Infectious Disease Modeling in Public Health

Friday, March 15, 2019 - 1:25pm - 2:25pm
Lind 305
Karyn Sutton (The Institute for Disease Modeling)
Challenges in public health and infectious diseases involve understanding inherently complex, often nonlinear processes, typically along with some uncertainty. Mathematical modeling, statistical analysis, and related methodologies have proven useful in a variety of ways, including interpreting health data to quantify the effect of a given intervention, and predicting the impact of proposed intervention strategies. In addition to academic settings, these approaches are useful to government workers, NGOs, and various institutes, all working to find and implement solutions to global health challenges. While there is much overlap working in these settings, there are subtle but important differences. I will highlight some of these in the context of some ongoing efforts in modeling the dynamics of the ongoing TB epidemic.

Karyn came to IDM from the Department of Mathematics at the University of Louisiana at Lafayette where she was an Associate Professor. Her research has been in applied mathematics with a heavy focus on modeling and inverse problems. She has worked on a wide range of applications such as population dynamics of invasive species, infectious disease dynamics of Mycobacterium marinum (fish TB), cell proliferation dynamics, intracellular signal transduction pathways, effective vaccination of pneumococcal diseases, behavior change mechanisms, dynamics of social networks, etc. She has developed, modified, and calibrated mathematical models to be used as tools to predict outcomes, gain insight into underlying processes, and anticipate effectiveness of control strategies or management scenarios (e.g., eradication of invasive species, vaccination and treatment of infectious diseases, etc.). She has focused on the use and development of inverse problem methods (including parameter estimation, sensitivity analysis, residual-based information content statistics) to inform model structure and calibration, and to intelligently guide the design of experiments. These efforts have necessarily involved the establishment of mathematical results in inverse problem methods in delay differential equations, and approximation schemes for PDE models. As part of the TB group at IDM, she is using modeling and related inverse problem and statistical approaches to understand the current state of the TB burden and possible reasons for its slow decline globally, in addition to aiding in the design and effective implementation of preventative and treatment measures.

She has directed two PhD Dissertations, and was the PI on a 3-yr grant from the Louisiana Board of Regents as part of their Research Competitiveness Subprogram. Prior to her academic position, she was a postdoctoral research associate at the Center for Research in Scientific Computation at North Carolina State University. She earned her PhD and Master’s degrees in Mathematics at Arizona State University, and Bachelor’s degree in Mathematics and Cell & Molecular Biology at the University of Michigan – Ann Arbor.