ArticleStatistics in medicine2021
Adaptively stacking ensembles for influenza forecasting.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Expanding optimization ensemble model methods for forecasting seasonal influenza in the U.S.Infectious Disease Modelling · 2026Article
- Retrospective evaluation of trained and untrained probabilistic ensemble forecasts for influenza hospital admissions - United States, 2022-2025.Infectious Disease Modelling · 2026Article
- Learning shared forecast-error structure to improve ensemble forecasts of seasonal respiratory outbreaks.medRxiv : the preprint server for health sciences · 2026Article
- A Software Platform for Collaborative Infectious Disease Modeling.Nature health · 2026Article
- A disease-agnostic approach to ensemble learning for infectious disease forecasting.Nature communications · 2026Article
- Multi-Model Ensembles in Infectious Disease and Public Health: Methods, Interpretation, and Implementation in R.Statistics in medicine · 2026Article
- Comparing imputation approaches to handle systematically missing inputs in risk calculators.PLOS digital health · 2025Article
- An adaptive weight ensemble approach to forecast influenza activity in an irregular seasonality context.Nature communications · 2024Article
- Challenges of COVID-19 Case Forecasting in the US, 2020-2021.PLoS computational biology · 2024Article
- Comparison of combination methods to create calibrated ensemble forecasts for seasonal influenza in the U.S.Statistics in medicine · 2023Article
- Repeatability and timing of tropical influenza epidemics.PLoS computational biology · 2023Article
- Application progress of ensemble forecast technology in influenza forecast based on infectious disease model.Frontiers in public health · 2023Review
- Supporting COVID-19 policy-making with a predictive epidemiological multi-model warning system.Communications medicine · 2022Article
- Chimeric forecasting: combining probabilistic predictions from computational models and human judgment.BMC infectious diseases · 2022Article
- Aggregating human judgment probabilistic predictions of the safety, efficacy, and timing of a COVID-19 vaccine.Vaccine · 2022Article
- Trade-offs between individual and ensemble forecasts of an emerging infectious disease.Nature communications · 2021Article
- Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S.PLoS computational biology · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Seasonal influenza infects between 10 and 50 million people in the United States every year. Accurate forecasts of influenza and influenza-like illness (ILI) have been named by the CDC as an important tool to fight the damaging effects of these epidemics. Multi-model ensembles make accurate forecasts of seasonal influenza, but current operational ensemble forecasts are static: they require an abundance of past ILI data and assign fixed weights to component models at the beginning of a season, but do not update weights as new data on component model performance is collected. We propose an adaptive ensemble that (i) does not initially need data to combine forecasts and (ii) finds optimal weights which are updated week-by-week throughout the influenza season. We take a regularized likelihood approach and investigate this regularizer's ability to impact adaptive ensemble performance. After finding an optimal regularization value, we compare our adaptive ensemble to an equal-weighted and static ensemble. Applied to forecasts of short-term ILI incidence at the regional and national level, our adaptive model outperforms an equal-weighted ensemble and has similar performance to the static ensemble using only a fraction of the data available to the static ensemble. Needing no data at the beginning of an epidemic, an adaptive ensemble can quickly train and forecast an outbreak, providing a practical tool to public health officials looking for a forecast to conform to unique features of a specific season.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.