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10.1073/pnas.2007868117 http://scihub22266oqcxt.onion/10.1073/pnas.2007868117 33004629!7585017!33004629     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       Proc+Natl+Acad+Sci+U+S+A 2020 ; 117 (42): 26190-26196 Nephropedia Template TP {{tp|p=33004629|t=2020. The turning point and end of an expanding epidemic cannot be precisely forecast |pdf=|usr=}}{{33004629}} gab.com Text The turning point and end of an expanding epidemic cannot be precisely forecast JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=33004629 #FOAvip Epidemic spread is characterized by exponentially growing dynamics, which are intrinsically unpredictable. The time at which the growth in the number of infected individuals halts and starts decreasing cannot be calculated with certainty before the turning point is actually attained; neither can the end of the epidemic after the turning point. A susceptible-infected-removed (SIR) model with confinement (SCIR) illustrates how lockdown measures inhibit infection spread only above a threshold that we calculate. The existence of that threshold has major effects in predictability: A Bayesian fit to the COVID-19 pandemic in Spain shows that a slowdown in the number of newly infected individuals during the expansion phase allows one to infer neither the precise position of the maximum nor whether the measures taken will bring the propagation to the inhibition regime. There is a short horizon for reliable prediction, followed by a dispersion of the possible trajectories that grows extremely fast. The impossibility to predict in the midterm is not due to wrong or incomplete data, since it persists in error-free, synthetically produced datasets and does not necessarily improve by using larger datasets. Our study warns against precise forecasts of the evolution of epidemics based on mean-field, effective, or phenomenological models and supports that only probabilities of different outcomes can be confidently given. Twit Text FOAVip The turning point and end of an expanding epidemic cannot be precisely forecast ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=33004629 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33004629 #FOAvip English Wikipedia <ref>{{Cite pmid|33004629}}</ref> The turning point and end of an expanding epidemic cannot be precisely forecast #MMPMID33004629 Castro M; Ares S; Cuesta JA; Manrubia S Proc Natl Acad Sci U S A 2020[Oct]; 117 (42): 26190-26196 PMID33004629show ga Epidemic spread is characterized by exponentially growing dynamics, which are intrinsically unpredictable. The time at which the growth in the number of infected individuals halts and starts decreasing cannot be calculated with certainty before the turning point is actually attained; neither can the end of the epidemic after the turning point. A susceptible-infected-removed (SIR) model with confinement (SCIR) illustrates how lockdown measures inhibit infection spread only above a threshold that we calculate. The existence of that threshold has major effects in predictability: A Bayesian fit to the COVID-19 pandemic in Spain shows that a slowdown in the number of newly infected individuals during the expansion phase allows one to infer neither the precise position of the maximum nor whether the measures taken will bring the propagation to the inhibition regime. There is a short horizon for reliable prediction, followed by a dispersion of the possible trajectories that grows extremely fast. The impossibility to predict in the midterm is not due to wrong or incomplete data, since it persists in error-free, synthetically produced datasets and does not necessarily improve by using larger datasets. Our study warns against precise forecasts of the evolution of epidemics based on mean-field, effective, or phenomenological models and supports that only probabilities of different outcomes can be confidently given. |*Forecasting/methods[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology/prevention & control/transmission[MESH] |Humans[MESH] |Models, Biological[MESH] |Pandemics/prevention & control[MESH] |Pneumonia, Viral/*epidemiology/prevention & control/transmission[MESH] |SARS-CoV-2[MESH] |Spain/epidemiology[MESH]The turning point and end of an expanding epidemic cannot be precisely(epmc).pdf DeepDyve Pubget Overpricing