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10.1016/j.chaos.2020.110167 http://scihub22266oqcxt.onion/10.1016/j.chaos.2020.110167 32868967!7448881!32868967     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 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       Chaos+Solitons+Fractals 2020 ; 140 (ä): 110167 Nephropedia Template TP {{tp|p=32868967|t=2020. A Bayesian approach for monitoring epidemics in presence of undetected cases |pdf=|usr=}}{{32868967}} gab.com Text A Bayesian approach for monitoring epidemics in presence of undetected cases JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32868967 #FOAvip One of the key indicators used in tracking the evolution of an infectious disease is the reproduction number. This quantity is usually computed using the reported number of cases, but ignoring that many more individuals may be infected (e.g. asymptomatic carriers). We develop a Bayesian procedure to quantify the impact of undetected infectious cases on the determination of the effective reproduction number. Our approach is stochastic, data-driven and not relying on any compartmental model. It is applied to the COVID-19 outbreak in eight different countries and all Italian regions, showing that the effect of undetected cases leads to estimates of the effective reproduction numbers larger than those obtained only with the reported cases by factors ranging from two to ten. Twit Text FOAVip A Bayesian approach for monitoring epidemics in presence of undetected cases ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32868967 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32868967 #FOAvip English Wikipedia <ref>{{Cite pmid|32868967}}</ref> A Bayesian approach for monitoring epidemics in presence of undetected cases #MMPMID32868967 De Simone A; Piangerelli M Chaos Solitons Fractals 2020[Nov]; 140 (ä): 110167 PMID32868967show ga One of the key indicators used in tracking the evolution of an infectious disease is the reproduction number. This quantity is usually computed using the reported number of cases, but ignoring that many more individuals may be infected (e.g. asymptomatic carriers). We develop a Bayesian procedure to quantify the impact of undetected infectious cases on the determination of the effective reproduction number. Our approach is stochastic, data-driven and not relying on any compartmental model. It is applied to the COVID-19 outbreak in eight different countries and all Italian regions, showing that the effect of undetected cases leads to estimates of the effective reproduction numbers larger than those obtained only with the reported cases by factors ranging from two to ten. äA Bayesian approach for monitoring epidemics in presence of undetected(epmc).pdf DeepDyve Pubget Overpricing