Mining local climate data to assess spatiotemporal dengue fever epidemic patterns in French Guiana

Sandra Bringay 1, 2 Claude Flamand 3 Mickaël Fabrègue 4, 5 Vanessa Ardillon 6, 7 Quenel Philippe 8 Jean-Claude Desenclos 9 Maguelonne Teisseire 4, 2
1 MIAP - Département de Mathématiques et Informatique Appliquées
2 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 INVS - Institut de Veille Sanitaire
4 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
5 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
6 Cellule Inter Régionale d'Epidémiologie des Antilles - Guyane
7 Cellule Interrégionale d'Epidémiologie Antilles-Guyane
8 Irset - Institut de recherche, santé, environnement et travail
9 Département des maladies infectieuses
Abstract : OBJECTIVE:To identify local meteorological drivers of dengue fever in French Guiana, we applied an original data mining method to the available epidemiological and climatic data. Through this work, we also assessed the contribution of the data mining method to the understanding of factors associated with the dissemination of infectious diseases and their spatiotemporal spread.METHODS:We applied contextual sequential pattern extraction techniques to epidemiological and meteorological data to identify the most significant climatic factors for dengue fever, and we investigated the relevance of the extracted patterns for the early warning of dengue outbreaks in French Guiana.RESULTS:The maximum temperature, minimum relative humidity, global brilliance, and cumulative rainfall were identified as determinants of dengue outbreaks, and the precise intervals of their values and variations were quantified according to the epidemiologic context. The strongest significant correlations were observed between dengue incidence and meteorological drivers after a 4-6-week lag.DISCUSSION:We demonstrated the use of contextual sequential patterns to better understand the determinants of the spatiotemporal spread of dengue fever in French Guiana. Future work should integrate additional variables and explore the notion of neighborhood for extracting sequential patterns.CONCLUSIONS:Dengue fever remains a major public health issue in French Guiana. The development of new methods to identify such specific characteristics becomes crucial in order to better understand and control spatiotemporal transmission.Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
Keywords : Data Mining Dengue fever Epidemiologic surveillance French Guiana Infectious diseases Meteorological factors
Type de document :
Article dans une revue
Journal of the American Medical Informatics Association, BMJ Publishing Group, 2014, 21 (e2), pp.232-240. 〈10.1136/amiajnl-2013-002348〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01379072
Contributeur : Sandra Bringay <>
Soumis le : mardi 11 octobre 2016 - 10:00:03
Dernière modification le : mercredi 18 juillet 2018 - 20:02:02

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LIRMM | IRSTEA | INVS | IRSET | AGROPOLIS | BIOSIT | ADVANSE | CIRAD | AGROPARISTECH | EHESP | UR1-UFR-SVE | USPC | DSETGS | MIPS | UNIV-MONTPELLIER | INC-CNRS | SITE-ALSACE | UNIV-RENNES

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Sandra Bringay, Claude Flamand, Mickaël Fabrègue, Vanessa Ardillon, Quenel Philippe, et al.. Mining local climate data to assess spatiotemporal dengue fever epidemic patterns in French Guiana. Journal of the American Medical Informatics Association, BMJ Publishing Group, 2014, 21 (e2), pp.232-240. 〈10.1136/amiajnl-2013-002348〉. 〈lirmm-01379072〉

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