SynthesisInfluenza and other respiratory viruses2014
A systematic review of studies on forecasting the dynamics of influenza outbreaks.
Synthesis in Influenza and other respiratory viruses, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers, 3 of them syntheses that pooled it.
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.
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Who cites it
99 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action.Epidemics · 2020Pooled it
- Epidemiological models for predicting Ross River virus in Australia: A systematic review.PLoS neglected tropical diseases · 2020Pooled it
- Pooled it
- Influenza-Like Illness Forecasting Using Multisource Data: Comparative Deep Learning Study.JMIR medical informatics · 2026Article
- Measuring population immunity against influenza using individual antibody titres: a multicountry, retrospective observational study.The Lancet. Infectious diseases · 2026Observational
- Understanding spatiotemporal clustering of seasonal influenza in the United States.BMC infectious diseases · 2026Article
- Leveraging universal and transfer learning models for influenza prediction in Thailand.Scientific reports · 2026Article
- FaXNet: a frequency-adaptive, explainable, and uncertainty-aware network for influenza forecasting.Frontiers in public health · 2026Article
- Mapping Incidence and Prevalence Peak Data for SIR Modeling Applications.Journal of mathematical biology · 2025Article
- Prediction of influenza-like illness incidence using meteorological factors in Kunming : deep learning model study.BMC public health · 2025Article
- Connecting mass-action models and network models for infectious diseases.PLoS computational biology · 2025Article
- Analysis of influenza-like illness trends in Saudi Arabia: a comparative study of statistical and deep learning techniques.Osong public health and research perspectives · 2025Article
- Drug utilisation patterns & clinical outcomes in hospitalised COVID-19 patients: A geospatial & machine learning approach.The Indian journal of medical research · 2025Article
- Influenza Vaccination of Nurses and Other Health Care Workers in Different Occupational Settings: A Classic and AI Mixed Approach for Time-to-Event Data.Nursing reports (Pavia, Italy) · 2025Article
- Spectral study of COVID-19 pandemic in Japan: The dependence of spectral gradient on the population size of the community.PloS one · 2025Article
- Predicting seasonal influenza outbreaks with regime shift-informed dynamics for improved public health preparedness.Scientific reports · 2024Article
- What's next? Forecasting scientific research trends.Heliyon · 2024Article
- Incorporating connectivity among Internet search data for enhanced influenza-like illness tracking.PloS one · 2024Article
- Article
- Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models.Communications medicine · 2023Article
39 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Forecasting the dynamics of influenza outbreaks could be useful for decision-making regarding the allocation of public health resources. Reliable forecasts could also aid in the selection and implementation of interventions to reduce morbidity and mortality due to influenza illness. This paper reviews methods for influenza forecasting proposed during previous influenza outbreaks and those evaluated in hindsight. We discuss the various approaches, in addition to the variability in measures of accuracy and precision of predicted measures. PubMed and Google Scholar searches for articles on influenza forecasting retrieved sixteen studies that matched the study criteria. We focused on studies that aimed at forecasting influenza outbreaks at the local, regional, national, or global level. The selected studies spanned a wide range of regions including USA, Sweden, Hong Kong, Japan, Singapore, United Kingdom, Canada, France, and Cuba. The methods were also applied to forecast a single measure or multiple measures. Typical measures predicted included peak timing, peak height, daily/weekly case counts, and outbreak magnitude. Due to differences in measures used to assess accuracy, a single estimate of predictive error for each of the measures was difficult to obtain. However, collectively, the results suggest that these diverse approaches to influenza forecasting are capable of capturing specific outbreak measures with some degree of accuracy given reliable data and correct disease assumptions. Nonetheless, several of these approaches need to be evaluated and their performance quantified in real-time predictions.
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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.