Researchers at KNUST are leveraging AI to analyse mosquito wingbeats for real-time disease surveillance, aiming to prevent outbreaks of malaria, yellow fever, and dengue in Ghana.
Their machine learning software identifies mosquito species, sex, and age with 96.79% accuracy, enabling targeted, early interventions. The project, led by Dr. Kingsley Badu, highlights how proactive AI-driven monitoring can replace late responses to epidemics.
The team also developed a biosensor device locally and is integrating climate data to boost prediction accuracy. Funded by Canada and the UK, the initiative is part of a global $7 million project, with planned expansions to Peru and the Philippines.
A mobile app is also in development to enable public participation in mosquito tracking using smartphones.
Source: https://www.knust.edu.gh/news/news-items/knust-researchers-are-using-ai-combat-mosquito-borne-diseases-ghana
Their machine learning software identifies mosquito species, sex, and age with 96.79% accuracy, enabling targeted, early interventions. The project, led by Dr. Kingsley Badu, highlights how proactive AI-driven monitoring can replace late responses to epidemics.
The team also developed a biosensor device locally and is integrating climate data to boost prediction accuracy. Funded by Canada and the UK, the initiative is part of a global $7 million project, with planned expansions to Peru and the Philippines.
A mobile app is also in development to enable public participation in mosquito tracking using smartphones.
Source: https://www.knust.edu.gh/news/news-items/knust-researchers-are-using-ai-combat-mosquito-borne-diseases-ghana