AI-Powered Integrated Early Warning Systems for Predictive and Proactive Disease Outbreak Management in Africa

Key Aspects of the Project:

• Challenge: Infectious diseases are exacerbated by climate change and displacement, and traditional surveillance is slow and reactive.

• Solution (EWS): The EWS uses AI and advanced Spatio-Temporal Modeling to predict disease risk at the district/county level with a critical 4–12 week lead time.

• Data Integration: The system fuses three complementary data streams:

  • High-Resolution Satellite Data (for climate and environment).

  • Anonymized Mobile Network Data (for human mobility and displacement).

  • Standardized Public Health Surveillance Data (for historical ground truth).

• Implementation: Initial deployment is targeted across nine countries, including the Sahel region: Burkina Faso, Niger, Mali, Democratic Republic of Congo (DRC), Ghana, Ethiopia, Nigeria, Somalia, and Mauritania.

• Partnerships & Adoption: Systemic adoption is ensured through collaboration with the Africa CDC and the respective Ministries of Health (MoH) in target countries.

• Financials & Sustainability: The total project cost for 24 months is $2,500,000 USD. The system will be shared under an Open-Source License (Apache 2.0 or MIT) to enable global adoption and sustainment.