Disease Emulation Workflow

EpiSimS is a large, agent-based simulation that models the spread of epidemics, allowing for the assessment of disease prevention, intervention, and response strategies. It is used as an experimental test bed for analyzing the consequences, feasibility, and effectiveness of response options to disease outbreaks. Due to its large resource, time, and parameter constraints, data analysis and visualization can be challenging. We work with the domain scientists to help them understand and view their data better. Our most recent project involved integrating an emulation workflow into the EpiSimS framework to allow scientists to predict and analyze how full runs will look.


Chris is a PhD student at UC Davis working with Los Alamos National Laboratory in visual and data analytics. His research interests include genomic and educational visualization, disease visualization, and predictive analytics. His current projects include integrating machine learning and statistical modelling into simulation and emulation workflows, for epidemic and cosmological simulations.