Real-time prediction of severe influenza epidemics using extreme value statistics
Journal article, 2022

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.

extreme value statistics

influenza epidemics

real-time prediction of extremes

anomaly detection

generalized pareto models


Maud Thomas

Sorbonne University

Holger Rootzen

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

University of Gothenburg

Journal of the Royal Statistical Society. Series C: Applied Statistics

0035-9254 (ISSN) 1467-9876 (eISSN)

Vol. 71 2 376-394

Subject Categories

Water Engineering

Bioinformatics (Computational Biology)

Environmental Health and Occupational Health



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4/5/2022 5