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10.1002/jmv.25929 http://scihub22266oqcxt.onion/10.1002/jmv.25929 32330297!7264525!32330297     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Med+Virol 2020 ; 92 (10): 1988-1994 Nephropedia Template TP {{tp|p=32330297|t=2020. Optimization of group size in pool testing strategy for SARS-CoV-2: A simple mathematical model |pdf=|usr=}}{{32330297}} gab.com Text Optimization of group size in pool testing strategy for SARS-CoV-2: A simple mathematical model JO: J Med Virol http://www.kidney.de/pget.php?&tw=1&pmid=32330297 #FOAvip Coronavirus disease (Covid-19) has reached unprecedented pandemic levels and is affecting almost every country in the world. Ramping up the testing capacity of a country supposes an essential public health response to this new outbreak. A pool testing strategy where multiple samples are tested in a single reverse transcriptase-polymerase chain reaction (RT-PCR) kit could potentially increase a country's testing capacity. The aim of this study is to propose a simple mathematical model to estimate the optimum number of pooled samples according to the relative prevalence of positive tests in a particular healthcare context, assuming that if a group tests negative, no further testing is done whereas if a group tests positive, all the subjects of the group are retested individually. The model predicts group sizes that range from 11 to 3 subjects. For a prevalence of 10% of positive tests, 40.6% of tests can be saved using testing groups of four subjects. For a 20% prevalence, 17.9% of tests can be saved using groups of three subjects. For higher prevalences, the strategy flattens and loses effectiveness. Pool testing individuals for severe acute respiratory syndrome coronavirus 2 is a valuable strategy that could considerably boost a country's testing capacity. However, further studies are needed to address how large these groups can be, without losing sensitivity on the RT-PCR. The strategy best works in settings with a low prevalence of positive tests. It is best implemented in subgroups with low clinical suspicion. The model can be adapted to specific prevalences, generating a tailored to the context implementation of the pool testing strategy. Twit Text FOAVip Optimization of group size in pool testing strategy for SARS-CoV-2: A simple mathematical model ????J Med Virol http://www.kidney.de/pget.php?&tw=1&pmid=32330297 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32330297 #FOAvip English Wikipedia <ref>{{Cite pmid|32330297}}</ref> Optimization of group size in pool testing strategy for SARS-CoV-2: A simple mathematical model #MMPMID32330297 Aragon-Caqueo D; Fernandez-Salinas J; Laroze D J Med Virol 2020[Oct]; 92 (10): 1988-1994 PMID32330297show ga Coronavirus disease (Covid-19) has reached unprecedented pandemic levels and is affecting almost every country in the world. Ramping up the testing capacity of a country supposes an essential public health response to this new outbreak. A pool testing strategy where multiple samples are tested in a single reverse transcriptase-polymerase chain reaction (RT-PCR) kit could potentially increase a country's testing capacity. The aim of this study is to propose a simple mathematical model to estimate the optimum number of pooled samples according to the relative prevalence of positive tests in a particular healthcare context, assuming that if a group tests negative, no further testing is done whereas if a group tests positive, all the subjects of the group are retested individually. The model predicts group sizes that range from 11 to 3 subjects. For a prevalence of 10% of positive tests, 40.6% of tests can be saved using testing groups of four subjects. For a 20% prevalence, 17.9% of tests can be saved using groups of three subjects. For higher prevalences, the strategy flattens and loses effectiveness. Pool testing individuals for severe acute respiratory syndrome coronavirus 2 is a valuable strategy that could considerably boost a country's testing capacity. However, further studies are needed to address how large these groups can be, without losing sensitivity on the RT-PCR. The strategy best works in settings with a low prevalence of positive tests. It is best implemented in subgroups with low clinical suspicion. The model can be adapted to specific prevalences, generating a tailored to the context implementation of the pool testing strategy. |*Models, Theoretical[MESH] |COVID-19 Nucleic Acid Testing/*methods[MESH] |COVID-19/*diagnosis/epidemiology[MESH] |Humans[MESH] |Mass Screening/*methods[MESH] |Prevalence[MESH]Optimization of group size in pool testing strategy for SARS-CoV-2: A (epmc).pdf DeepDyve Pubget Overpricing