tetano
Editor, Senior Moderator
Proc Natl Acad Sci U S A. 2014 Jun 16. pii: 201321656. [Epub ahead of print]
Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers.
Axelsen JB1, Yaari R2, Grenfell BT3, Stone L4.
Author information
Abstract
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.
KEYWORDS:
Bayesian epidemic model; climate; infectious disease; model forecasting; predictive model
PMID:
24979763
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/24979763
Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers.
Axelsen JB1, Yaari R2, Grenfell BT3, Stone L4.
Author information
Abstract
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.
KEYWORDS:
Bayesian epidemic model; climate; infectious disease; model forecasting; predictive model
PMID:
24979763
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/24979763