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

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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.
Idioma originalInglés
Número de artículo3220244
Número de páginas14
PublicaciónJournal of Environmental and Public Health
EstadoPublicada - 1 nov. 2021
Publicado de forma externa

Nota bibliográfica

Publisher Copyright:
Copyright © 2021 Isabel Byrne et al.

Funding Information:
The authors greatly appreciate the support offered by Institut Pierre Richet and are especially grateful to Pierre Kouame Kouakou, Mesmin Atsain, Serge Koffi, and Ayebi Hermann Florent Yapo for their assistance in entomological work. The authors also appreciate the field assistance provided by Julien Kouadio Kouacou, Marcelin N?Zue, Magloire Gnamien Kouakou, and Paul Kouame Kouame. The authors acknowledge Dr. Nouhoun Belko, Dr. Nana Kofi Amoah, and other staff from AfricaRice for their help with drones and other field equipment. The authors also acknowledge Dr. Joseph Timothy from the London School of Hygiene and Tropical Medicine for providing the methodology behind the offline maps used in the field. The authors acknowledge all the staff involved in the entomological work in the Eave Tube Phase 3 Randomized Control Trial in Ivory Coast (2016?2020). The authors appreciate Maria Bernardez Agrafojo, Anastasia Ioakeimidou, and the Network for the use of drones for malaria vector control (MACONDO) for their technical support. This work was undertaken as part of, and funded by, the CGIAR Research Program on Agriculture for Nutrition and Health (A4NH). This paper has not gone through the standard peer-review procedure of A4NH?s Lead Center, IFPRI. Additional funding was provided by MACONDO, ?International collaborative network for the integration, standardization and assessment of the use of drones in malaria vector control strategies? funded by the Global Challenges Research Fund (GCRF) Networking Grants (GCRFNGR4\1140). KMF is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant no. 221963/Z/20/Z).


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