title: Mining local climate data to assess spatiotemporal dengue fever epidemic patterns in French Guiana.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Epidemiology Unit, Institut Pasteur in French Guiana, Cayenne, French Guiana.
authors.forename: Claude
authors.initials: C
authors.lastname: Flamand
authors.affiliation: LIRMM, CNRS, UMR 5506, Montpellier, France.
authors.forename: Mickael
authors.initials: M
authors.lastname: Fabregue
authors.affiliation: LIRMM, CNRS, UMR 5506, Montpellier, France MIAp Department, University Paul-Valery, Montpellier, France.
authors.forename: Sandra
authors.initials: S
authors.lastname: Bringay
authors.affiliation: Regional Epidemiology Unit of the French Institute for Public Health Surveillance, Institut de Veille Sanitaire, Cayenne, French Guiana.
authors.forename: Vanessa
authors.initials: V
authors.lastname: Ardillon
authors.affiliation: Epidemiology Unit, Institut Pasteur in French Guiana, Cayenne, French Guiana.
authors.forename: Philippe
authors.initials: P
authors.lastname: Quénel
authors.affiliation: French Institute for Public Health Surveillance (Institut de Veille Sanitaire), Saint-Maurice, France.
authors.forename: Jean-Claude
authors.initials: JC
authors.lastname: Desenclos
authors.affiliation: Laboratory Department of Information System, Irstea-TETIS, Montpellier, France.
authors.forename: Maguelonne
authors.initials: M
authors.lastname: Teisseire
mesh_terms: D002980:Climate; D057225:Data Mining; D003715:Dengue / Q000453:epidemiology*; D058872:Epidemics / Q000706:statistics & numerical data*; D005616:French Guiana; D006801:Humans; D015994:Incidence
keywords: Data Mining; Dengue fever; Epidemiologic surveillance; French Guiana; Infectious diseases; Meteorological factors