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10.1097/EDE.0000000000001202 http://scihub22266oqcxt.onion/10.1097/EDE.0000000000001202 32324625!7269020!32324625     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Epidemiology 2020 ; 31 (4): 567-569 Nephropedia Template TP {{tp|p=32324625|t=2020. Estimating the Size of a COVID-19 Epidemic from Surveillance Systems |pdf=|usr=}}{{32324625}} gab.com Text Estimating the Size of a COVID-19 Epidemic from Surveillance Systems JO: Epidemiology http://www.kidney.de/pget.php?&tw=1&pmid=32324625 #FOAvip Public health policy makers in countries with Coronavirus Disease 2019 (COVID-19) outbreaks face the decision of when to switch from measures that seek to contain and eliminate the outbreak to those designed to mitigate its effects. Estimates of epidemic size are complicated by surveillance systems that cannot capture all cases, and by the need for timely estimates as the epidemic is ongoing. This article provides a Bayesian methodology to estimate outbreak size from one or more surveillance systems such as virologic testing of pneumonia cases or samples from a network of general practitioners. Twit Text FOAVip Estimating the Size of a COVID-19 Epidemic from Surveillance Systems ????Epidemiology http://www.kidney.de/pget.php?&tw=1&pmid=32324625 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32324625 #FOAvip English Wikipedia <ref>{{Cite pmid|32324625}}</ref> Estimating the Size of a COVID-19 Epidemic from Surveillance Systems #MMPMID32324625 Yue M; Clapham HE; Cook AR Epidemiology 2020[Jul]; 31 (4): 567-569 PMID32324625show ga Public health policy makers in countries with Coronavirus Disease 2019 (COVID-19) outbreaks face the decision of when to switch from measures that seek to contain and eliminate the outbreak to those designed to mitigate its effects. Estimates of epidemic size are complicated by surveillance systems that cannot capture all cases, and by the need for timely estimates as the epidemic is ongoing. This article provides a Bayesian methodology to estimate outbreak size from one or more surveillance systems such as virologic testing of pneumonia cases or samples from a network of general practitioners. |*Epidemics[MESH] |Bayes Theorem[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology[MESH] |Disease Outbreaks[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH]Estimating the Size of a COVID-19 Epidemic from Surveillance Systems (epmc).pdf DeepDyve Pubget Overpricing