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10.1016/j.jtbi.2020.110556 http://scihub22266oqcxt.onion/10.1016/j.jtbi.2020.110556 33385402!7774323!33385402     free     free     free       J+Theor+Biol 2021 ; 512 (ä): 110556 Nephropedia Template TP {{tp|p=33385402|t=2021. A simple correction for COVID-19 sampling bias |pdf=|usr=}}{{33385402}} gab.com Text A simple correction for COVID-19 sampling bias JO: J Theor Biol http://www.kidney.de/pget.php?&tw=1&pmid=33385402 #FOAvip COVID-19 testing has become a standard approach for estimating prevalence which then assist in public health decision making to contain and mitigate the spread of the disease. The sampling designs used are often biased in that they do not reflect the true underlying populations. For instance, individuals with strong symptoms are more likely to be tested than those with no symptoms. This results in biased estimates of prevalence (too high). Typical post-sampling corrections are not always possible. Here we present a simple bias correction methodology derived and adapted from a correction for publication bias in meta analysis studies. The methodology is general enough to allow a wide variety of customization making it more useful in practice. Implementation is easily done using already collected information. Via a simulation and two real datasets, we show that the bias corrections can provide dramatic reductions in estimation error. Twit Text FOAVip A simple correction for COVID-19 sampling bias ????J Theor Biol http://www.kidney.de/pget.php?&tw=1&pmid=33385402 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33385402 #FOAvip English Wikipedia <ref>{{Cite pmid|33385402}}</ref> A simple correction for COVID-19 sampling bias #MMPMID33385402 Diaz-Pachon DA; Rao JS J Theor Biol 2021[Mar]; 512 (ä): 110556 PMID33385402show ga COVID-19 testing has become a standard approach for estimating prevalence which then assist in public health decision making to contain and mitigate the spread of the disease. The sampling designs used are often biased in that they do not reflect the true underlying populations. For instance, individuals with strong symptoms are more likely to be tested than those with no symptoms. This results in biased estimates of prevalence (too high). Typical post-sampling corrections are not always possible. Here we present a simple bias correction methodology derived and adapted from a correction for publication bias in meta analysis studies. The methodology is general enough to allow a wide variety of customization making it more useful in practice. Implementation is easily done using already collected information. Via a simulation and two real datasets, we show that the bias corrections can provide dramatic reductions in estimation error. |*COVID-19/epidemiology/transmission[MESH] |*Computer Simulation[MESH] |*Models, Biological[MESH] |*SARS-CoV-2[MESH] |Humans[MESH]A simple correction for COVID-19 sampling bias (epmc).pdf DeepDyve Pubget Overpricing