Utilizing Health Information for Meaningful Impact in East Africa through Data Science (UZIMA-DS)

Young people in East Africa face a critical and often overlooked health burden shaped by the intersection of rapid demographic growth, limited health infrastructure, and insufficient data systems to guide timely intervention. Africa is the youngest continent in the world, with 60% of its population under the age of 25—yet health research and early warning systems designed specifically for this population remain underdeveloped. The window spanning early life through young adulthood is biologically and socially consequential: environmental exposures, psychosocial stressors, and gaps in maternal, newborn, and child health care during these years can produce cascading effects on long-term well-being. Mental health represents an additional and frequently neglected dimension of this challenge. Despite the enormous volumes of health data now being collected across East Africa and rapid advances in artificial intelligence and data science, these tools have not been systematically applied to improve health trajectories for young Africans at scale. 

The Utilizing Health Information for Meaningful Impact in East Africa through Data Science (UZIMA-DS) Research Hub addresses this gap by building a state-of-the-art, scalable, and sustainable platform for advanced data science in health research. Hosted at Aga Khan University's Institute for Human Development, the hub applies cutting-edge data assimilation techniques alongside novel artificial intelligence and machine learning methodologies to develop early warning systems capable of significantly improving health outcomes. The name draws on the Swahili word uzima—meaning health or well-being—reflecting the hub's commitment to locally grounded, community-relevant science. 

The hub fosters synergistic collaboration among statisticians, computer scientists, and informatics specialists; healthcare clinicians and practitioners; and community stakeholders across Kenya and Tanzania. This interdisciplinary structure is designed to enhance the quality, efficiency, and relevance of health data science research, ensuring that analytical methods are matched by deep contextual knowledge and community trust. Expected outcomes include operational early warning systems for maternal, newborn, child, and mental health conditions; validated AI and machine learning models trained on East African health data; strengthened regional capacity in data science and health research; and peer-reviewed scholarship advancing the field of data-driven global health equity. By centering African data, African institutions, and African communities, UZIMA-DS positions East Africa not as a subject of research but as a leader in generating the evidence needed to protect and improve the health of the continent's young people.