Real-time prediction of severe influenza epidemics using Extreme Value Statistics

Maud Thomas (1) , Holger Rootzén (2)

1 LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation
2 Department of Mathematical Sciences

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 the weekly influenza case reporting in France, we use recent results on multivariate GP models in Extreme Value Statistics to develop methods for real-time prediction of the risk that an ongoing epidemic will be exceptionally severe and for real-time detection of anomalous epidemics. Quality of predictions is assessed on observed and simulated data.

Mots clés: Anomaly detection; Extreme Value Statistics; Generalized Pareto models; Influenza epidemics; Real-time prediction; Real-time prediction 1

Domaines: Applications [stat.AP]

Maud Thomas, Holger Rootzén. Real-time prediction of severe influenza epidemics using Extreme Value Statistics. Journal of the Royal Statistical Society: Series C Applied Statistics, 2021. ⟨hal-02332898v2⟩