translated into practice using pooled human nasopharyngeal specimens by accurately estimating a 1% prevalence among 2304 samples using only 48 tests and through pooled sample identification in a panel of 960 samples. Our results show that accounting for variation in sampled viral loads provides a nuanced picture of how pooling affects sensitivity to detect infections. Using simple, practical group testing designs can vastly increase surveillance capabilities in resource-limited settings. Twit Text FOAVip Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings ????Sci Transl Med http://www.kidney.de/pget.php?&tw=1&pmid=33619080 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33619080 #FOAvip English Wikipedia <ref>{{Cite pmid|33619080}}</ref> Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings #MMPMID33619080 Cleary B; Hay JA; Blumenstiel B; Harden M; Cipicchio M; Bezney J; Simonton B; Hong D; Senghore M; Sesay AK; Gabriel S; Regev A; Mina MJ Sci Transl Med 2021[Apr]; 13 (589): ä PMID33619080show ga Virological testing is central to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) containment, but many settings face severe limitations on testing. Group testing offers a way to increase throughput by testing pools of combined samples; however, most proposed designs have not yet addressed key concerns over sensitivity loss and implementation feasibility. Here, we combined a mathematical model of epidemic spread and empirically derived viral kinetics for SARS-CoV-2 infections to identify pooling designs that are robust to changes in prevalence and to ratify sensitivity losses against the time course of individual infections. We show that prevalence can be accurately estimated across a broad range, from 0.02 to 20%, using only a few dozen pooled tests and using up to 400 times fewer tests than would be needed for individual identification. We then exhaustively evaluated the ability of different pooling designs to maximize the number of detected infections under various resource constraints, finding that simple pooling designs can identify up to 20 times as many true positives as individual testing with a given budget. Crucially, we confirmed that our theoretical results can be translated into practice using pooled human nasopharyngeal specimens by accurately estimating a 1% prevalence among 2304 samples using only 48 tests and through pooled sample identification in a panel of 960 samples. Our results show that accounting for variation in sampled viral loads provides a nuanced picture of how pooling affects sensitivity to detect infections. Using simple, practical group testing designs can vastly increase surveillance capabilities in resource-limited settings. |*COVID-19[MESH] |*Epidemics[MESH] |Humans[MESH] |SARS-CoV-2[MESH] |Serologic Tests[MESH] |Specimen Handling[MESH]Using viral load and epidemic dynamics to optimize pooled testing in (epmc).pdf DeepDyve Pubget Overpricing