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10.1371/journal.pcbi.1009211 http://scihub22266oqcxt.onion/10.1371/journal.pcbi.1009211 34310593!8341713!34310593     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 269.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 269.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+Comput+Biol 2021 ; 17 (7): e1009211 Nephropedia Template TP {{tp|p=34310593|t=2021. A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic |pdf=|usr=}}{{34310593}} gab.com Text A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic JO: PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=34310593 #FOAvip The effective reproduction number Reff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. This is exactly the situation we are confronted with during this COVID-19 pandemic. In this work, we propose to estimate Reff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate. This rate is modeled by a Brownian diffusion process embedded in a stochastic model. The model is then fitted by Bayesian inference (particle Markov Chain Monte Carlo method) using multiple well-documented hospital datasets from several regions in France and in Ireland. This mechanistic modeling framework enables us to reconstruct the temporal evolution of the transmission rate of the COVID-19 based only on the available data. Except for the specific model structure, it is non-specifically assumed that the transmission rate follows a basic stochastic process constrained by the observations. This approach allows us to follow both the course of the COVID-19 epidemic and the temporal evolution of its Reff(t). Besides, it allows to assess and to interpret the evolution of transmission with respect to the mitigation strategies implemented to control the epidemic waves in France and in Ireland. We can thus estimate a reduction of more than 80% for the first wave in all the studied regions but a smaller reduction for the second wave when the epidemic was less active, around 45% in France but just 20% in Ireland. For the third wave in Ireland the reduction was again significant (>70%). Twit Text FOAVip A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic ????PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=34310593 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34310593 #FOAvip English Wikipedia <ref>{{Cite pmid|34310593}}</ref> A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic #MMPMID34310593 Cazelles B; Champagne C; Nguyen-Van-Yen B; Comiskey C; Vergu E; Roche B PLoS Comput Biol 2021[Jul]; 17 (7): e1009211 PMID34310593show ga The effective reproduction number Reff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. This is exactly the situation we are confronted with during this COVID-19 pandemic. In this work, we propose to estimate Reff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate. This rate is modeled by a Brownian diffusion process embedded in a stochastic model. The model is then fitted by Bayesian inference (particle Markov Chain Monte Carlo method) using multiple well-documented hospital datasets from several regions in France and in Ireland. This mechanistic modeling framework enables us to reconstruct the temporal evolution of the transmission rate of the COVID-19 based only on the available data. Except for the specific model structure, it is non-specifically assumed that the transmission rate follows a basic stochastic process constrained by the observations. This approach allows us to follow both the course of the COVID-19 epidemic and the temporal evolution of its Reff(t). Besides, it allows to assess and to interpret the evolution of transmission with respect to the mitigation strategies implemented to control the epidemic waves in France and in Ireland. We can thus estimate a reduction of more than 80% for the first wave in all the studied regions but a smaller reduction for the second wave when the epidemic was less active, around 45% in France but just 20% in Ireland. For the third wave in Ireland the reduction was again significant (>70%). |*Basic Reproduction Number/statistics & numerical data[MESH] |*Pandemics/statistics & numerical data[MESH] |*SARS-CoV-2[MESH] |Algorithms[MESH] |Bayes Theorem[MESH] |COVID-19/*epidemiology/*transmission[MESH] |Computational Biology[MESH] |Epidemics/statistics & numerical data[MESH] |France/epidemiology[MESH] |Humans[MESH] |Ireland/epidemiology[MESH] |Markov Chains[MESH] |Models, Statistical[MESH] |Monte Carlo Method[MESH] |Seroepidemiologic Studies[MESH] |Stochastic Processes[MESH]A mechanistic and data-driven reconstruction of the time-varying repro(epmc).pdf DeepDyve Pubget Overpricing