Technical workflow development for integrating drone surveys and entomological sampling to characterise aquatic larval habitats of anopheles funestus in agricultural landscapes in Côte d’Ivoire

Isabel Byrne, Kallista Chan, Edgar Manrique, Jo Lines, Rosine Z. Wolie, Fedra Trujillano, Gabriel Jimenez Garay, Miguel Nunez Del Prado Cortez, Hugo Alatrista-Salas, Eleanore Sternberg, Jackie Cook, Raphael N’Guessan, Alphonsine Koffi, Ludovic P Ahoua Alou, Nombre Apollinaire, Louisa A. Messenger, Mojca Kristan, Gabriel Carrasco-Escobar, Kimberly Fornace

Research output: Contribution to journalArticle in a journalpeer-review

Abstract

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 Côte 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 km2 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 Côte d’Ivoire and the analysis of risk factors for these sites.
Original languageEnglish
Article number3220244
Number of pages14
JournalJournal of Environmental and Public Health
Volume2021
DOIs
StatePublished - 1 Nov 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
Copyright © 2021 Isabel Byrne et al.

Publisher Copyright:
© 2021 Isabel Byrne et al.

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