期刊
JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH
卷 2021, 期 -, 页码 -出版社
HINDAWI LTD
DOI: 10.1155/2021/3220244
关键词
-
资金
- CGIAR Research Program on Agriculture for Nutrition and Health (A4NH)
- MACONDO, International collaborative network for the integration, standardization and assessment of the use of drones in malaria vector control strategies - Global Challenges Research Fund (GCRF) Networking Grants [GCRFNGR4\1140]
- Sir Henry Dale Fellowship - Wellcome Trust [221963/Z/20/Z]
- Sir Henry Dale Fellowship - Royal Society [221963/Z/20/Z]
- Wellcome Trust [221963/Z/20/Z] Funding Source: Wellcome Trust
This study successfully conducted an investigation into Anopheles funestus breeding sites near two villages in an agricultural setting in Cote d'Ivoire through integrating drone surveys and mosquito larval sampling. Using satellite remote sensing data and a classification scheme, potential breeding habitats for An. funestus were identified. These data can support targeted vector control efforts.
Land-use practices such as agriculture can impact mosquito vector breeding ecology, resulting in changes in disease transmission. The typical breeding habitats of Africa's second most important malaria vector Anopheles funestus are large, semipermanent water bodies, which make them potential candidates for targeted larval source management. This is a technical workflow for the integration of drone surveys and mosquito larval sampling, designed for a case study aiming to characterise An. funestus breeding sites near two villages in an agricultural setting in Cote d'Ivoire. Using satellite remote sensing data, we developed an environmentally and spatially representative sampling frame and conducted paired mosquito larvae and drone mapping surveys from June to August 2021. To categorise the drone imagery, we also developed a land cover classification scheme with classes relative to An. funestus breeding ecology. We sampled 189 potential breeding habitats, of which 119 (63%) were positive for the Anopheles genus and nine (4.8%) were positive for An. funestus. We mapped 30.42 km(2) of the region of interest including all water bodies which were sampled for larvae. These data can be used to inform targeted vector control efforts, although its generalisability over a large region is limited by the fine-scale nature of this study area. This paper develops protocols for integrating drone surveys and statistically rigorous entomological sampling, which can be adjusted to collect data on vector breeding habitats in other ecological contexts. Further research using data collected in this study can enable the development of deep-learning algorithms for identifying An. funestus breeding habitats across rural agricultural landscapes in Cote d'Ivoire and the analysis of risk factors for these sites.
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