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10.1136/bmjopen-2020-041472 http://scihub22266oqcxt.onion/10.1136/bmjopen-2020-041472 34035086!8154292!34035086     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 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       BMJ+Open 2021 ; 11 (5): e041472 Nephropedia Template TP {{tp|p=34035086|t=2021. Predicting the propagation of COVID-19 at an international scale: extension of an SIR model |pdf=|usr=}}{{34035086}} gab.com Text Predicting the propagation of COVID-19 at an international scale: extension of an SIR model JO: BMJ Open http://www.kidney.de/pget.php?&tw=1&pmid=34035086 #FOAvip OBJECTIVES: Several epidemiological models have been published to forecast the spread of the COVID-19 pandemic, yet many of them have proven inaccurate for reasons that remain to be fully determined. We aimed to develop a novel model and implement it in a freely accessible web application. DESIGN: We built an SIR-type compartmental model with two additional compartments: D (deceased patients); L (individuals who will die but who will not infect anybody due to social or medical isolation) and integration of a time-dependent transmission rate and a periodical weekly component linked to the way in which cases and deaths are reported. RESULTS: The model was implemented in a web application (as of 2 June 2020). It was shown to be able to accurately capture the changes in the dynamics of the pandemic for 20 countries whatever the type of pandemic spread or containment measures: for instance, the model explains 97% of the variance of US data (daily cases) and predicts the number of deaths at a 2-week horizon with an error of 1%. CONCLUSIONS: In early performance evaluation, our model showed a high level of accuracy between prediction and observed data. Such a tool might be used by the global community to follow the spread of the pandemic. Twit Text FOAVip Predicting the propagation of COVID-19 at an international scale: extension of an SIR model ????BMJ Open http://www.kidney.de/pget.php?&tw=1&pmid=34035086 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34035086 #FOAvip English Wikipedia <ref>{{Cite pmid|34035086}}</ref> Predicting the propagation of COVID-19 at an international scale: extension of an SIR model #MMPMID34035086 Lavielle M; Faron M; Lefevre JH; Zeitoun JD BMJ Open 2021[May]; 11 (5): e041472 PMID34035086show ga OBJECTIVES: Several epidemiological models have been published to forecast the spread of the COVID-19 pandemic, yet many of them have proven inaccurate for reasons that remain to be fully determined. We aimed to develop a novel model and implement it in a freely accessible web application. DESIGN: We built an SIR-type compartmental model with two additional compartments: D (deceased patients); L (individuals who will die but who will not infect anybody due to social or medical isolation) and integration of a time-dependent transmission rate and a periodical weekly component linked to the way in which cases and deaths are reported. RESULTS: The model was implemented in a web application (as of 2 June 2020). It was shown to be able to accurately capture the changes in the dynamics of the pandemic for 20 countries whatever the type of pandemic spread or containment measures: for instance, the model explains 97% of the variance of US data (daily cases) and predicts the number of deaths at a 2-week horizon with an error of 1%. CONCLUSIONS: In early performance evaluation, our model showed a high level of accuracy between prediction and observed data. Such a tool might be used by the global community to follow the spread of the pandemic. |*COVID-19[MESH] |*Pandemics[MESH] |Forecasting[MESH] |Humans[MESH]Predicting the propagation of COVID-19 at an international scale: exte(epmc).pdf DeepDyve Pubget Overpricing