Journal Article

Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016

Madhav Erraguntla, Joceline Lega, Naren Ramakrishnan, Matthew Biggerstaff, Jarad Niemi, Sangwon Hyun, Craig J. McGowan, Matteo Convertino, Jeffrey Shaman, Yang Liu, Michal Ben-Nun, David C. Farrow, Nehemias Ulloa, Evan L. Ray, Katie Will, Wan Yang, Qian Zhang, Logan Brooks, David Bacon, Nicholas Michaud, Carrie Reed, Haruka Morita, James Turtle, Sasikiran Kandula, Karyn M. Apfeldorf, Nicholas G. Reich, Pete Riley, Ryan Tibshirani, Saurav Ghosh, Roni Rosenfeld, Alessandro Vespignani, Steven Riley, Michael Johansson, John Freeze

2019

Overview

Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015–2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza forecasting challenges and utilization of ensemble forecasting techniques. The mean ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts.

• Publisher: Springer Science and Business Media LLC,Nature Publishing Group UK,Nature Publishing Group
• Subject: 631/114/2397 / 692/308/174 / 692/699/255/1578 / Biodefense / Centers for Disease Control and Prevention / Centers for Disease Control and Prevention, U.S / Disease Outbreaks / Emerging Infectious Diseases / Forecasting / Forecasting techniques / Human / Humanities and Social Sciences / Humans / Infectious Diseases / Influenza / Influenza Forecasting Working Group / Influenza, Human / Influenza, Human - epidemiology / Influenza, Human - mortality / Models, Statistical / Morbidity / multidisciplinary / Pneumonia & Influenza / Prevention / Public health / Science / Science (multidisciplinary) / Seasons / Statistical / U.S / United States / United States - epidemiology / Vaccine Related