The NSF and NIH Invite MIE’s Chaitra Gopalappa to Present Her Research During a Symposium on July 16

Content

In a symposium on July 16th, Associate Professor Chaitra Gopalappa of the UMass Amherst Mechanical and Industrial Engineering (MIE) Department will present her research on “Simulation and Decision-Analysis for Integrated Modeling of Diseases” during a webcast sponsored by the National Science Foundation (NSF) and the National Institutes of Health (NIH). According to the NSF and NIH, this symposium – called “Smart Health Frontiers: Precision Medicine Through Engineering” – celebrates the work of researchers funded by the NSF Smart Health Program and showcases how advances in engineering analytics can help create tailored solutions to critical issues in biomedical research. 

In her research, Gopalappa asks the critical question: How can industrial engineers help predict, prevent, and control diseases? Her answer is that tools from this field can be applied to develop systems-engineering models of disease epidemiology and thus create, analyze, and improve national and international healthcare strategies. 

Gopalappa, the director of the UMass Amherst Disease Prediction and Prevention Modeling Laboratory, takes a synergistic approach to disease prevention. She considers the underlying social, economic, and environmental determinants common to most diseases and uses her lab to develop open-source systems models to develop intervention strategies for the optimal allocation of limited resources.

As Gopalappa says about the research she is presenting on July 16, “This NSF-funded work was conducted in collaboration with a multidisciplinary team from engineering, computer-science, and social-sciences from UMass Amherst and the United States Centers for Disease Control and Prevention.”

In her July 16th presentation, Gopalappa will explain how “this work generates novel simulation and decision-analysis techniques by integrating methods from networks, deep learning, copula, probabilistic graphical models, and reinforcement learning.”

As the background for her research, Gopalappa says that “Observational studies across numerous diseases, both infectious and non-infectious, consistently show positive correlations between disease risk and socio-economic burden, thus referred to as ‘social determinants of health.’ This compelling evidence has motivated the inclusion of structural interventions as a key part of disease-prevention strategies….”

However, says Gopalappa, the complexity of the problem creates challenges in developing decision-analytic tools for evaluating and identifying optimal intervention strategies that help inform public health programs, policies, and operations.

On July 16th, Gopalappa will discuss how her lab works on three components of this critical problem while using sexually transmitted infections as a case study. 

The first component is a simulation technique for jointly modeling diseases that share common behavioral mechanisms and social determinants of health to evaluate the impact of structural interventions on overall disease prevention. “The influence of these common pathways to disease risk,” as Gopalappa observes, “can be overlooked in the more common single-disease silo approach.”

The second component is the “mechanistic modeling of social determinants of health in decision-analytic simulations.” In addition to simulating disease risk as a function of health behaviors, this component extends the process to simulate health behaviors as a function of social determinants of health in order to identify and evaluate structural interventions.

The third and last component is developing a set of dynamic, sequential, decision-analysis algorithms for solving optimal combinations of intervention strategies in the context of Gopalappa’s pioneering method for collectively modeling diseases that share common behavioral mechanisms and social determinants of health.

One example of similar research by Gopalappa involved working with collaborators at the United States Centers for Disease Control and Prevention on a model for the analyses of national strategies for prevention of HIV in the United States. Another example was collaborating with Avenir Health and the World Health Organization on a model for the estimation of health impacts of breast- and cervical-cancer screening in low- and middle-income countries and corresponding resource needs. 

Gopalappa concludes that “Solving societal problems is a multidisciplinary effort. Industrial-engineering tools can play a critical role in these humanitarian efforts.” (July 2024)