ReviewReviews in medical virology2026
Forecasting Influenza Epidemics and Pandemics in the Age of AI and Machine Learning.
Review in Reviews in medical virology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Emergence and Global Circulation of the HRSV BA Variant with a 7-AA Extension in the G Protein Under Persistent BA Predominance - Global, 1996-2025.China CDC weekly · 2026Article
- From sites to structure to serology: a roadmap for structure-aware molecular evolution of antigenically evolving viruses.Journal of virology · 2026Review
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Forecasting Influenza Epidemics and Pandemics in the Age of AI and Machine Learning.Reviews in medical virology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Influenza's rapid evolution, driven by its segmented RNA genome, high mutation rate, and extensive animal reservoirs, underpins its capacity to cause recurring epidemics and unpredictable pandemics. Recent advances in artificial intelligence (AI) and machine learning (ML) are transforming influenza forecasting by enabling the prediction of viral evolution and the optimisation of public health preparedness. This review synthesises insights from historical data (1890-2025) and contemporary research to examine the evolving role of AI in influenza prediction. It highlights major developments including transformer-based models for viral evolution, real-time integration of mobility and environmental data, hybrid quantum, which are classical algorithms, and multimodal data fusion frameworks, it also consideres critical risk modifiers such as meteorological variation, armed conflict, and host genetics. Importantly, the review distinguishes between retrospective, proof-of-concept analyses and prospective, real-time forecasting applications, clarifying their respective contributions to operational public health preparedness and informed decision-making.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.