Leveraging artificial intelligence and mathematical modelling for evidence-based prevention and response to mpox

About this project

Infectious disease outbreaks are increasing in both severity and frequency. A growing number of these diseases are spreading from animals to humans (zoonosis) due to factors such as increasing human encroachment into natural landscapes. Reverse zoonosis — the spread of infectious diseases from humans to animals — is also a growing concern. The resurgence of mpox witnessed in 2024 underscores the need for enhanced public health preparedness and response mechanisms. Adopting a One Health approach, which examines how animal, environmental and human health interact with one another, is essential to address the root drivers of infectious disease outbreaks. Strengthening locally developed and championed data systems that are grounded in community needs, analytical and modelling capacities, and the ability to visualize and communicate findings to key decision-makers, is a critical step toward this aim. This project aims to strengthen equitable and responsive public health systems that leverage Africa-led responsible AI and mathematical modelling to generate evidence-informed responses to the mpox outbreak. This will help optimize surveillance, early-warning systems and vaccination strategies and address public misinformation and support equitable health-care access. It will also enhance understanding of social, gender and cultural drivers of the spread of mpox. Working in the Democratic Republic of the Congo, Nigeria, Burundi, Cameroon, Senegal, Ghana and Ethiopia, the project will…

Published by

International Development Research Centre · Other public sector

Countries

Congo - Kinshasa, Burundi, Nigeria, Cameroon, Ethiopia, Ghana, Senegal

Sectors

Infectious disease control

Focus

Gender equality · Also contributesClimate adaptation · 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-301-2-110693-001.

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