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10.1073/pnas.2009033117 http://scihub22266oqcxt.onion/10.1073/pnas.2009033117 32727898!7443931!32727898     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       Proc+Natl+Acad+Sci+U+S+A 2020 ; 117 (33): 19873-19878 Nephropedia Template TP {{tp|p=32727898|t=2020. Timing social distancing to avert unmanageable COVID-19 hospital surges |pdf=|usr=}}{{32727898}} gab.com Text Timing social distancing to avert unmanageable COVID-19 hospital surges JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=32727898 #FOAvip Following the April 16, 2020 release of the Opening Up America Again guidelines for relaxing coronavirus disease 2019 (COVID-19) social distancing policies, local leaders are concerned about future pandemic waves and lack robust strategies for tracking and suppressing transmission. Here, we present a strategy for triggering short-term shelter-in-place orders when hospital admissions surpass a threshold. We use stochastic optimization to derive triggers that ensure hospital surges will not exceed local capacity and lockdowns are as short as possible. For example, Austin, Texas-the fastest-growing large city in the United States-has adopted a COVID-19 response strategy based on this method. Assuming that the relaxation of social distancing increases the risk of infection sixfold, the optimal strategy will trigger a total of 135 d (90% prediction interval: 126 d to 141 d) of sheltering, allow schools to open in the fall, and result in an expected 2,929 deaths (90% prediction interval: 2,837 to 3,026) by September 2021, which is 29% of the annual mortality rate. In the months ahead, policy makers are likely to face difficult choices, and the extent of public restraint and cocooning of vulnerable populations may save or cost thousands of lives. Twit Text FOAVip Timing social distancing to avert unmanageable COVID-19 hospital surges ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=32727898 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32727898 #FOAvip English Wikipedia <ref>{{Cite pmid|32727898}}</ref> Timing social distancing to avert unmanageable COVID-19 hospital surges #MMPMID32727898 Duque D; Morton DP; Singh B; Du Z; Pasco R; Meyers LA Proc Natl Acad Sci U S A 2020[Aug]; 117 (33): 19873-19878 PMID32727898show ga Following the April 16, 2020 release of the Opening Up America Again guidelines for relaxing coronavirus disease 2019 (COVID-19) social distancing policies, local leaders are concerned about future pandemic waves and lack robust strategies for tracking and suppressing transmission. Here, we present a strategy for triggering short-term shelter-in-place orders when hospital admissions surpass a threshold. We use stochastic optimization to derive triggers that ensure hospital surges will not exceed local capacity and lockdowns are as short as possible. For example, Austin, Texas-the fastest-growing large city in the United States-has adopted a COVID-19 response strategy based on this method. Assuming that the relaxation of social distancing increases the risk of infection sixfold, the optimal strategy will trigger a total of 135 d (90% prediction interval: 126 d to 141 d) of sheltering, allow schools to open in the fall, and result in an expected 2,929 deaths (90% prediction interval: 2,837 to 3,026) by September 2021, which is 29% of the annual mortality rate. In the months ahead, policy makers are likely to face difficult choices, and the extent of public restraint and cocooning of vulnerable populations may save or cost thousands of lives. |*Logistic Models[MESH] |*Physical Distancing[MESH] |COVID-19/economics/*epidemiology/prevention & control[MESH] |Coronavirus Infections/economics/*epidemiology/prevention & control[MESH] |Cost of Illness[MESH] |Hospitalization/economics/statistics & numerical data[MESH] |Humans[MESH] |Pandemics/economics/prevention & control[MESH] |Pneumonia, Viral/economics/*epidemiology/prevention & control[MESH] |Quarantine/economics/*methods/organization & administration[MESH] |Surge Capacity/economics/*organization & administration[MESH] |Time[MESH]Timing social distancing to avert unmanageable COVID-19 hospital surge(epmc).pdf DeepDyve Pubget Overpricing