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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
The American journal of tropical medicine and hygiene
2026
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| Online Access: | https://pubmed.ncbi.nlm.nih.gov/42119533/ |
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Table of Contents:
- Modeling and Predicting Counts and Environmental Correlates of Mosquito Distributions in Haiti Using Surveillance Data from the Ouest Department. Pshea-Smith, Ian A Okech, Bernard So, John Boncy, Jacques Sutherland, Ian Hamilton, Theron Dunford, James Blanton, Jason Existe, Alexandre Rudolph, Francisca Javiera Matulis, Graham A Ponciano, Jose Miguel Koehler, Jeffrey W Blackburn, Jason K von Fricken, Michael E Vector-borne diseases present significant public health challenges in Haiti, particularly dengue fever and lymphatic filariasis (LF), with Haiti hosting 90% of LF at-risk individuals in the Americas. Understanding mosquito vector distribution is crucial, especially as ongoing sociopolitical instability in Haiti limits vector surveillance and control measures. This study analyzed the spatial distribution and abundance patterns of key mosquito vectors using ecological modeling approaches. Mosquito surveillance was conducted at 19 sites from the communes of Carrefour, Genier, and Léogâne in the Ouest Department of Haiti from August 2018 to September 2019 using BG Sentinel, CDC Gravid, and CDC Light traps. Environmental covariates including temperature, precipitation, wind speed, elevation, and land-cover data were incorporated into boosted regression trees to predict mosquito presence and abundance. Of 22,504 mosquitoes captured, Culex quinquefasciatus dominated (53.44%), followed by Cx. nigripalpus (19.99%), Aedes aegypti (16.87%), Ae. albopictus (6.37%), Ae. mediovittatus (1.65%), and Psorophora columbiae (1.68%). Precipitation and temperature emerged as key correlates of mosquito presence and abundance. Predictive mapping demonstrated spatial heterogeneities in presence and abundance across each month for each species. These findings serve as a foundation for evidence-based vector control, and future research should incorporate additional environmental variables and expand sampling locations to strengthen predictive capabilities.