Project Summary The overarching goal of the Data Science for Social Determinants (DSSD) at New York University, Moi University and Brown University is to develop future leaders in data science who are equipped to develop and analyze data to better leverage deep and rich survey as well as internet and other digitized data sources that can help us capture information on the social determinants of health. DSSD's design will rapidly expand the local base of expertise via curriculum development, resulting in 2 PhD (4-year training) and 6 total postdoctoral (2-year) and faculty trainees (12-14 month) will train at NYU, while 8 Masters and 2 PhD trainees will commence or complete training (2-year and 4-year training, respectively) through newly developed data science tracks at Moi University. Connecting with local data science industries and organizations (IBM, Deep Learning Indaba, DataKind, AI.Kenya and Aga Khan University Nairobi and Karachi) will bring in and create intellectual meeting spaces for a wide pool and variety of talented trainees from both data science and health backgrounds, to propel and sustainably advance data science capacity in Kenyan institutions as well as the DSI-consortium.

Narrative The overarching goal of the Data Science for Social Determinants (DSSD) pre- and post-doctoral training program is to develop future leaders in data science who are equipped to develop and analyze data to better leverage deep and rich survey as well as internet and other digitized data sources that can help us capture information on the social determinants of health. Such data can provide unique illumination into factors on where we live, work, and play which can be used to better understand and address health disparities across noncommunicable disease, infectious disease and injuries in the Kenyan context. However, the data are often unstructured and can have complex relationships with health outcomes, making the use of modern data science approaches critical. DSSD leverages strong investment across biomedical, public health, data science and artificial intelligence investment at both partners (NYU School of Medicine is 11th in the nation for NIH funding, NYU is home of the AI-now institute and Moi University has a strong NIH funding record) as well as a broader network of academic, industrial and non-governmental organizations (IBM, Deep Learning Indaba, DataKind, AI.Kenya and Aga Khan University Nairobi and Karachi), opening a wide pool of potential, strong trainees from both data science and health backgrounds. To develop capacity, priority will be given to Moi affiliates, Kenyan citizens and then others from anywhere in Africa while also prioritizing diversity. A complementary team of 23 mentors at NYU and Moi committed to training and career development will offer resources across social determinants, biomedical sciences, statistics, epidemiology, data management, analytics and ethics. First, the program will provide long-term development of a cohort of PhD, postdoctoral and Faculty fellow trainees who will benefit from interdisciplinary training that carefully layers a core set of competencies in data science across data management and wrangling, prediction and analytics and data communication and ethics, with didactic and experiential training in social determinants as well as best practices in responsible conduct of research, reproducibility and community-engaged, ethical data science at NYU. Second, the program will leverage international standard biostatistics and informatics faculty and programming at Moi with trainees equipped in advanced data science coursework at NYU, to expand the base of local expertise and capacity in data science by developing MSc and PhD data science tracks at the Institute for Biomedical Informatics and Biostatistics programs at Moi University within 5 years. Finally, continuing education opportunities and networking will occur through workshops bringing together experts in health and data science in Kenya and surrounding areas. Programs will be evaluated annually by our Internal Advisory Board and Training Advisory Committee. As social determinants can be cross-cutting rather than disease-specific, classroom and research opportunities across social determinants and their applicability to wide range of health application domains will be available at both project sites and curricula will be relevant to research and training efforts across the entire DSI-Africa consortium.