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10.1007/s10654-020-00661-0 http://scihub22266oqcxt.onion/10.1007/s10654-020-00661-0 32676971!7366467!32676971     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       Eur+J+Epidemiol 2020 ; 35 (8): 727-729 Nephropedia Template TP {{tp|p=32676971|t=2020. Forecasting efforts from prior epidemics and COVID-19 predictions |pdf=|usr=}}{{32676971}} gab.com Text Forecasting efforts from prior epidemics and COVID-19 predictions JO: Eur J Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=32676971 #FOAvip Since the onset of the COVID-19 pandemic, countless disease prediction models have emerged, shaping the focus of news media, policymakers, and broader society. We reviewed the accuracy of forecasts made during prior twenty-first century epidemics, namely SARS, H1N1, and Ebola. We found that while disease prediction models were relatively nascent as a research focus during SARS and H1N1, for Ebola, numerous such forecasts were published. We found that forecasts of deaths for Ebola were often far from the eventual reality, with a strong tendency to over predict. Given the societal prominence of these models, it is crucial that their uncertainty be communicated. Otherwise, we will be unaware if we are being falsely lulled into complacency or unjustifiably shocked into action. Twit Text FOAVip Forecasting efforts from prior epidemics and COVID-19 predictions ????Eur J Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=32676971 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32676971 #FOAvip English Wikipedia <ref>{{Cite pmid|32676971}}</ref> Forecasting efforts from prior epidemics and COVID-19 predictions #MMPMID32676971 Nadella P; Swaminathan A; Subramanian SV Eur J Epidemiol 2020[Aug]; 35 (8): 727-729 PMID32676971show ga Since the onset of the COVID-19 pandemic, countless disease prediction models have emerged, shaping the focus of news media, policymakers, and broader society. We reviewed the accuracy of forecasts made during prior twenty-first century epidemics, namely SARS, H1N1, and Ebola. We found that while disease prediction models were relatively nascent as a research focus during SARS and H1N1, for Ebola, numerous such forecasts were published. We found that forecasts of deaths for Ebola were often far from the eventual reality, with a strong tendency to over predict. Given the societal prominence of these models, it is crucial that their uncertainty be communicated. Otherwise, we will be unaware if we are being falsely lulled into complacency or unjustifiably shocked into action. |*Coronavirus Infections/epidemiology/prevention & control/transmission[MESH] |*Forecasting[MESH] |*Pandemics[MESH] |*Pneumonia, Viral/epidemiology/prevention & control/transmission[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Epidemics[MESH] |Hemorrhagic Fever, Ebola/epidemiology[MESH] |Humans[MESH] |Influenza A Virus, H1N1 Subtype[MESH] |Influenza, Human/epidemiology[MESH] |Models, Statistical[MESH] |SARS-CoV-2[MESH]Forecasting efforts from prior epidemics and COVID-19 predictions (epmc).pdf DeepDyve Pubget Overpricing