Long-term trends in the spatial clustering of Aedes aegypti infestation within a tropical urban environment Open Access

LaCon, Genevieve Frances (2013)

Permanent URL: https://etd.library.emory.edu/concern/etds/rv042t612?locale=en
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Abstract

Background:Dengue, the most important mosquito borne viral disease in the world, is a major source of morbidity and mortality in tropical and temperate urban environments. While spatial clustering of the dengue vector Aedes aegypti has been previously studied, long term trends in clustering have not. This represents a promising area of study to help effectively target vector control in areas with limited resources.

Objective:This study longitudinally quantified the long term (over 3 years) spatial trends of Aedes aegypti clustering in the Maynas neighborhood of Iquitos, a city in the Peruvian Amazon, and determined the factors that influence a home's membership in a cluster of high mosquito abundance.

Methods:Spatial methods at the global (neighborhood) and local (household) level were applied to understand long term trends in adult and pupae clustering from 9 entomologic surveys spaced ~4 months. A GLM model was used to determine which household and environmental characteristics predicted proportion of time a household was a member of a cluster.

Results:While individual analysis of entomologic surveys did not indicate the occurrence of any apparent clustering, the proportion of time house was a cluster for adults as well as the proportion of time a house was a cluster for pupae ranged from 0-1, with some houses being members of clusters a high proportion of the time. Average kernel density across survey also showed a clear long term pattern of clustering. The best model predicting proportion of time house was a member of a cluster used household characteristics.

Discussion:Although Ae aegypti is highly heterogeneous and poorly predicted, overall there is a strong distribution pattern, Results from the model indicate household characteristics like water source and number of residents are good predictors of cluster membership. Future research should connect information on clusters to dengue infection, to determine if living in, or visiting a cluster raises risk of infection with dengue.

Table of Contents

Abstract.............................................. iv
Acknowledgements.............................................. vi
Table of Contents.............................................. vii
LITERATURE REVIEW.............................................. 1
ROLE.............................................. 8
RESEARCH MANUSCRIPT.............................................. 9
TITLE.............................................. 9
AUTHORS.............................................. 9
ABSTRACT.............................................. 9
INTRODUCTION.............................................. 11
MATERIALS AND METHODS.............................................. 13
RESULTS.............................................. 18
DISCUSSION.............................................. 22
REFERENCES.............................................. 27
TABLES.............................................. 30
FIGURES.............................................. 35

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