Data Science Without Borders: Bridging the Divide in Data Science Capacity Across African Health Institutions

Agnes Kiragga^1*Samuel Iddi¹Abel Wilson Walekhwa²Miranda Barasa¹Steve Cygu¹Rachel Odhiambo¹Moctar Gningue³Aminata Mboup³Anicet Onana⁴Bethlehem Adnew⁵Akililu Alemu⁵Simon Hodson⁶Jay Greenfield⁷Jim Todd⁸Tathagata Bhattacharjee⁸Malvika Sharan⁹Raphael Sonabe⁹Damazo Kagengye¹Bertrand Hugo Mbatchou¹⁰Alemseged Abdissa⁵Moussa Sarr³Joyce Nakatumba-Nabende¹¹Moses Bamutura¹²Bekure Tamirat¹³Nebiyu Derebe¹³Elvis Temfack¹³

• ¹African Population and Health Research Center (APHRC), Nairobi, Kenya
• ²University of Cambridge Department of Veterinary Medicine, Cambridge, United Kingdom
• ³Institut de Recherche en Sante de Surveillance Epidemiologique et de Formation, Diamniadio, Senegal
• ⁴Douala General Hospital, Cameroon, Doula, Cameroon
• ⁵Armauer Hansen Research Institute, Addis Ababa, Ethiopia
• ⁶Committee on Data (CODATA), France, France
• ⁷Committee on Data, France, France
• ⁸London School of Hygiene and Tropical Medicine, London, United Kingdom
• ⁹OSPO NOW, London, United Kingdom
• ¹⁰Universite de Douala, Douala, Cameroon
• ¹¹Makerere University, Department of Computer Science, Center for Artificial Intelligence Lab, Kampala, Uganda, Kampala, Uganda
• ¹²Africa centers for Disease Prevention and Control, Addis Ababa, Ethiopia
• ¹³Africa Centres for Disease Control and Prevention, Addis Ababa, Ethiopia

Background: Effective public health data science in Africa requires a clear understanding of institutional capacities across multiple dimensions. This study conducted a multidimensional baseline assessment of three African health institutions to evaluate health data availability, training needs, data governance, and infrastructure, with the goal of informing data science capacity-building for health challenges. Methods: This assessment forms part of the Data Science Without Borders Project—a three-year, multi-country initiative implemented at the Institute for Health Research, Epidemiological Surveillance and Training (IRESSEF, Senegal), the Armauer Hansen Research Institute (AHRI, Ethiopia), and Douala General Hospital (DGH, Cameroon). A structured needs assessment survey was designed to evaluate: (1) health data availability across 16 dataset categories; (2) training needs across seven domains; (3) data governance considerations; and (4) infrastructure capabilities, including computing resources and connectivity. An integrated analysis identified patterns, gaps, and opportunities across institutions. Results: Institutions exhibited distinct yet complementary strengths. IRESSEF possessed rich datasets—especially in genomics, maternal health, and geographic health disparities—moderate infrastructure (8 GB RAM, 67% service capacity), and high training needs in data analytics (4.7/5.0) and data governance (4.0/5.0). AHRI showed strong computational capacity (512 GB RAM, 64 CPU cores), specialized surveillance data (9.9%), and moderate training needs (3.0/5.0). DGH demonstrated expertise in infectious disease research (3.3%), moderate infrastructure (32 GB RAM), and substantial potential to expand research using electronic health records. Shared priorities included strengthening analytical skills (average 4.3/5.0), advancing artificial intelligence and machine learning use (IRESSEF: 5.0, AHRI: 4.0, DGH: 5.0), and developing data governance frameworks to enhance cross-institutional data sharing. Conclusions: The findings highlight the need for tailored, phased capacity building that leverages institutional strengths and complementarities rather than a one-size-fits-all approach. We recommend: (1) establishing robust data governance frameworks; (2) implementing customized training aligned with institutional priorities; and (3) addressing infrastructure gaps to enable sustainable data-driven research. This model offers a scalable foundation for advancing African-led health data science, AI adoption, and continental collaboration.