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10.1016/j.mbs.2020.108466 http://scihub22266oqcxt.onion/10.1016/j.mbs.2020.108466 32920095!7482592!32920095     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Math+Biosci 2020 ; 329 (ä): 108466 Nephropedia Template TP {{tp|p=32920095|t=2020. Modeling COVID-19 pandemic using Bayesian analysis with application to Slovene data |pdf=|usr=}}{{32920095}} gab.com Text Modeling COVID-19 pandemic using Bayesian analysis with application to Slovene data JO: Math Biosci http://www.kidney.de/pget.php?&tw=1&pmid=32920095 #FOAvip In the paper, we propose a semiparametric framework for modeling the COVID-19 pandemic. The stochastic part of the framework is based on Bayesian inference. The model is informed by the actual COVID-19 data and the current epidemiological findings about the disease. The framework combines many available data sources (number of positive cases, number of patients in hospitals and in intensive care, etc.) to make outputs as accurate as possible and incorporates the times of non-pharmaceutical governmental interventions which were adopted worldwide to slow-down the pandemic. The model estimates the reproduction number of SARS-CoV-2, the number of infected individuals and the number of patients in different disease progression states in time. It can be used for estimating current infection fatality rate, proportion of individuals not detected and short term forecasting of important indicators for monitoring the state of the healthcare system. With the prediction of the number of patients in hospitals and intensive care units, policy makers could make data driven decisions to potentially avoid overloading the capacities of the healthcare system. The model is applied to Slovene COVID-19 data showing the effectiveness of the adopted interventions for controlling the epidemic by reducing the reproduction number of SARS-CoV-2. It is estimated that the proportion of infected people in Slovenia was among the lowest in Europe (0.350%, 90% CI [0.245-0.573]%), that infection fatality rate in Slovenia until the end of first wave was 1.56% (90% CI [0.94-2.21]%) and the proportion of unidentified cases was 88% (90% CI [83-93]%). The proposed framework can be extended to more countries/regions, thus allowing for comparison between them. One such modification is exhibited on data for Slovene hospitals. Twit Text FOAVip Modeling COVID-19 pandemic using Bayesian analysis with application to Slovene data ????Math Biosci http://www.kidney.de/pget.php?&tw=1&pmid=32920095 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32920095 #FOAvip English Wikipedia <ref>{{Cite pmid|32920095}}</ref> Modeling COVID-19 pandemic using Bayesian analysis with application to Slovene data #MMPMID32920095 Manevski D; Ruzic Gorenjec N; Kejzar N; Blagus R Math Biosci 2020[Nov]; 329 (ä): 108466 PMID32920095show ga In the paper, we propose a semiparametric framework for modeling the COVID-19 pandemic. The stochastic part of the framework is based on Bayesian inference. The model is informed by the actual COVID-19 data and the current epidemiological findings about the disease. The framework combines many available data sources (number of positive cases, number of patients in hospitals and in intensive care, etc.) to make outputs as accurate as possible and incorporates the times of non-pharmaceutical governmental interventions which were adopted worldwide to slow-down the pandemic. The model estimates the reproduction number of SARS-CoV-2, the number of infected individuals and the number of patients in different disease progression states in time. It can be used for estimating current infection fatality rate, proportion of individuals not detected and short term forecasting of important indicators for monitoring the state of the healthcare system. With the prediction of the number of patients in hospitals and intensive care units, policy makers could make data driven decisions to potentially avoid overloading the capacities of the healthcare system. The model is applied to Slovene COVID-19 data showing the effectiveness of the adopted interventions for controlling the epidemic by reducing the reproduction number of SARS-CoV-2. It is estimated that the proportion of infected people in Slovenia was among the lowest in Europe (0.350%, 90% CI [0.245-0.573]%), that infection fatality rate in Slovenia until the end of first wave was 1.56% (90% CI [0.94-2.21]%) and the proportion of unidentified cases was 88% (90% CI [83-93]%). The proposed framework can be extended to more countries/regions, thus allowing for comparison between them. One such modification is exhibited on data for Slovene hospitals. |*Betacoronavirus[MESH] |*Models, Biological[MESH] |Basic Reproduction Number/statistics & numerical data[MESH] |Bayes Theorem[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology/transmission[MESH] |Disease Progression[MESH] |Forecasting[MESH] |Hospitalization/statistics & numerical data[MESH] |Humans[MESH] |Mathematical Concepts[MESH] |Pandemics/*statistics & numerical data[MESH] |Pneumonia, Viral/*epidemiology/transmission[MESH] |SARS-CoV-2[MESH] |Slovenia/epidemiology[MESH]Modeling COVID-19 pandemic using Bayesian analysis with application to(epmc).pdf DeepDyve Pubget Overpricing