ArticleScientific reports2018
Shifting patterns of seasonal influenza epidemics.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Wildfire-Season Fine Particulate Matter Exposure and Associations with Influenza and Influenza-like-Illness Risk in the Western USA.Environmental health perspectives · 2026Article
- Detecting changepoints in dynamical systems: Modeling time-varying transmission of seasonal influenza.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- An analysis of WHO FluNet and FluID influenza surveillance data for South East Asia Region, 2015-2023.PloS one · 2026Article
- Seasonality and mycobacterial infectious diseases in animals and humans: is there a generality of seasonal patterns for mycobacterial infections?Infectious diseases of poverty · 2025Review
- Spatiotemporal patterns of influenza in Western Australia.Public health in practice (Oxford, England) · 2025Article
- Retrospective Epidemiological Analysis of Influenza A Infections in a Single Hospital in Korea (2007-2024): Age, Sex, and Seasonal Patterns.Pathogens (Basel, Switzerland) · 2025Article
- Concepts and Methods for Predicting Viral Evolution.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Concepts and methods for predicting viral evolution.bioRxiv : the preprint server for biology · 2024Article
- Article
- Review
- Predicting seasonal influenza outbreaks with regime shift-informed dynamics for improved public health preparedness.Scientific reports · 2024Article
- Addressing the Knowledge Deficit in Hospital Bed Planning and Defining an Optimum Region for the Number of Different Types of Hospital Beds in an Effective Health Care System.International journal of environmental research and public health · 2023Review
- Article
- Spatial aggregation choice in the era of digital and administrative surveillance data.PLOS digital health · 2022Article
- Interplay between mobility, multi-seeding and lockdowns shapes COVID-19 local impact.PLoS computational biology · 2021Article
- Epidemiology of acute respiratory viral infections in children in Vientiane, Lao People's Democratic Republic.Journal of medical virology · 2021Article
- Differences in epidemic spread patterns of norovirus and influenza seasons of Germany: an application of optical flow analysis in epidemiology.Scientific reports · 2020Article
- On the predictability of infectious disease outbreaks.Nature communications · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Seasonal waves of influenza display a complex spatiotemporal pattern resulting from the interplay of biological, sociodemographic, and environmental factors. At country level many studies characterized the robust properties of annual epidemics, depicting a typical season. Here we analyzed season-by-season variability, introducing a clustering approach to assess the deviations from typical spreading patterns. The classification is performed on the similarity of temporal configurations of onset and peak times of regional epidemics, based on influenza-like-illness time-series in France from 1984 to 2014. We observed a larger variability in the onset compared to the peak. Two relevant classes of clusters emerge: groups of seasons sharing similar recurrent spreading patterns (clustered seasons) and single seasons displaying unique patterns (monoids). Recurrent patterns exhibit a more pronounced spatial signature than unique patterns. We assessed how seasons shift between these classes from onset to peak depending on epidemiological, environmental, and socio-demographic variables. We found that the spatial dynamics of influenza and its association with commuting, previously observed as a general property of French influenza epidemics, apply only to seasons exhibiting recurrent patterns. The proposed methodology is successful in providing new insights on influenza spread and can be applied to incidence time-series of different countries and different diseases.
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