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10.1186/s12874-020-01081-0 http://scihub22266oqcxt.onion/10.1186/s12874-020-01081-0 32703158!7376319!32703158     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Med+Res+Methodol 2020 ; 20 (1): 196 Nephropedia Template TP {{tp|p=32703158|t=2020. COVID-19 prevalence estimation by random sampling in population - optimal sample pooling under varying assumptions about true prevalence |pdf=|usr=}}{{32703158}} gab.com Text COVID-19 prevalence estimation by random sampling in population - optimal sample pooling under varying assumptions about true prevalence JO: BMC Med Res Methodol http://www.kidney.de/pget.php?&tw=1&pmid=32703158 #FOAvip BACKGROUND: The number of confirmed COVID-19 cases divided by population size is used as a coarse measurement for the burden of disease in a population. However, this fraction depends heavily on the sampling intensity and the various test criteria used in different jurisdictions, and many sources indicate that a large fraction of cases tend to go undetected. METHODS: Estimates of the true prevalence of COVID-19 in a population can be made by random sampling and pooling of RT-PCR tests. Here I use simulations to explore how experiment sample size and degrees of sample pooling impact precision of prevalence estimates and potential for minimizing the total number of tests required to get individual-level diagnostic results. RESULTS: Sample pooling can greatly reduce the total number of tests required for prevalence estimation. In low-prevalence populations, it is theoretically possible to pool hundreds of samples with only marginal loss of precision. Even when the true prevalence is as high as 10% it can be appropriate to pool up to 15 samples. Sample pooling can be particularly beneficial when the test has imperfect specificity by providing more accurate estimates of the prevalence than an equal number of individual-level tests. CONCLUSION: Sample pooling should be considered in COVID-19 prevalence estimation efforts. Twit Text FOAVip COVID-19 prevalence estimation by random sampling in population - optimal sample pooling under varying assumptions about true prevalence ????BMC Med Res Methodol http://www.kidney.de/pget.php?&tw=1&pmid=32703158 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32703158 #FOAvip English Wikipedia <ref>{{Cite pmid|32703158}}</ref> COVID-19 prevalence estimation by random sampling in population - optimal sample pooling under varying assumptions about true prevalence #MMPMID32703158 Brynildsrud O BMC Med Res Methodol 2020[Jul]; 20 (1): 196 PMID32703158show ga BACKGROUND: The number of confirmed COVID-19 cases divided by population size is used as a coarse measurement for the burden of disease in a population. However, this fraction depends heavily on the sampling intensity and the various test criteria used in different jurisdictions, and many sources indicate that a large fraction of cases tend to go undetected. METHODS: Estimates of the true prevalence of COVID-19 in a population can be made by random sampling and pooling of RT-PCR tests. Here I use simulations to explore how experiment sample size and degrees of sample pooling impact precision of prevalence estimates and potential for minimizing the total number of tests required to get individual-level diagnostic results. RESULTS: Sample pooling can greatly reduce the total number of tests required for prevalence estimation. In low-prevalence populations, it is theoretically possible to pool hundreds of samples with only marginal loss of precision. Even when the true prevalence is as high as 10% it can be appropriate to pool up to 15 samples. Sample pooling can be particularly beneficial when the test has imperfect specificity by providing more accurate estimates of the prevalence than an equal number of individual-level tests. CONCLUSION: Sample pooling should be considered in COVID-19 prevalence estimation efforts. |Algorithms[MESH] |Betacoronavirus/genetics/*isolation & purification[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnosis/epidemiology/virology[MESH] |Diagnostic Tests, Routine/*methods[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnosis/epidemiology/virology[MESH] |Population Surveillance/*methods[MESH] |Prevalence[MESH] |Reproducibility of Results[MESH] |Reverse Transcriptase Polymerase Chain Reaction/methods[MESH] |SARS-CoV-2[MESH] |Sample Size[MESH] |Sensitivity and Specificity[MESH]COVID-19 prevalence estimation by random sampling in population - opti(epmc).pdf DeepDyve Pubget Overpricing