Grillet2010194

Référence

Grillet, M.-E., Barrera, R., Martinez, J.-E., Berti, J., Fortin, M.-J. (2010) Disentangling the effect of local and global spatial variation on a mosquito-borne infection in a neotropical heterogeneous environment. American Journal of Tropical Medicine and Hygiene, 82(2):194-201. (Scopus )

Résumé

Mosquito-borne pathogen transmission exhibits spatial-temporal variability caused by ecological interactions acting at different scales. We used local spatial statistics and geographically weighted regression (GWR) to determine the spatial pattern of malaria incidence and persistence in northeastern Venezuela. Seven to 11 hot spots of malaria transmission were detected by using local spatial statistics, although disease persistence was explained only for four of those hot spots. The GWR models greatly improved predictions of malaria risk compared with ordinary least squares (OLS) regression models. Malaria incidence was largely explained by the proximity to and number of Anopheles aquasalis habitats nearby (1-3 km), and low-elevation terrains. Disease persistence was associated with greater human population density, lower elevations, and proximity to aquatic habitats. However, there was significant local spatial variation in the relationship between malaria and environmental variables. Spatial modeling improves the understanding of the causal factors operating at several scales in the transmission of malaria. Copyright © 2010 by The American Society of Tropical Medicine and Hygiene.

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@ARTICLE { Grillet2010194,
    AUTHOR = { Grillet, M.-E. and Barrera, R. and Martinez, J.-E. and Berti, J. and Fortin, M.-J. },
    TITLE = { Disentangling the effect of local and global spatial variation on a mosquito-borne infection in a neotropical heterogeneous environment },
    JOURNAL = { American Journal of Tropical Medicine and Hygiene },
    YEAR = { 2010 },
    VOLUME = { 82 },
    NUMBER = { 2 },
    PAGES = { 194-201 },
    NOTE = { cited By 32 },
    ABSTRACT = { Mosquito-borne pathogen transmission exhibits spatial-temporal variability caused by ecological interactions acting at different scales. We used local spatial statistics and geographically weighted regression (GWR) to determine the spatial pattern of malaria incidence and persistence in northeastern Venezuela. Seven to 11 hot spots of malaria transmission were detected by using local spatial statistics, although disease persistence was explained only for four of those hot spots. The GWR models greatly improved predictions of malaria risk compared with ordinary least squares (OLS) regression models. Malaria incidence was largely explained by the proximity to and number of Anopheles aquasalis habitats nearby (1-3 km), and low-elevation terrains. Disease persistence was associated with greater human population density, lower elevations, and proximity to aquatic habitats. However, there was significant local spatial variation in the relationship between malaria and environmental variables. Spatial modeling improves the understanding of the causal factors operating at several scales in the transmission of malaria. Copyright © 2010 by The American Society of Tropical Medicine and Hygiene. },
    AFFILIATION = { Laboratorio de Biologia de Vectores, Instituto de Zooloǵa Tropical, Universidad Central de Venezuela, Avenida Los Ilustres, Los Chaguaramos, Caracas 1041-A, Venezuela; Division of Vector Borne Infectious Diseases, Centers for Disease Control and Prevention (CDC), National Center for Infectious Diseases, San Juan, Puerto Rico; Instituto de Altos Estudios de Salud Pública Dr. Arnoldo Gabaldon (IAESP-MPPS), Venezuela; Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.4269/ajtmh.2010.09-0040 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-77449129113&doi=10.4269%2fajtmh.2010.09-0040&partnerID=40&md5=692c39b096eb740d91c298e7848505a8 },
}

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