ArticleInternational journal of forecasting
Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.
Article in International journal of forecasting. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 66 citations in OpenAlex.
- Retrospective evaluation of trained and untrained probabilistic ensemble forecasts for influenza hospital admissions - United States, 2022-2025.Infectious Disease Modelling · 2026Article
- Evaluation of short-term multi-target respiratory forecasts over winter 2024-25 in England using sub-ensemble contribution analyses.PLoS computational biology · 2026Article
- Evaluation of respiratory disease hospitalisation forecasts using synthetic outbreak data.Communications medicine · 2026Article
- Learning shared forecast-error structure to improve ensemble forecasts of seasonal respiratory outbreaks.medRxiv : the preprint server for health sciences · 2026Article
- Tailored forecasting from short time series via meta-learning.Science advances · 2026Article
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- A disease-agnostic approach to ensemble learning for infectious disease forecasting.Nature communications · 2026Article
- Protocol-aware epidemic forecasting across heterogeneous public health surveillance systems.Frontiers in public health · 2026Article
- Multi-Model Ensembles in Infectious Disease and Public Health: Methods, Interpretation, and Implementation in R.Statistics in medicine · 2026Article
- Multi-model approach to understand and predict past and future dengue epidemic dynamics.Royal Society open science · 2025Article
- Data science for pediatric infectious disease: utilizing COVID-19 as a model.Current opinion in infectious diseases · 2025Review
- The future of HIV: challenges in meeting the 2030 Ending the HIV Epidemic in the US (EHE) reduction goal.AIDS (London, England) · 2025Article
- Fine-grained forecasting of COVID-19 trends at the county level in the United States.NPJ digital medicine · 2025Article
- Post-processing and weighted combination of infectious disease nowcasts.PLoS computational biology · 2025Article
- Article
- Nowcasting reported covid-19 hospitalizations using de-identified, aggregated medical insurance claims data.PLoS computational biology · 2025Article
- The Future of HIV: Challenges in meeting the 2030medRxiv : the preprint server for health sciences · 2025Article
- PLANES: Plausibility analysis of epidemiological signals.PloS one · 2025Article
- Keeping a modeling-driven public health dashboard relevant-lessons learned from the California Communicable diseases Assessment Tool.Frontiers in public health · 2025Article
- Article
Corrections and comments
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Authors and funding
15 authors at 6 institutions in 3 countries.
Funding
Abstract
The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policymakers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision-makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful.
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Registered trials
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.