Harnessing Health Data Science capacity to strengthen evidence-based interventions, policy and response to the HIV/AIDS Epidemic in Uganda (H-DATA) - This application for the D43 Fogarty HIV Research Training Program for Low-and Middle-Income Country Institutions (PAR-22-151) leverages the established partnership between Makerere University and the University of California, San Francisco to equip Research Scientists and healthcare practitioners with advanced health data science capabilities to optimize data utilization for HIV/AIDS prevention, treatment, and clinical management. Recognizing that biomedical data science analytics generate critical knowledge that accelerates health prediction, clinical performance, and scientific discovery, this training program aims to: (1) Strengthen the scientific leadership and expertise needed for health data science research with increased focus on long term graduate (PhD and Msc) training in health data science. (2) Train MakCHS faculty to enhance their capabilities for Health data science research and mentorship of health data science trainees. (3) To strengthen trainee contributions to evidence-based decision making related to HIV prevention, care and treatment services through regular interactions with policy makers and HIV program implementers. Conducting this training in Uganda provides distinct advantages for U.S. public health interests by leveraging an unparalleled volume of long-term longitudinal clinical cohorts, diverse viral sub-types, and innovative health delivery models. This high-density epidemiological environment allows trainees to engineer highly precise predictive models and refine machine learning algorithms for retention-in-care that directly translate to parallel clinical challenges in hard-to-reach or geographically isolated American populations. Specifically, Uganda's pioneering framework in decentralized, community-led antiretroviral delivery provides a data-rich environment to optimize care-delivery strategies for federally designated U.S. Medically Underserved Areas (MUAs) and Health Professional Shortage Areas (HPSAs), including the rural South and major urban centers experiencing documented care-retention differentials. Furthermore, tracking complex comorbidities and multi-drug resistance patterns within Uganda’s extensive patient base yields critical predictive analytics that enhance domestic U.S. epidemic forecasting and biosecurity. This cross-continental training paradigm ensures that analytical insights generated from Ugandan health infrastructure translate directly into actionable, cost-effective interventions for the U.S. healthcare system, optimizing domestic clinical outcomes. The training curriculum integrates PhD, Master’s, and faculty trajectories with specialized short courses covering Fundamentals of Health Data Science, Digital Medicine, Introduction to Artificial Intelligence, Applied Machine Learning, Statistical Methods & Data Analysis, Bio-Python, Java Programming, R & Bio-Conductor, Discrete Mathematics, Health Data Science Algorithms, Healthcare Data Mining & Visualization, and Scientific Writing & Grantsmanship.
