Chaitra Gopalappa, an assistant professor in the Mechanical and Industrial Engineering (MIE) Department, was invited to make a presentation as a Session Speaker at the National Academy of Sciences’ 15th Japanese American Kavli Frontiers of Science symposium on December 2 to 4 in Irvine, California. “This symposium series is the Academy’s premiere activity for distinguished young scientists,” as National Academy of Sciences (NAS) President Marcia McNutt explained. The title of Gopalappa’s presentation was “Analyses of national and global strategic plans for disease prevention and control.”
As President McNutt said about the Frontiers of Science symposium series “Unlike meetings that cover a single, narrow slice of science, these symposia are designed to provide an overview of advances and opportunities in a wide-ranging set of disciplines and to provide an opportunity for the future leaders of science to build a network with their colleagues. Attendees are selected by a committee of NAS members from among young researchers who have already made recognized contributions to science, including recipients of major national fellowships and awards and who have been identified as future leaders in science. Since its inception in 1989, more than 175 symposia “alumni” have been elected to the NAS, and ten have received Nobel Prizes.”
As one of these very selective symposium presenters, Gopalappa does research that integrates methods from simulation modeling, stochastic processes, and optimization modeling for economic analysis of public health strategic plans. As she notes “national and global strategic plans for disease prevention and control are extremely critical as they drive allocation of resources to intervention programs from national- to local- levels. Decision-makers are often faced with a challenge of developing evidence-based strategies when usually such decision-making precedes evidence availability.”
Her interests are in advancing mathematical methodologies to derive information that may help in decision-making under such settings. She works closely with the Centers for Disease Control and Prevention and the World Health Organization on noncommunicable diseases such as cancers and communicable diseases such as HIV.
Gopalappa runs the Disease Prediction and Prevention Lab in the MIE department, which, as she has said, “works on development of new methodologies and computational models for simulating the dynamics of disease incidence and spread for purposes of disease prediction, prevention, and control.”
As Gopalappa explained on her website, “Disease is one threat that is common to all human beings across the globe and across generations. Prediction of diseases is a tough problem because it is the outcome of a complex dynamical system that consists of interactions between multiple factors related to epidemiological, social, economical, environmental, population mobility, demographical, and individual behavioral and lifestyle. Disease prevention and intervention decisions, and subsequently resource allocation, at the national and global levels thus need to be based on evaluations of the impact of alternative decisions under this complex dynamical context.”
Gopalappa explained the background behind her NAS presentation by noting that “The White House National HIV/AIDS Strategy calls for reduction in HIV incidence in the United States by 25% by 2020. The United Nations Sustainable Development Goals on Non-communicable Diseases (NCDs) calls for reduction in premature deaths from NCDs by one-third globally by 2030. While there exists these political commitments, there is a growing recognition that, under constrained resources, successfully achieving these goals requires a highly effective strategic plan for intervention implementation.”
In her presentation, Gopalappa went on to explain that, while traditional public health intervention guidelines are based on evidence of disease impact measured independently through clinical trials or mathematical models, achieving national and global targets requires a paradigm shift in analyses of interventions. These new paradigms arise from the fact that population-level disease prevention and control involve implementation of multiple intervention programs that dynamically interact with each other so that the overall impact is not a direct sum of the individual parts.
Gopalappa added that for communicable diseases the trajectory of disease spread is defined by dynamical interactions between multiple parameters related to individual behavior, socio-economic conditions, population contact structures, and biological factors of the infectious agent.
“With the rise in computational feasibility and growing availability of data,” concluded Gopalappa, “mathematical models can play an increasingly important role through innovative techniques for simulating the above complicated dynamics, integrating disparate data sources to derive significant information that otherwise cannot be inferred through any of the data sources independently, analyzing multiple alternative combinations of interventions, and further informing data collection for more accurate design of models in the future.”
Then, at the NAS symposium, she went on to discuss her lab’s groundbreaking work in this area.
Gopalappa earned her B.E. in Industrial Engineering from Bangalore Institute of Technology and her Masters and Ph.D. in Industrial Engineering from the University of South Florida. Among other accomplishments, she has received the Steven M. Teutsch Prevention Effectiveness Post-doctoral Fellowship from the Centers for Disease Control and Prevention from 2010 through 2012. Publications of work from her team have been cited in national and global strategic plans such as the White House National HIV/AIDS Strategy and WHO guidelines on HIV treatment. Her lab most recently developed mathematical models for cost-effectiveness analyses of cancer screening strategies for low- to middle-income countries for the WHO Global Action Plan for the Prevention of Noncommunicable Diseases. (December 2016)