ArticlePLoS computational biology2023
Neural network models for influenza forecasting with associated uncertainty using Web search activity trends.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Basic Baseline model design choices can substantially influence performance in collaborative forecast hubs.medRxiv : the preprint server for health sciences · 2026Article
- Learning from pandemics: Feature selection and clustering for robust digital disease nowcasting.PloS one · 2026Article
- FaXNet: a frequency-adaptive, explainable, and uncertainty-aware network for influenza forecasting.Frontiers in public health · 2026Article
- Predicting influenza in the post-COVID era: assessing LSTM, GRU, and transformer robustness to covariate shift.Frontiers in artificial intelligence · 2026Article
- Forecasting Influenza Epidemics and Pandemics in the Age of AI and Machine Learning.Reviews in medical virology · 2026Review
- Dynamic ensemble deep learning with multi-source data for robust influenza forecasting in Yangzhou.BMC public health · 2025Article
- Deep recurrent models for forecasting infectious diseases.Frontiers in public health · 2025Article
- Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model.BMC public health · 2024Article
- Using meta-learning to recommend an appropriate time-series forecasting model.BMC public health · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Influenza affects millions of people every year. It causes a considerable amount of medical visits and hospitalisations as well as hundreds of thousands of deaths. Forecasting influenza prevalence with good accuracy can significantly help public health agencies to timely react to seasonal or novel strain epidemics. Although significant progress has been made, influenza forecasting remains a challenging modelling task. In this paper, we propose a methodological framework that improves over the state-of-the-art forecasting accuracy of influenza-like illness (ILI) rates in the United States. We achieve this by using Web search activity time series in conjunction with historical ILI rates as observations for training neural network (NN) architectures. The proposed models incorporate Bayesian layers to produce associated uncertainty intervals to their forecast estimates, positioning themselves as legitimate complementary solutions to more conventional approaches. The best performing NN, referred to as the iterative recurrent neural network (IRNN) architecture, reduces mean absolute error by 10.3% and improves skill by 17.1% on average in nowcasting and forecasting tasks across 4 consecutive flu seasons.
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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.