This project develops machine-learning enhanced epidemic models that fuse surveillance data, mobility patterns, and climate signals to produce real-time forecasts for outbreaks across West Africa.

SCIM focuses on bridging the gap between mathematical theory and industrial application. We develop sophisticated computational models to solve complex engineering and logistics challenges facing West African industries.

DSCHANGE leverages big data analytics and machine learning to tackle pressing global issues. Our research focuses on predictive modeling for climate patterns and epidemiological trends across the African continent.