ArticlePloS one2021
Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Bridging Traditional Modeling and Artificial Intelligence in Measles Epidemiology: Methods, Applications, and Future Directions-A Narrative Review.Journal of clinical medicine · 2026Review
- Cross-lingual transfer with multilingual language models for influenza-like-illness detection in social media texts.npj digital public health · 2026Article
- A novel graph neural network based approach for influenza-like illness nowcasting: exploring the interplay of temporal, geographical, and functional spatial features.BMC public health · 2025Article
- Multisite development and validation of machine learning models to predict severe outcomes and guide decision-making for emergency department patients with influenza.Journal of the American College of Emergency Physicians open · 2024Article
- Early warning of emerging infectious diseases based on multimodal data.Biosafety and health · 2023Review
- Multisite evaluation of prediction models for emergency department crowding before and during the COVID-19 pandemic.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Real-world data: a brief review of the methods, applications, challenges and opportunities.BMC medical research methodology · 2022Review
- Article
- Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models.Journal of medical Internet research · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza activity information that lags real-time by one to three weeks. This temporal data gap introduces uncertainty that prevents public health officials from having a timely perspective on the population-level disease activity. Here, we present a machine-learning modeling approach that produces real-time estimates and short-term forecasts of influenza activity for the twelve continental regions of France by leveraging multiple disparate data sources that include, Google search activity, real-time and local weather information, flu-related Twitter micro-blogs, electronic health records data, and historical disease activity synchronicities across regions. Our results show that all data sources contribute to improving influenza surveillance and that machine-learning ensembles that combine all data sources lead to accurate and timely predictions.
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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.