This Smart and Connected Health (SCH) award will contribute to the advancement of the national health and welfare by studying the effectiveness of public health investments in impacting a collection of related chronic diseases. It is generally accepted that social conditions, including access to essential public services, early childhood development and education, economic and food security and environmental conditions, are important determinants of individual health. Referred to collectively as Social Determinants of Health (SDOH), research in social epidemiology has shown that structural interventions aimed at improving SDOH can prevent diseases, thereby improving well-being. This project addresses the important interactions between a variety of risk factors for chronic diseases, and develops mathematical models and decision-support methods that enable economic analysis of cost-effective combinations of structural interventions as part of an overall public health strategy. Focusing on a complex of high-burden chronic diseases with viral origins (e.g., HIV, HPV, HBV and HCV infections), this project aims to develop and validate a multi-disease model that captures the syndemic nature of transmission and disease progression and enables better prediction of the overall effectiveness of structural interventions. The research team will exploit ongoing collaborations with key staff at the Centers for Disease Control and Prevention and the World Health Organization who are potential stakeholders in this work. The project engages both engineering and computer science students in multi-disciplinary research aimed at developing innovative decision-analytic models for informing national and global public health decisions.
The research objectives are to: 1) develop a computational framework for integrated multi-disease prediction modeling; 2) develop new methodologies for parameterizing the natural progression of interacting communicable and non-communicable diseases in a population influenced by socioeconomic and demographic factors; and 3) develop a dynamic decision-analytic model for evaluation of structural interventions. The computational framework combines agent-based simulation and compartmental analysis to incorporate viral transmission and progression. This project employs techniques from system dynamics, machine learning, graph theory, and optimization to extend current state-of-the-art in simulation and parameterization methods. Data on disease incidence, morbidity and mortality will be obtained from national surveillance and survey databases for model validation.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
