ArticleJournal of the Royal Society, Interface2016
Superensemble forecasts of dengue outbreaks.
Article in Journal of the Royal Society, Interface, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- Optimizing wild bird fecal surveillance sites for HPAI through risk estimation using machine learning algorithms.One health (Amsterdam, Netherlands) · 2026Article
- Ensemble forecasts of COVID-19 activity to support Australia's pandemic response: 2020-22.PLoS computational biology · 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
- Predicting high dengue incidence in municipalities of Brazil using path signatures.Scientific reports · 2025Article
- Generalized unscented transformation for forecasting non-Gaussian processes.Physical review. E · 2025Article
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- Post-processing and weighted combination of infectious disease nowcasts.PLoS computational biology · 2025Article
- Advancing adoptability and sustainability of digital prediction tools for climate-sensitive infectious disease prevention and control.Nature communications · 2025Review
- Comparison of combination methods to create calibrated ensemble forecasts for seasonal influenza in the U.S.Statistics in medicine · 2023Article
- Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm.PLoS neglected tropical diseases · 2023Article
- PICTUREE-Aedes: A Web Application for Dengue Data Visualization and Case Prediction.Pathogens (Basel, Switzerland) · 2023Article
- Hindcasts and forecasts of suicide mortality in US: A modeling study.PLoS computational biology · 2023Observational
- Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations.PLoS computational biology · 2023Article
- Context-dependent representation of within- and between-model uncertainty: aggregating probabilistic predictions in infectious disease epidemiology.Journal of the Royal Society, Interface · 2023Article
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- An ensemble n-sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA.PLoS computational biology · 2022Article
- Comparing human and model-based forecasts of COVID-19 in Germany and Poland.PLoS computational biology · 2022Article
- An ensemblemedRxiv : the preprint server for health sciences · 2022Article
- An ensemble forecast system for tracking dynamics of dengue outbreaks and its validation in China.PLoS computational biology · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
In recent years, a number of systems capable of predicting future infectious disease incidence have been developed. As more of these systems are operationalized, it is important that the forecasts generated by these different approaches be formally reconciled so that individual forecast error and bias are reduced. Here we present a first example of such multi-system, or superensemble, forecast. We develop three distinct systems for predicting dengue, which are applied retrospectively to forecast outbreak characteristics in San Juan, Puerto Rico. We then use Bayesian averaging methods to combine the predictions from these systems and create superensemble forecasts. We demonstrate that on average, the superensemble approach produces more accurate forecasts than those made from any of the individual forecasting systems.
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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.