Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A Collaborative Multi-Model Ensemble for Real-Time Influenza Season Forecasting in the U.S

View ORCID ProfileNicholas G Reich, View ORCID ProfileCraig J McGowan, View ORCID ProfileTeresa K Yamana, Abhinav Tushar, Evan L Ray, Dave Osthus, Sasikiran Kandula, Logan C Brooks, Willow Crawford-Crudell, Graham Casey Gibson, Evan Moore, Rebecca Silva, Matthew Biggerstaff, Michael A Johansson, Roni Rosenfeld, Jeffrey Shaman
doi: https://doi.org/10.1101/566604
Nicholas G Reich
1University of Massachusetts-Amherst, Amherst, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Nicholas G Reich
Craig J McGowan
2Influenza Division, Centers for Disease Control and Prevention, Atlanta, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Craig J McGowan
Teresa K Yamana
3Columbia University, New York, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Teresa K Yamana
Abhinav Tushar
1University of Massachusetts-Amherst, Amherst, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Evan L Ray
4Mount Holyoke College, South Hadley, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Dave Osthus
5Los Alamos National Laboratory, Los Alamos, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sasikiran Kandula
3Columbia University, New York, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Logan C Brooks
6Carnegie Mellon University, Pittsburgh, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Willow Crawford-Crudell
7Smith College, Northampton, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Graham Casey Gibson
1University of Massachusetts-Amherst, Amherst, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Evan Moore
1University of Massachusetts-Amherst, Amherst, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rebecca Silva
8Amherst College, Amherst, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Matthew Biggerstaff
2Influenza Division, Centers for Disease Control and Prevention, Atlanta, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michael A Johansson
9Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Roni Rosenfeld
6Carnegie Mellon University, Pittsburgh, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jeffrey Shaman
3Columbia University, New York, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

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 based on that component’s forecast accuracy in 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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
Back to top
PreviousNext
Posted March 08, 2019.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A Collaborative Multi-Model Ensemble for Real-Time Influenza Season Forecasting in the U.S
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A Collaborative Multi-Model Ensemble for Real-Time Influenza Season Forecasting in the U.S
Nicholas G Reich, Craig J McGowan, Teresa K Yamana, Abhinav Tushar, Evan L Ray, Dave Osthus, Sasikiran Kandula, Logan C Brooks, Willow Crawford-Crudell, Graham Casey Gibson, Evan Moore, Rebecca Silva, Matthew Biggerstaff, Michael A Johansson, Roni Rosenfeld, Jeffrey Shaman
bioRxiv 566604; doi: https://doi.org/10.1101/566604
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
A Collaborative Multi-Model Ensemble for Real-Time Influenza Season Forecasting in the U.S
Nicholas G Reich, Craig J McGowan, Teresa K Yamana, Abhinav Tushar, Evan L Ray, Dave Osthus, Sasikiran Kandula, Logan C Brooks, Willow Crawford-Crudell, Graham Casey Gibson, Evan Moore, Rebecca Silva, Matthew Biggerstaff, Michael A Johansson, Roni Rosenfeld, Jeffrey Shaman
bioRxiv 566604; doi: https://doi.org/10.1101/566604

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Epidemiology
Subject Areas
All Articles
  • Animal Behavior and Cognition (3506)
  • Biochemistry (7348)
  • Bioengineering (5324)
  • Bioinformatics (20266)
  • Biophysics (10020)
  • Cancer Biology (7744)
  • Cell Biology (11306)
  • Clinical Trials (138)
  • Developmental Biology (6437)
  • Ecology (9954)
  • Epidemiology (2065)
  • Evolutionary Biology (13325)
  • Genetics (9361)
  • Genomics (12587)
  • Immunology (7702)
  • Microbiology (19027)
  • Molecular Biology (7444)
  • Neuroscience (41049)
  • Paleontology (300)
  • Pathology (1230)
  • Pharmacology and Toxicology (2138)
  • Physiology (3161)
  • Plant Biology (6861)
  • Scientific Communication and Education (1273)
  • Synthetic Biology (1897)
  • Systems Biology (5313)
  • Zoology (1089)