Abstract

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.