Real-time prediction of severe influenza epidemics using extreme value statistics
Artikel i vetenskaplig tidskrift, 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

Författare

Maud Thomas

Sorbonne Université

Holger Rootzen

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Göteborgs universitet

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

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

Vol. 71 2 376-394

Ämneskategorier

Vattenteknik

Bioinformatik (beräkningsbiologi)

Miljömedicin och yrkesmedicin

DOI

10.1111/rssc.12537

Mer information

Senast uppdaterat

2022-04-05