ArticlePLoS computational biology2018
Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Learning shared forecast-error structure to improve ensemble forecasts of seasonal respiratory outbreaks.medRxiv : the preprint server for health sciences · 2026Article
- Real-time prediction of influenza and respiratory syncytial virus epidemics in primary care using the Gompertz model.Scientific reports · 2026Article
- A stacked ensemble model with NNLS-based weighting for influenza forecasting: a case study of Anhui Province, China.Frontiers in public health · 2026Article
- Real-time forecasting of data revisions in epidemic surveillance streams.PLoS computational biology · 2025Article
- Fine-grained forecasting of COVID-19 trends at the county level in the United States.NPJ digital medicine · 2025Article
- A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data.Epidemics · 2025Article
- Integrating information from historical data into mechanistic models for influenza forecasting.PLoS computational biology · 2024Article
- Challenges of COVID-19 Case Forecasting in the US, 2020-2021.PLoS computational biology · 2024Article
- Neural network models for influenza forecasting with associated uncertainty using Web search activity trends.PLoS computational biology · 2023Article
- Joint COVID-19 and influenza-like illness forecasts in the United States using internet search information.Communications medicine · 2023Article
- Dynamic downscaling and daily nowcasting from influenza surveillance data.Statistics in medicine · 2022Article
- Addressing delayed case reporting in infectious disease forecast modeling.PLoS computational biology · 2022Article
- An endemic-epidemic beta model for time series of infectious disease proportions.Journal of applied statistics · 2022Article
- Fast and accurate influenza forecasting in the United States with Inferno.PLoS computational biology · 2022Article
- Adaptively stacking ensembles for influenza forecasting.Statistics in medicine · 2021Article
- An open repository of real-time COVID-19 indicators.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.PLoS neglected tropical diseases · 2021Article
- COVID-19: Estimation of the transmission dynamics in Spain using a stochastic simulator and black-box optimization techniques.Computer methods and programs in biomedicine · 2021Article
- Trade-offs between individual and ensemble forecasts of an emerging infectious disease.Nature communications · 2021Article
- Accurate influenza forecasts using type-specific incidence data for small geographic units.PLoS computational biology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Accurate and reliable forecasts of seasonal epidemics of infectious disease can assist in the design of countermeasures and increase public awareness and preparedness. This article describes two main contributions we made recently toward this goal: a novel approach to probabilistic modeling of surveillance time series based on "delta densities", and an optimization scheme for combining output from multiple forecasting methods into an adaptively weighted ensemble. Delta densities describe the probability distribution of the change between one observation and the next, conditioned on available data; chaining together nonparametric estimates of these distributions yields a model for an entire trajectory. Corresponding distributional forecasts cover more observed events than alternatives that treat the whole season as a unit, and improve upon multiple evaluation metrics when extracting key targets of interest to public health officials. Adaptively weighted ensembles integrate the results of multiple forecasting methods, such as delta density, using weights that can change from situation to situation. We treat selection of optimal weightings across forecasting methods as a separate estimation task, and describe an estimation procedure based on optimizing cross-validation performance. We consider some details of the data generation process, including data revisions and holiday effects, both in the construction of these forecasting methods and when performing retrospective evaluation. The delta density method and an adaptively weighted ensemble of other forecasting methods each improve significantly on the next best ensemble component when applied separately, and achieve even better cross-validated performance when used in conjunction. We submitted real-time forecasts based on these contributions as part of CDC's 2015/2016 FluSight Collaborative Comparison. Among the fourteen submissions that season, this system was ranked by CDC as the most accurate.
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