ArticleScientific reports2015
Multi-scale modeling for the transmission of influenza and the evaluation of interventions toward it.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Multi-source agent-based modeling to optimize influenza mitigation strategies in Hong Kong.Nature communications · 2026Article
- A study on the climate-driven spatiotemporal dynamics of influenza in Lanzhou spanning the COVID-19 era.Frontiers in cellular and infection microbiology · 2026Article
- FaXNet: a frequency-adaptive, explainable, and uncertainty-aware network for influenza forecasting.Frontiers in public health · 2026Article
- Article
- The long-term effectiveness and cost-effectiveness of public health interventions; how can we model behavior? A review.Health economics · 2023Review
- Who Gets the Flu? Individualized Validation of Influenza-like Illness in Urban Spaces.International journal of environmental research and public health · 2023Article
- Agent-based model of the impact of higher influenza vaccine efficacy on seasonal influenza burden.Vaccine: X · 2023Article
- Impact of Low Rates of Influenza on Next-Season Influenza Infections.American journal of preventive medicine · 2022Article
- Analysis of multi-strain infection of vaccinated and recovered population through epidemic model: Application to COVID-19.PloS one · 2022Article
- Catch Me if You Can: Superspreading of COVID-19.Trends in microbiology · 2021Review
- Article
- The relationship between cyclonic weather regimes and seasonal influenza over the Eastern Mediterranean.The Science of the total environment · 2021Article
- Influenza spread on context-specific networks lifted from interaction-based diary data.Royal Society open science · 2021Article
- Misinformation making a disease outbreak worse: outcomes compared for influenza, monkeypox, and norovirus.Simulation · 2020Article
- Article
- Influenza surveillance: determining the epidemic threshold for influenza by using the Moving Epidemic Method (MEM), Montenegro, 2010/11 to 2017/18 influenza seasons.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2019Article
- Infection prevention behaviour and infectious disease modelling: a review of the literature and recommendations for the future.BMC public health · 2018Review
- Mathematical Analysis of Influenza A Dynamics in the Emergence of Drug Resistance.Computational and mathematical methods in medicine · 2018Article
- Mathematical and Computational Modeling in Complex Biological Systems.BioMed research international · 2017Review
- Relevance of workplace social mixing during influenza pandemics: an experimental modelling study of workplace cultures.Epidemiology and infection · 2016Article
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
Abstract
Mathematical modeling of influenza epidemic is important for analyzing the main cause of the epidemic and finding effective interventions towards it. The epidemic is a dynamic process. In this process, daily infections are caused by people's contacts, and the frequency of contacts can be mainly influenced by their cognition to the disease. The cognition is in turn influenced by daily illness attack rate, climate, and other environment factors. Few existing methods considered the dynamic process in their models. Therefore, their prediction results can hardly be explained by the mechanisms of epidemic spreading. In this paper, we developed a heterogeneous graph modeling approach (HGM) to describe the dynamic process of influenza virus transmission by taking advantage of our unique clinical data. We built social network of studied region and embedded an Agent-Based Model (ABM) in the HGM to describe the dynamic change of an epidemic. Our simulations have a good agreement with clinical data. Parameter sensitivity analysis showed that temperature influences the dynamic of epidemic significantly and system behavior analysis showed social network degree is a critical factor determining the size of an epidemic. Finally, multiple scenarios for vaccination and school closure strategies were simulated and their performance was analyzed.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.