Claude Flamand1, Mickael Fabregue2, Sandra Bringay3, Vanessa Ardillon4, Philippe Quénel1, Jean-Claude Desenclos5, Maguelonne Teisseire6. 1. Epidemiology Unit, Institut Pasteur in French Guiana, Cayenne, French Guiana. 2. LIRMM, CNRS, UMR 5506, Montpellier, France. 3. LIRMM, CNRS, UMR 5506, Montpellier, France MIAp Department, University Paul-Valery, Montpellier, France. 4. Regional Epidemiology Unit of the French Institute for Public Health Surveillance, Institut de Veille Sanitaire, Cayenne, French Guiana. 5. French Institute for Public Health Surveillance (Institut de Veille Sanitaire), Saint-Maurice, France. 6. Laboratory Department of Information System, Irstea-TETIS, Montpellier, France.
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.
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.
Entities:
Keywords:
Data Mining; Dengue fever; Epidemiologic surveillance; French Guiana; Infectious diseases; Meteorological factors
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