Results from the second year of a collaborative effort to forecast influenza seasons in the United States
Matthew Biggerstaff,
Michael Johansson,
David Alper,
Logan C. Brooks,
Prithwish Chakraborty,
David C. Farrow,
Sangwon Hyun,
Sasikiran Kandula,
Craig McGowan,
Naren Ramakrishnan,
Roni Rosenfeld,
Jeffrey Shaman,
Rob Tibshirani,
Ryan J. Tibshirani,
Alessandro Vespignani,
Wan Yang,
Qian Zhang,
Carrie Reed
Affiliations
Matthew Biggerstaff
Epidemiology and Prevention Branch, Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA; Corresponding author at: Centers for Disease Control and Prevention, 1600 Clifton Road NE MS A-32, Atlanta, GA 30333, USA.
Michael Johansson
Dengue Branch, Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
David Alper
Everyday Health, New York City, NY, USA
Logan C. Brooks
Department of Computer Science, Carnegie Mellon University, Pittsburg, PA, USA
Prithwish Chakraborty
Discovery Analytics Center, Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
David C. Farrow
Department of Computational Biology, Carnegie Mellon University, Pittsburg, PA, USA
Sangwon Hyun
Deptartment of Statistics, Carnegie Mellon University, Pittsburg, PA, USA
Sasikiran Kandula
Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
Craig McGowan
Epidemiology and Prevention Branch, Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
Naren Ramakrishnan
Discovery Analytics Center, Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
Roni Rosenfeld
Deptartment of Machine Learning, Department of Language Technologies, Department of Computational Biology, Department of Computer Science, Carnegie Mellon University, Pittsburg, PA, USA
Jeffrey Shaman
Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
Rob Tibshirani
Department of Health Research and Policy, Department of Statistics, Stanford University, Stanford, CA, USA
Ryan J. Tibshirani
Deptartment of Statistics, Department of Machine Learning, Carnegie Mellon University, Pittsburg, PA, USA
Alessandro Vespignani
Northeastern University, Boston, MA, USA
Wan Yang
Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
Qian Zhang
Northeastern University, Boston, MA, USA
Carrie Reed
Epidemiology and Prevention Branch, Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked with external researchers to improve influenza forecasts by coordinating seasonal challenges for the United States and the 10 Health and Human Service Regions. Forecasted targets for the 2014–15 challenge were the onset week, peak week, and peak intensity of the season and the weekly percent of outpatient visits due to influenza-like illness (ILI) 1–4 weeks in advance. We used a logarithmic scoring rule to score the weekly forecasts, averaged the scores over an evaluation period, and then exponentiated the resulting logarithmic score. Poor forecasts had a score near 0, and perfect forecasts a score of 1.Five teams submitted forecasts from seven different models. At the national level, the team scores for onset week ranged from <0.01 to 0.41, peak week ranged from 0.08 to 0.49, and peak intensity ranged from <0.01 to 0.17. The scores for predictions of ILI 1–4 weeks in advance ranged from 0.02–0.38 and was highest 1 week ahead. Forecast skill varied by HHS region.Forecasts can predict epidemic characteristics that inform public health actions. CDC, state and local health officials, and researchers are working together to improve forecasts. Keywords: Influenza, Epidemics, Forecasting, Prediction, Modeling
