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SURVEILLANCE OF DENGUE VECTORS USING SPATIO-TEMPORAL BAYESIAN MODELING
Métodos Bayesian
Modelos espaço-temporais
Dengue
Dengue
Bayesian methods
Spatio-temporal models
Zero-inflated models
INLA
Autor
Afiliación
Fundação Oswaldo Cruz. Escola Nacional de Saúde Pública Sérgio Arouca. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente Fernandes Figueira. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Presidência. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Transmissores de Hematozoários. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Núcleo Operacional Sentinela de Mosquitos Vetores. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Núcleo Operacional Sentinela de Mosquitos Vetores. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Núcleo Operacional Sentinela de Mosquitos Vetores. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Presidência. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Presidência. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Transmissores de Hematozoários. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Núcleo Operacional Sentinela de Mosquitos Vetores. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Núcleo Operacional Sentinela de Mosquitos Vetores. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Núcleo Operacional Sentinela de Mosquitos Vetores. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Presidência. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.
Resumen en ingles
Background
At present, dengue control focuses on reducing the density of the primary vector for the disease, Aedes aegypti, which is the only vulnerable link in the chain of transmission. The use of new approaches for dengue entomological surveillance is extremely important, since present methods are inefficient. With this in mind, the present study seeks to analyze the spatio-temporal dynamics of A. aegypti infestation with oviposition traps, using efficient computational methods. These methods will allow for the implementation of the proposed model and methodology into surveillance and monitoring systems.
Methods
The study area includes a region in the municipality of Rio de Janeiro, characterized by high population density, precarious domicile construction, and a general lack of infrastructure around it. Two hundred and forty traps were distributed in eight different sentinel areas, in order to continually monitor immature Aedes aegypti and Aedes albopictus mosquitoes. Collections were done weekly between November 2010 and August 2012. The relationship between egg number and climate and environmental variables was considered and evaluated through Bayesian zero-inflated spatio-temporal models. Parametric inference was performed using the Integrated Nested Laplace Approximation (INLA) method.Results
Infestation indexes indicated that ovipositing occurred during the entirety of the study period. The distance between each trap and the nearest boundary of the study area, minimum temperature and accumulated rainfall were all significantly related to the number of eggs present in the traps. Adjusting for the interaction between temperature and rainfall led to a more informative surveillance model, as such thresholds offer empirical information about the favorable climatic conditions for vector reproduction. Data were characterized by moderate time (0.29 – 0.43) and spatial (21.23 – 34.19 m) dependencies. The models also identified spatial patterns consistent with human population density in all sentinel areas. The results suggest the need for weekly surveillance in the study area, using traps allocated between 18 and 24 m, in order to understand the dengue vector dynamics.
Conclusions
Aedes aegypti, due to it short generation time and strong response to climate triggers, tend to show an eruptive dynamics that is difficult to predict and understand through just temporal or spatial models. The proposed methodology allowed for the rapid and efficient implementation of spatio-temporal models that considered zero-inflation and the interaction between climate variables and patterns in oviposition, in such a way that the final model parameters contribute to the identification of priority areas for entomological surveillance.
Palabras clave en portugues
Vigilância entomológicaMétodos Bayesian
Modelos espaço-temporais
Dengue
Palabras clave en ingles
Entomological surveillanceDengue
Bayesian methods
Spatio-temporal models
Zero-inflated models
INLA
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