Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers

Human influenza occurs annually in most temperate climatic zones of the world, with epidemics peaking in the cold winter months. Considerable debate surrounds the relative role of epidemic dynamics, viral evolution, and climatic drivers in driving year-to-year variability of outbreaks. The ultimate...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2014-07, Vol.111 (26), p.9538-9542
Main Authors: Axelsen, Jacob Bock, Yaari, Rami, Grenfell, Bryan T., Stone, Lewi
Format: Article
Language:English
Subjects:
Antigenic variation
Bayes Theorem
Bayesian analysis
Biological Evolution
Biological Sciences
Climate
Climate models
Correlations
Disease models
Epidemics
Epidemiology
Epitopes - genetics
Forecasting
Forecasting - methods
Forecasting models
Humans
Incidence
Influenza
Influenza A virus - genetics
Influenza A virus - immunology
Influenza, Human - epidemiology
Israel - epidemiology
Likelihood Functions
Modeling
Models, Biological
Pandemics
Seasons
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Summary:Human influenza occurs annually in most temperate climatic zones of the world, with epidemics peaking in the cold winter months. Considerable debate surrounds the relative role of epidemic dynamics, viral evolution, and climatic drivers in driving year-to-year variability of outbreaks. The ultimate test of understanding is prediction; however, existing influenza models rarely forecast beyond a single year at best. Here, we use a simple epidemiological model to reveal multiannual predictability based on high-quality influenza surveillance data for Israel; the model fit is corroborated by simple metapopulation comparisons within Israel. Successful forecasts are driven by temperature, humidity, antigenic drift, and immunity loss. Essentially, influenza dynamics are a balance between large perturbations following significant antigenic jumps, interspersed with nonlinear epidemic dynamics tuned by climatic forcing.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1321656111