An extreme value theory approach for the early detection of time clusters. A simulation-based assessment and an illustration to the surveillance of Salmonella

Abstract : We propose a new method that could be part of a warning system for the early detection of time clusters applied to public health surveillance data. This method is based on the extreme value theory (EVT). To any new count of a particular infection reported to a surveillance system, we associate a return period that corresponds to the time that we expect to be able to see again such a level. If such a level is reached, an alarm is generated. Although standard EVT is only defined in the context of continuous observations, our approach allows to handle the case of discrete observations occurring in the public health surveillance framework. Moreover, it applies without any assumption on the underlying unknown distribution function. The performance of our method is assessed on an extensive simulation study and is illustrated on real data from Salmonella surveillance in France.
Type de document :
Article dans une revue
Statistics in Medicine, Wiley-Blackwell, 2014, 33 (28), <10.1002/sim.6275>
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01311727
Contributeur : Grégory Thureau <>
Soumis le : mercredi 11 mai 2016 - 14:15:43
Dernière modification le : mardi 11 octobre 2016 - 12:01:20
Document(s) archivé(s) le : mercredi 16 novembre 2016 - 00:58:28

Fichier

Guillou-R3.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Armelle Guillou, Marie Kratz, Yann Le Strat. An extreme value theory approach for the early detection of time clusters. A simulation-based assessment and an illustration to the surveillance of Salmonella. Statistics in Medicine, Wiley-Blackwell, 2014, 33 (28), <10.1002/sim.6275>. <hal-01311727>

Partager

Métriques

Consultations de
la notice

54

Téléchargements du document

45