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10.1038/s41598-021-84679-8 http://scihub22266oqcxt.onion/10.1038/s41598-021-84679-8 33664380!7970858!33664380     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 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 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2021 ; 11 (1): 5183 Nephropedia Template TP {{tp|p=33664380|t=2021. Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions |pdf=|usr=}}{{33664380}} gab.com Text Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33664380 #FOAvip During the COVID-19 pandemic, the scientific community developed predictive models to evaluate potential governmental interventions. However, the analysis of the effects these interventions had is less advanced. Here, we propose a data-driven framework to assess these effects retrospectively. We use a regularized regression to find a parsimonious model that fits the data with the least changes in the [Formula: see text] parameter. Then, we postulate each jump in [Formula: see text] as the effect of an intervention. Following the do-operator prescriptions, we simulate the counterfactual case by forcing [Formula: see text] to stay at the pre-jump value. We then attribute a value to the intervention from the difference between true evolution and simulated counterfactual. We show that the recommendation to use facemasks for all activities would reduce the number of cases by 200,000 ([Formula: see text] CI 190,000-210,000) in Connecticut, Massachusetts, and New York State. The framework presented here might be used in any case where cause and effects are sparse in time. Twit Text FOAVip Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33664380 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33664380 #FOAvip English Wikipedia <ref>{{Cite pmid|33664380}}</ref> Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions #MMPMID33664380 Babino A; Magnasco MO Sci Rep 2021[Mar]; 11 (1): 5183 PMID33664380show ga During the COVID-19 pandemic, the scientific community developed predictive models to evaluate potential governmental interventions. However, the analysis of the effects these interventions had is less advanced. Here, we propose a data-driven framework to assess these effects retrospectively. We use a regularized regression to find a parsimonious model that fits the data with the least changes in the [Formula: see text] parameter. Then, we postulate each jump in [Formula: see text] as the effect of an intervention. Following the do-operator prescriptions, we simulate the counterfactual case by forcing [Formula: see text] to stay at the pre-jump value. We then attribute a value to the intervention from the difference between true evolution and simulated counterfactual. We show that the recommendation to use facemasks for all activities would reduce the number of cases by 200,000 ([Formula: see text] CI 190,000-210,000) in Connecticut, Massachusetts, and New York State. The framework presented here might be used in any case where cause and effects are sparse in time. |*Masks[MESH] |*Models, Statistical[MESH] |*Physical Distancing[MESH] |COVID-19/epidemiology/*prevention & control[MESH] |Humans[MESH] |Retrospective Studies[MESH]Masks and distancing during COVID-19: a causal framework for imputing (epmc).pdf DeepDyve Pubget Overpricing