ArticleBMC infectious diseases2016
Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge.
Article in BMC infectious diseases, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers, 1 of them a synthesis that pooled it.
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99 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
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- A Software Platform for Collaborative Infectious Disease Modeling.Nature health · 2026Article
- Local Influenza Forecasts Outperform State-Level Forecasts in the United States.medRxiv : the preprint server for health sciences · 2026Article
- Networked SIRS model with Kalman filter state estimation for epidemic monitoring in Europe.Communications medicine · 2026Article
- Ensemble forecasts of COVID-19 activity to support Australia's pandemic response: 2020-22.PLoS computational biology · 2026Article
- Forecastability of infectious disease time series: are some seasons and pathogens intrinsically more difficult to forecast?PLoS computational biology · 2026Article
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- A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data.Epidemics · 2025Article
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- Optimizing Disease Outbreak Forecast Ensembles.Emerging infectious diseases · 2024Article
- Predicting seasonal influenza outbreaks with regime shift-informed dynamics for improved public health preparedness.Scientific reports · 2024Article
- United States Influenza 2022-2023 Season Characteristics as Inferred from Wastewater Solids, Influenza Hospitalization, and Syndromic Data.Environmental science & technology · 2023Article
- Comparison of statistical approaches to predicting norovirus laboratory reports before and during COVID-19: insights to inform public health surveillance.Scientific reports · 2023Article
- Neural network models for influenza forecasting with associated uncertainty using Web search activity trends.PLoS computational biology · 2023Article
- When the role of uncertainty is…uncertain.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
39 more citing papers are in PubMed but not listed here.
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14 authors.
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Abstract
backgroundEarly insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season.
methodsChallenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet).
resultsNine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones.
conclusionForecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts.
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