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10.4414/smw.2020.20313 http://scihub22266oqcxt.onion/10.4414/smw.2020.20313 32677705!ä!32677705 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Swiss+Med+Wkly 2020 ; 150 (ä): w20313 Nephropedia Template TP {{tp|p=32677705|t=2020. Data-driven inference of the reproduction number for COVID-19 before and after interventions for 51 European countries |pdf=|usr=}}{{32677705}} gab.com Text Data-driven inference of the reproduction number for COVID-19 before and after interventions for 51 European countries JO: Swiss Med Wkly http://www.kidney.de/pget.php?&tw=1&pmid=32677705 #FOAvip The reproduction number is broadly considered as a key indicator for the spreading of the COVID-19 pandemic. Its estimated value is a measure of the necessity and, eventually, effectiveness of interventions imposed in various countries. Here we present an online tool for the data-driven inference and quantification of uncertainties for the reproduction number, as well as the time points of interventions for 51 European countries. The study relied on the Bayesian calibration of the SIR model with data from reported daily infections from these countries. The model fitted the data, for most countries, without individual tuning of parameters. We also compared the results of SIR and SEIR models, which give different estimates of the reproduction number, and provided an analytical relationship between the respective numbers. We deployed a Bayesian inference framework with efficient sampling algorithms, to present a publicly available graphical user interface (https://cse-lab.ethz.ch/coronavirus) that allows the user to assess and compare predictions for pairs of European countries. The results quantified the rate of the disease’s spread before and after interventions, and provided a metric for the effectiveness of non-pharmaceutical interventions in different countries. They also indicated how geographic proximity and the times of interventions affected the progression of the epidemic. Twit Text FOAVip Data-driven inference of the reproduction number for COVID-19 before and after interventions for 51 European countries ????Swiss Med Wkly http://www.kidney.de/pget.php?&tw=1&pmid=32677705 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32677705 #FOAvip English Wikipedia <ref>{{Cite pmid|32677705}}</ref> Data-driven inference of the reproduction number for COVID-19 before and after interventions for 51 European countries #MMPMID32677705 Karnakov P; Arampatzis G; Kicic I; Wermelinger F; Walchli D; Papadimitriou C; Koumoutsakos P Swiss Med Wkly 2020[Jul]; 150 (ä): w20313 PMID32677705show ga The reproduction number is broadly considered as a key indicator for the spreading of the COVID-19 pandemic. Its estimated value is a measure of the necessity and, eventually, effectiveness of interventions imposed in various countries. Here we present an online tool for the data-driven inference and quantification of uncertainties for the reproduction number, as well as the time points of interventions for 51 European countries. The study relied on the Bayesian calibration of the SIR model with data from reported daily infections from these countries. The model fitted the data, for most countries, without individual tuning of parameters. We also compared the results of SIR and SEIR models, which give different estimates of the reproduction number, and provided an analytical relationship between the respective numbers. We deployed a Bayesian inference framework with efficient sampling algorithms, to present a publicly available graphical user interface (https://cse-lab.ethz.ch/coronavirus) that allows the user to assess and compare predictions for pairs of European countries. The results quantified the rate of the disease’s spread before and after interventions, and provided a metric for the effectiveness of non-pharmaceutical interventions in different countries. They also indicated how geographic proximity and the times of interventions affected the progression of the epidemic. |*Coronavirus Infections/epidemiology/prevention & control/transmission[MESH] |*Epidemiological Monitoring[MESH] |*Pandemics/prevention & control/statistics & numerical data[MESH] |*Pneumonia, Viral/epidemiology/prevention & control/transmission[MESH] |Basic Reproduction Number/*statistics & numerical data[MESH] |Bayes Theorem[MESH] |Betacoronavirus/isolation & purification[MESH] |COVID-19[MESH] |Communicable Disease Control/methods/statistics & numerical data[MESH] |Disease Transmission, Infectious/prevention & control/*statistics & numerical data[MESH] |Epidemiologic Measurements[MESH] |Europe/epidemiology[MESH] |Humans[MESH] |SARS-CoV-2[MESH]Data-driven inference of the reproduction number for COVID-19 before a(epmc).pdf DeepDyve Pubget Overpricing