AMHERST, Mass. – A team of researchers headed by Chaitra Gopalappa, an assistant professor at the University of Massachusetts Amherst, has been awarded a four-year, $1.2-million grant from the National Science Foundation’s Smart and Connected Health program to develop a multi-disease model of interrelated diseases for studying the impact of public health investments on overall health.
This multidisciplinary project involves engineering, computer science, and social sciences research aimed at building new decision-analytic models for informing national and global public health decisions.
Focusing on a complex of high-burden chronic diseases with viral origins, such as human immunodeficiency virus (HIV), human papilloma virus (HPV) and cervical cancer, Hepatitis B (HBV), Hepatitis C (HCV) infections, and liver cancer, the project aims to create and validate a multi-disease model that captures the syndemic, or interrelated nature of disease transmission and progression, that will enable better prediction of the overall effectiveness of structural interventions. The goal is to provide the decision-support tools for analyses of the broader impact of structural interventions that can improve social determinants of health, reduce health disparities and improve overall health of the population. This is a shift from a disease-centered approach to modeling that focusses on disease-specific interventions.
The research team will use ongoing collaborations with key staff at the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) who are potential stakeholders in this work.
Gopalappa, an assistant professor of industrial engineering, has expertise in dynamic systems simulation and control optimization. She works closely with the CDC and WHO on non-communicable diseases such as cancers and communicable diseases such as HIV. Their mathematical modeling work has generated several significant contributions that have informed policy decision at the CDC and WHO.
At UMass Amherst, Gopalappa is the director of the Disease Prediction and Prevention Modeling laboratory that develops new methods and computational models for simulating the dynamics of disease incidence and spread for purposes of disease prediction, prevention and control analysis.
Other members of the research team are UMass Amherst professors Dean Robinson, political science, Hari Jagannatha Balasubramanian, industrial engineering, and Peter Haas, computer science, along with Harvard University professor Jagpreet Chhatwal. Robinson’s expertise is in social epidemiology with experience studying the relationship between race, social and economic policy and inequality in the U.S. while Balasubramanian’s expertise is in data analytics and stochastic optimization in healthcare delivery. Haas’s expertise is in modeling and computer simulation of complex stochastic systems. Chhatwal’s expertise is in decision sciences, health policy, and health economics.
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.
The research objectives are to develop a computational framework for integrated multi-disease prediction modeling; 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 develop a dynamic decision-analytic model for evaluation of structural interventions.