Real-time prediction of severe influenza epidemics using extreme value statistics

Each year, seasonal influenza epidemics cause hundreds of thousands of deaths worldwide and put high loads on health care systems. A main concern for resource planning is the risk of exceptionally severe epidemics. Taking advantage of recent results on multivariate Generalized Pareto models in extreme value statistics we develop methods for real-time prediction of the risk that an ongoing influenza epidemic will be exceptionally severe and for real-time detection of anomalous epidemics and use them for prediction and detection of anomalies for influenza epidemics in France. Quality of predictions is assessed on observed and simulated data.

Automated summary

A method for real-time prediction and detection of exceptionally severe influenza epidemics using multivariate Generalized Pareto models in extreme value statistics is developed and tested in France. The quality of predictions is assessed using observed and simulated data.