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

TL;DR

This paper introduces real-time prediction methods for severe influenza epidemics using Extreme Value Statistics, aiding health resource planning by identifying exceptionally severe outbreaks and anomalies in France.

Contribution

It develops novel multivariate Generalized Pareto models for real-time epidemic severity prediction and anomaly detection, advancing statistical tools in epidemiology.

Findings

Effective real-time prediction of severe epidemics demonstrated.

Models successfully detect anomalous influenza outbreaks.

Predictions validated on observed and simulated data.

Abstract

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.