ArticleEpidemics2018
The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt.
Article in Epidemics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 126 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
126 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Marburg virus disease outbreaks, mathematical models, and disease parameters: a systematic review.The Lancet. Infectious diseases · 2024Pooled it
- Mechanistic models for West Nile virus transmission: a systematic review of features, aims and parametrization.Proceedings. Biological sciences · 2024Pooled it
- A real-time forecasting framework for emerging infectious diseases affecting animal populations.PLoS computational biology · 2026Article
- Adaptive multi-model ensembles for improved epidemic projections and decision support.medRxiv : the preprint server for health sciences · 2026Article
- Predictive Modeling of Enterovirus Hospital Burden Using Machine Learning and Age-Specific Surveillance Data: Operational Forecasting in Taiwan During the Postpandemic Era.JMIR formative research · 2026Article
- 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
- AZD5582 robustly reactivates latently infected cells and clears the majority of those reactivated from the SIV reservoir.bioRxiv : the preprint server for biology · 2026Article
- Forecastability of infectious disease time series: are some seasons and pathogens intrinsically more difficult to forecast?PLoS computational biology · 2026Article
- A disease-agnostic approach to ensemble learning for infectious disease forecasting.Nature communications · 2026Article
- Basic Baseline model design choices can substantially influence performance in collaborative forecast hubs.medRxiv : the preprint server for health sciences · 2026Article
- Leveraging probabilistic forecasts for dengue preparedness and control: The 2024 Dengue Forecasting Sprint in Brazil.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Multi-Model Ensembles in Infectious Disease and Public Health: Methods, Interpretation, and Implementation in R.Statistics in medicine · 2026Article
- Coordinating collaborative infectious disease modeling projects with the hubverse.medRxiv : the preprint server for health sciences · 2025Article
- Microbiome-Immune Interaction and Harnessing for Next-Generation Vaccines Against Highly Pathogenic Avian Influenza in Poultry.Vaccines · 2025Review
- Article
- What can we learn from 100,000 freshwater forecasts? A synthesis from the NEON Ecological Forecasting Challenge.Ecological applications : a publication of the Ecological Society of America · 2025Article
- When are predictions useful? A new method for evaluating epidemic forecasts.BMC global and public health · 2024Article
- Integrating information from historical data into mechanistic models for influenza forecasting.PLoS computational biology · 2024Article
- Mathematical modelling for pandemic preparedness in Canada: Learning from COVID-19.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2024Article
66 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Infectious disease forecasting is gaining traction in the public health community; however, limited systematic comparisons of model performance exist. Here we present the results of a synthetic forecasting challenge inspired by the West African Ebola crisis in 2014-2015 and involving 16 international academic teams and US government agencies, and compare the predictive performance of 8 independent modeling approaches. Challenge participants were invited to predict 140 epidemiological targets across 5 different time points of 4 synthetic Ebola outbreaks, each involving different levels of interventions and "fog of war" in outbreak data made available for predictions. Prediction targets included 1-4 week-ahead case incidences, outbreak size, peak timing, and several natural history parameters. With respect to weekly case incidence targets, ensemble predictions based on a Bayesian average of the 8 participating models outperformed any individual model and did substantially better than a null auto-regressive model. There was no relationship between model complexity and prediction accuracy; however, the top performing models for short-term weekly incidence were reactive models with few parameters, fitted to a short and recent part of the outbreak. Individual model outputs and ensemble predictions improved with data accuracy and availability; by the second time point, just before the peak of the epidemic, estimates of final size were within 20% of the target. The 4th challenge scenario - mirroring an uncontrolled Ebola outbreak with substantial data reporting noise - was poorly predicted by all modeling teams. Overall, this synthetic forecasting challenge provided a deep understanding of model performance under controlled data and epidemiological conditions. We recommend such "peace time" forecasting challenges as key elements to improve coordination and inspire collaboration between modeling groups ahead of the next pandemic threat, and to assess model forecasting accuracy for a variety of known and hypothetical pathogens.
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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.