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

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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Publication Details

Journal
Frontiers in Public Health
Published
2025-12-05
DOI
https://doi.org/10.3389/fpubh.2025.1695907
Primary Topic
Global Health and Surgery
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Samuel Iddi, Aminata Mboup, Nebiyu Dereje, Raphael Sonabend et al.
Frontiers in Public Health
Global Health and Surgery
article

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

Samuel Iddi, Aminata Mboup, Nebiyu Dereje, Raphael Sonabend, Elvis Temfack, Jay Greenfield, Damazo T. Kadengye, Malvika Sharan, Moussa Sarr, Tathagata Bhattacharjee, Agnes Kiragga, Alemseged Abdissa, Joyce Nakatumba‐Nabende, Jim Todd, Rachael Odhiambo, Akililu Alemu Ashuro, Moses Bamutura, Simon Hudson, Steve Cygu, Bertrand Hugo Mbatchou, Abel W. Walekhwa, Bethlehem Adnew, Bekure Tamurat, Miranda Barasa, Anicet Onana, Moctar Gningue
article en

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.

Frontiers in Public HealthVol. 13
University of Ghana (GH), University of Douala (CM), University of Cambridge (GB), London School of Hygiene & Tropical Medicine (GB), Africa Centres for Disease Control and Prevention (ET), DATA4 (France) (FR), African Population and Health Research Center (KE), Armauer Hansen Research Institute (ET), Makerere University (UG)
African Population and Health Research Center
Openalex Percentile: Top 51%
Global Health and Surgery
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