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10.1371/journal.pone.0252827 http://scihub22266oqcxt.onion/10.1371/journal.pone.0252827 34077448!8171941!34077448     free     free     free 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 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 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 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       PLoS+One 2021 ; 16 (6): e0252827 Nephropedia Template TP {{tp|p=34077448|t=2021. Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave |pdf=|usr=}}{{34077448}} gab.com Text Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=34077448 #FOAvip The novel coronavirus (SARS-CoV-2) has rapidly developed into a global epidemic. To control its spread, countries have implemented non-pharmaceutical interventions (NPIs), such as school closures, bans of small gatherings, or even stay-at-home orders. Here we study the effectiveness of seven NPIs in reducing the number of new infections, which was inferred from the reported cases of COVID-19 using a semi-mechanistic Bayesian hierarchical model. Based on data from the first epidemic wave of n = 20 countries (i.e., the United States, Canada, Australia, the EU-15 countries, Norway, and Switzerland), we estimate the relative reduction in the number of new infections attributed to each NPI. Among the NPIs considered, bans of large gatherings were most effective, followed by venue and school closures, whereas stay-at-home orders and work-from-home orders were least effective. With this retrospective cross-country analysis, we provide estimates regarding the effectiveness of different NPIs during the first epidemic wave. Twit Text FOAVip Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=34077448 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34077448 #FOAvip English Wikipedia <ref>{{Cite pmid|34077448}}</ref> Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave #MMPMID34077448 Banholzer N; van Weenen E; Lison A; Cenedese A; Seeliger A; Kratzwald B; Tschernutter D; Salles JP; Bottrighi P; Lehtinen S; Feuerriegel S; Vach W PLoS One 2021[]; 16 (6): e0252827 PMID34077448show ga The novel coronavirus (SARS-CoV-2) has rapidly developed into a global epidemic. To control its spread, countries have implemented non-pharmaceutical interventions (NPIs), such as school closures, bans of small gatherings, or even stay-at-home orders. Here we study the effectiveness of seven NPIs in reducing the number of new infections, which was inferred from the reported cases of COVID-19 using a semi-mechanistic Bayesian hierarchical model. Based on data from the first epidemic wave of n = 20 countries (i.e., the United States, Canada, Australia, the EU-15 countries, Norway, and Switzerland), we estimate the relative reduction in the number of new infections attributed to each NPI. Among the NPIs considered, bans of large gatherings were most effective, followed by venue and school closures, whereas stay-at-home orders and work-from-home orders were least effective. With this retrospective cross-country analysis, we provide estimates regarding the effectiveness of different NPIs during the first epidemic wave. |Bayes Theorem[MESH] |COVID-19/*prevention & control[MESH] |Disease Outbreaks/prevention & control/statistics & numerical data[MESH] |Epidemics/prevention & control/statistics & numerical data[MESH] |Humans[MESH] |Physical Distancing[MESH] |Quarantine/*methods/*trends[MESH] |Retrospective Studies[MESH]Estimating the effects of non-pharmaceutical interventions on the numb(epmc).pdf DeepDyve Pubget Overpricing