ArticleNature communications2021
Multiscale influenza forecasting.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Stage-aware deep learning with adversarial data synthesis enables prospective season-scale A/H1N1 influenza forecasting in China.Infectious Disease Modelling · 2027Article
- Multisource Early Warning Framework Integrating Regional Pathogen Surveillance and Baidu Index Data for Respiratory Infectious Disease Prediction in Beijing: Retrospective Time-Series Modeling Study.JMIR formative research · 2026Article
- Leveraging synthetic and genetic data to improve epidemic forecasting.PLoS computational biology · 2026Article
- When to vaccinate for seasonal influenza: check the peak forecast.BMC public health · 2025Article
- How multi-scale modeling can help examine social determinants of health and resulting disparities.PLoS computational biology · 2025Article
- Competition between transmission lineages mediated by human mobility shapes seasonal influenza epidemics in the US.Nature communications · 2025Article
- Article
- An adaptive weight ensemble approach to forecast influenza activity in an irregular seasonality context.Nature communications · 2024Article
- A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases.BMC infectious diseases · 2024Article
- Commuting-driven competition between transmission chains shapes seasonal influenza virus epidemics in the United States.medRxiv : the preprint server for health sciences · 2024Article
- Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention.Current medical science · 2024Article
- Identify future epidemic threshold and intensity for influenza-like illness in Iraq by using the moving epidemic method.IJID regions · 2024Article
- Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models.Communications medicine · 2023Article
- Neural network models for influenza forecasting with associated uncertainty using Web search activity trends.PLoS computational biology · 2023Article
- A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases, hospitalizations and deaths.Journal of the Royal Society, Interface · 2022Article
- Fast and accurate influenza forecasting in the United States with Inferno.PLoS computational biology · 2022Article
- Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.International journal of forecastingArticle
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Influenza forecasting in the United States (US) is complex and challenging due to spatial and temporal variability, nested geographic scales of interest, and heterogeneous surveillance participation. Here we present Dante, a multiscale influenza forecasting model that learns rather than prescribes spatial, temporal, and surveillance data structure and generates coherent forecasts across state, regional, and national scales. We retrospectively compare Dante's short-term and seasonal forecasts for previous flu seasons to the Dynamic Bayesian Model (DBM), a leading competitor. Dante outperformed DBM for nearly all spatial units, flu seasons, geographic scales, and forecasting targets. Dante's sharper and more accurate forecasts also suggest greater public health utility. Dante placed 1st in the Centers for Disease Control and Prevention's prospective 2018/19 FluSight challenge in both the national and regional competition and the state competition. The methodology underpinning Dante can be used in other seasonal disease forecasting contexts having nested geographic scales of interest.
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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.