Data science without borders: bridging the divide in data science capacity across African health institutions

Abstract

Our integrated assessment suggests that effective capacity building requires moving beyond standardized approaches to embrace a phased model that leverages institutional needs and complementarities. We recommend: (1) establishing robust data governance frameworks as a foundation; (2) implementing a phased and customized approach where institutions receive training according to their immediate demands and strengths; (3) addressing critical infrastructure gaps to support data. We are involved in science projects in Africa that support federated analyses to maintain data sovereignty. This approach offers potential for a varying African approach to health data science, which could extend to AI adoption and broader continental collaboration.

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