Eneza Data Science: Enhancing Data Science Capability and Tools for Health in East Africa

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Abstract The Research Training program "Eneza Data Science: Enhancing Data Science Capability and Tools for Health in East Africa" builds on Aga Khan University's UZIMA DS-I Africa project, whose focus is to improve health outcomes for at-risk mothers and children, mental health outcomes for at-risk adolescents and young adults in Kenya, and bioinformatics research and capacity building programs that have grown within the Human Health and Heredity in Africa (h3africa.org) program; Eastern Africa Network for Bioinformatics Training (EANBiT) and H3ABioNet, the pan-African bioinformatics network. EANBiT has as the main output masters level graduates in bioinformatics, which includes a five-week intensive residential training program that leads to thesis research in a range of bioscience disciplines, including infectious pathogens and vectors and microbial and human genetics. H3ABioNet has built an accessible and distributed introduction to bioinformatics training (IBT) and intermediate courses that often have ~1000 students simultaneously in Africa. ICIPE will lead Eneza Data Science, building on experience gained through H3Africa, in collaboration with Aga Khan University, and support from experts from the University of Michigan, the United States International University in Africa (USIU-Africa, based in Nairobi); Pwani University in Kenya, and the Open Pharma Foundation in India. This program brings together transdisciplinary expertise in machine learning / artificial intelligence, clinical practice, statistics, bioinformatics, and extensive knowledge of infectious diseases in the tropics. We have structured Eneza Data Science as a reproducible short-term training and internship pipeline to enhance data science capabilities measurably and provide opportunities to grow collaboration across communicable and noncommunicable diseases research and clinical communities in the region. Eneza is a Swahili word that means 'spread' or 'disseminate', and our vision is that we will incrementally contribute to individual skills and the number of trained health sector personnel who apply data science tools to improve decision making in healthcare service settings. The program specific aims are: Aim 1: Develop a collaborative platform for promoting and strengthening data science for health training and career development; Aim 2: Build awareness of the need for data science for health through engagement among stakeholders to grow the application of data science tools in clinical settings; Aim 3: Design a training model that combines short courses, hackathons, and mentorship programs to build capacity for clinicians, researchers, trainers, and trainees.