Publications

Diarrheal diseases remain a leading cause of morbidity and mortality in African urban centers, particularly affecting children in informal settlements where inadequate sanitation, unsafe water, and overcrowding prevail. Traditional surveillance systems often rely on retrospective health data, limiting timely outbreak detection and response. This review explores the potential of artificial intelligence (AI) in predicting diarrheal outbreaks in African cities, highlighting machine learning, deep learning, and data fusion techniques that integrate diverse data streams, including environmental, climatic, demographic, and health facility records. Case studies from Nigeria, Kenya, and South Africa illustrate how AI-driven models can improve early warning systems, optimize resource allocation, and reduce disease burden. The review also examines challenges such as data limitations, infrastructural constraints, ethical concerns, and capacity gaps, while identifying opportunities for hybrid modeling, community engagement, policy integration, and regional collaboration. Strategic application of AI promises proactive, data-driven public health interventions, contributing to resilient urban health systems and improved outcomes for vulnerable populations.