Field evaluation of drone and AI assisted larval source management in Ghana
Godfred A. Bokpin, Francis A. Adzei, Samuel Dadzie, Masaki Umeda, Juhoe Kim, Himmat Singh, Himmat Singh, Himmat Singh, Himmat Singh
TL;DR
This study shows that using drones and AI for malaria control in Ghana can save resources without reducing effectiveness.
Contribution
A novel field-adapted LSM approach integrating drone mapping and AI for efficient vector control.
Findings
Drone-assisted mapping identified over three times more breeding sites than conventional methods.
AI-based targeting reduced larvicide use by over 60% and worker requirements by 50%.
Malaria case trends were comparable between intervention and control sub-districts.
Abstract
Malaria remains a major public health burden in sub-Saharan Africa. In Ghana, in particular, larval source management (LSM) is increasingly recognized as a complementary vector control strategy. This study evaluates a field-adapted LSM approach that integrates drone-based mapping and artificial intelligence (AI)–driven site prioritization to enhance operational efficiency and reduce resource use. The intervention replaces conventional manual scouting with aerial mapping conducted one day prior to larvicide application. An AI model analyzes geospatial and morphological features of water bodies to identify high-risk larval habitats. Site coordinates are transmitted to field teams via mobile devices for targeted treatment. A comparative field trial was conducted in eight administrative sub-districts within Ghana’s Eastern Region. Four sub-districts implemented the drone- and AI-assisted…
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Taxonomy
TopicsMalaria Research and Control · Mosquito-borne diseases and control · UAV Applications and Optimization