Responsible AI, empowering people synergy grant: Mosquito surveillance system for dengue detection in Africa and Asia

About this project

Dengue and other mosquito-borne diseases pose a heavy burden of disease across the Global South. Rapid urbanization, climate change and population growth have expanded mosquito habitats in many parts of Africa and Southeast Asia, fueling more frequent and severe outbreaks. This project will develop a responsible mosquito detection model enabled by artificial intelligence (AI) to strengthen surveillance of dengue in Ghana and the Philippines. It plans to develop a responsible AI-driven mosquito detection model driven to classify mosquito wingbeat audio to help detect the presence of Aedes mosquitoes. By using a climate-sensitive dengue model, it will be possible to extend forecasting models, using local climate data (temperature, rainfall, humidity, vegetation) to predict dengue incidence several weeks ahead of an outbreak. The results will engage local scientists and health workers in the dynamics of dengue, AI and data use, and share results through open-source tools. The research will focus on gender-responsive analysis and community-based approaches that respond to vulnerable groups such as slum dwellers and Indigenous communities. AI-enabled solutions will use responsible AI principles, building on existing solutions in Ghana and the Philippines. The project will engage community and policymaking stakeholders in solutions and enable earlier detection, potentially saving many lives. The project is part of the Artificial Intelligence for Development program, a partnership…

Published by

International Development Research Centre · Other public sector

Countries

Philippines, Ghana

Sectors

Infectious disease control

Focus

Environment · Also contributesGood governance · Also contributesBiodiversity · Also contributes

Published by International Development Research Centre to the International Aid Transparency Initiative (IATI), last updated 1 Sept 2026 and imported automatically. IATI identifier: XM-DAC-301-2-110731-001.

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