tetano
Editor, Senior Moderator
PLoS Comput Biol. 2019 Nov 22;15(11):e1007486. doi: 10.1371/journal.pcbi.1007486. [Epub ahead of print] [h=1]Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S.[/h]
Reich NG[SUP]1[/SUP], McGowan CJ[SUP]2[/SUP], Yamana TK[SUP]3[/SUP], Tushar A[SUP]4[/SUP], Ray EL[SUP]5[/SUP], Osthus D[SUP]6[/SUP], Kandula S[SUP]3[/SUP], Brooks LC[SUP]7[/SUP], Crawford-Crudell W[SUP]8[/SUP], Gibson GC[SUP]1[/SUP], Moore E[SUP]1[/SUP], Silva R[SUP]9[/SUP], Biggerstaff M[SUP]2[/SUP], Johansson MA[SUP]10[/SUP], Rosenfeld R[SUP]11[/SUP], Shaman J[SUP]3[/SUP].
[h=3]Author information[/h] 1 Department of Biostatistics and Epidemiology, University of Massachusetts-Amherst, Amherst, Massachusetts, United States of America. 2 Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America. 3 Department of Environmental Health Sciences, Columbia University, New York, New York, United States of America. 4 School of Computer Science, University of Massachusetts-Amherst, Amherst, Massachusetts, United States of America. 5 Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, Massachusetts, United States of America. 6 Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America. 7 Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America. 8 Department of Mathematics and Statistics, Smith College, Northampton, Massachusetts, United States of America. 9 Department of Mathematics and Statistics, Amherst College, Amherst, Massachusetts, United States of America. 10 Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico, United States of America. 11 Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
[h=3]Abstract[/h] Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is determined by maximizing overall ensemble accuracy over past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.
PMID: 31756193 DOI: 10.1371/journal.pcbi.1007486
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Reich NG[SUP]1[/SUP], McGowan CJ[SUP]2[/SUP], Yamana TK[SUP]3[/SUP], Tushar A[SUP]4[/SUP], Ray EL[SUP]5[/SUP], Osthus D[SUP]6[/SUP], Kandula S[SUP]3[/SUP], Brooks LC[SUP]7[/SUP], Crawford-Crudell W[SUP]8[/SUP], Gibson GC[SUP]1[/SUP], Moore E[SUP]1[/SUP], Silva R[SUP]9[/SUP], Biggerstaff M[SUP]2[/SUP], Johansson MA[SUP]10[/SUP], Rosenfeld R[SUP]11[/SUP], Shaman J[SUP]3[/SUP].
[h=3]Author information[/h] 1 Department of Biostatistics and Epidemiology, University of Massachusetts-Amherst, Amherst, Massachusetts, United States of America. 2 Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America. 3 Department of Environmental Health Sciences, Columbia University, New York, New York, United States of America. 4 School of Computer Science, University of Massachusetts-Amherst, Amherst, Massachusetts, United States of America. 5 Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, Massachusetts, United States of America. 6 Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America. 7 Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America. 8 Department of Mathematics and Statistics, Smith College, Northampton, Massachusetts, United States of America. 9 Department of Mathematics and Statistics, Amherst College, Amherst, Massachusetts, United States of America. 10 Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico, United States of America. 11 Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
[h=3]Abstract[/h] Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is determined by maximizing overall ensemble accuracy over past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.
PMID: 31756193 DOI: 10.1371/journal.pcbi.1007486
Free full text