ArticleEpidemics2019
Superensemble forecast of respiratory syncytial virus outbreaks at national, regional, and state levels in the United States.
Article in Epidemics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Determining the timing of respiratory syncytial virus (RSV) epidemics: a systematic review, 2016 to 2021; method categorisation and identification of influencing factors.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2024Pooled it
- A disease-agnostic approach to ensemble learning for infectious disease forecasting.Nature communications · 2026Article
- Real-time prediction of influenza and respiratory syncytial virus epidemics in primary care using the Gompertz model.Scientific reports · 2026Article
- Article
- Post-processing and weighted combination of infectious disease nowcasts.PLoS computational biology · 2025Article
- Challenges of COVID-19 Case Forecasting in the US, 2020-2021.PLoS computational biology · 2024Article
- Multi-faceted analysis and prediction for the outbreak of pediatric respiratory syncytial virus.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Simulation of the COVID-19 patient flow and investigation of the future patient arrival using a time-series prediction model: a real-case study.Medical & biological engineering & computing · 2022Article
- Use of mathematical modelling to assess respiratory syncytial virus epidemiology and interventions: a literature review.Journal of mathematical biology · 2022Review
- The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review.Heliyon · 2021Review
- Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019.PLoS neglected tropical diseases · 2019Article
- Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.International journal of forecastingArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Respiratory syncytial virus (RSV) infections peak during the winter months in the United States, yet the timing, intensity, and onset of these outbreaks vary each year. An RSV vaccine is on the cusp of being released; precise models and accurate forecasts of RSV epidemics may prove vital for planning where and when the vaccine should be deployed. Accurate forecasts with sufficient spatial and temporal resolution could also be used to support the prevention or treatment of RSV infections. Previously, we developed and validated an RSV forecast system at the regional scale in the United States. This model-inference system had considerable forecast skill, relative to the historical expectance, for outbreak peak intensity, total outbreak size, and onset, but only marginal skill for predicting the timing of the outbreak peak. Here, we use a superensemble approach to combine three forecasting methods for RSV prediction in the US at three different spatial resolutions: national, regional, and state. At the regional and state levels, we find a substantial improvement of forecast skill, relative to historical expectance, for peak intensity, timing, and onset outbreak up to two months in advance of the predicted outbreak peak. Moreover, due to the greater variability of RSV outbreaks at finer spatial scales, we find that improvement of forecast skill at the state level exceeds that at the regional and national levels. Such finer scale superensemble forecasts may be more relevant for effecting local-scale interventions, particularly in communities with a high burden of RSV infection.
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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.