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

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  • Maud Thomas
  • Holger Rootzén

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.

Suggested Citation

  • Maud Thomas & Holger Rootzén, 2022. "Real‐time prediction of severe influenza epidemics using extreme value statistics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 376-394, March.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:2:p:376-394
    DOI: 10.1111/rssc.12537
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    References listed on IDEAS

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