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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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    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Rootzen, Holger & Segers, Johan & Wadsworth, Jennifer, 2018. "Multivariate peaks over thresholds models," LIDAM Reprints ISBA 2018005, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Brodin, Erik & Rootzén, Holger, 2009. "Univariate and bivariate GPD methods for predicting extreme wind storm losses," Insurance: Mathematics and Economics, Elsevier, vol. 44(3), pages 345-356, June.
    4. Jiangpeng Chen & Xun Lei & Li Zhang & Bin Peng, 2015. "Using Extreme Value Theory Approaches to Forecast the Probability of Outbreak of Highly Pathogenic Influenza in Zhejiang, China," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-10, February.
    5. Andrew Rambaut & Oliver G. Pybus & Martha I. Nelson & Cecile Viboud & Jeffery K. Taubenberger & Edward C. Holmes, 2008. "The genomic and epidemiological dynamics of human influenza A virus," Nature, Nature, vol. 453(7195), pages 615-619, May.
    6. René Michel, 2009. "Parametric Estimation Procedures in Multivariate Generalized Pareto Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 60-75, March.
    7. Kyriaki Kalimeri & Matteo Delfino & Ciro Cattuto & Daniela Perrotta & Vittoria Colizza & Caroline Guerrisi & Clement Turbelin & Jim Duggan & John Edmunds & Chinelo Obi & Richard Pebody & Ana O Franco , 2019. "Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-21, April.
    8. Maud Thomas & Magali Lemaitre & Mark L Wilson & Cécile Viboud & Youri Yordanov & Hans Wackernagel & Fabrice Carrat, 2016. "Applications of Extreme Value Theory in Public Health," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-7, July.
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