マスダ　ガク   Masuda Gaku   益田　岳    所属   医学部 医学科    職種   助教
言語種別	英語
発表タイトル	Drone Technology for Malaria Control: Locating and Managing Mosquito Breeding Sites
会議名	16th International Congress of Physiological Anthropology
学会区分	国際学会及び海外の学会
発表形式	口頭
講演区分	シンポジウム・ワークショップ パネル（公募）
発表者・共同発表者	@MASUDA Gaku, Kawada Hitoshi, Nakazawa Shusuke, Pemba Dylo Foster , Ngumbira Thomson
発表年月日	2023/09/07
国名	マレーシア
開催地 （都市, 国名）	sabah
開催期間	2023/09/07～2023/09/08
学会抄録	Borneo Journal of Medical Sciences 17,20 2023
概要	This study aims to effectively manage mosquito populations and reduce the spread of mosquito-borne diseases. The use of drone technology allows accurate identification of mosquito breeding sites and targeted application of insecticides to minimize dosage. Drones are used to detect, map and control mosquito breeding sites. Advanced techniques such as Normalized Difference Water Index (NDWI), high-resolution aerial imagery and AI computer vision algorithms are used to identify these sites. Promising early results include improved mapping of water surfaces and accurate detection of mosquito larvae with minimal training data. The study addresses the challenges faced in areas where malaria is prevalent, but resources are limited, resulting in uncontrolled mosquito breeding in small bodies of water. The research aims to develop effective interventions to identify and manage these breeding sites, complementing the World Health Organization's Integrated Vector Management (IVM) recommendations. The ultimate goal is to integrate insecticide applicators into drones for targeted control of both larval and adult mosquitoes. This integrated approach would allow a single operator to monitor and manage mosquito breeding sites over large areas on a weekly basis, improving the management of mosquito-borne disease outbreaks. Sustainability is a critical issue for such activities. While advanced technical expertise is required initially, day-to-day operations become more straightforward. Long-term viability depends on working with local communities, providing maintenance support, minimizing labor and energy consumption, and addressing technological uncertainties. In addition, using the citizen science approach engages young people in the beneficiary communities. Creating meaningful employment opportunities for young people in malaria-endemic regions is also essential. In conclusion, this study highlights the potential of drone technology to identify and manage mosquito habitats for malaria control. By using aerial imagery, AI computer vision, and targeted insecticide application, it offers a promising solution to combat vector-borne diseases and reduce the global burden of malaria.