I am working with Bill Eddy investigating the statistical properties of agent-based models and their relationship to compartment models for applications in epidemiology. More specifically, I am interested in model selection and model validation in these contexts in order to create more accurate and interpretable models in order to better predict and curb the spread of infectious disease.
Other interests include the production and maintenance of synthetic ecosystems (such as SPEW made with Lee Richardson and Sam Ventura), data visualization (such as SPEW View, which won first prize at the MIDAS Mission Public Health Hackathon in 2016), sports analytics (see Kayla Frisoli , Amanda Luby, and my Honorable Mention and $1,000 award paper at CMSAC18), and improving community engagement and support for the statistics community via Women in Statistics (see this recent article that was in the news).
SPEW: Synthetic Populations and Ecosystems of the World. Gallagher, S., Richardson, L. F., Ventura, S.L., and Eddy, W.F.. Journal of Computational and Graphical Statistics, 2018.
“Nine Ways to Estimate R0 in the SIR Model with an example from H1N1 Pandemic Influenza A.” Gallagher, S., Chang, A., and Eddy, W.F. In preparation, 2019.
"Opening up the court (surface) in tennis grand slams". Gallagher, S., Frisoli, K., and Luby, A. Submitted, 2019
Prediction Fever: Modeling Influenza with Regional Effects (ADA Final Report; 2/2016). Joint work with Ryan Tibshirani, Roni Rosenfeld, and Bill Eddy.
Opening up the (court) surface in tennis grand slams (Carnegie Mellon University Sports Analytics Conference. 10/2018, Pittsburgh, PA). Honorable Mention Presentation . Joint work with Kayla Frisoli and Amanda Luby.
SPEW: Synthetic Populations and Ecosystems of the World (International Conference on Synthetic Populations. 2/2017, Lucca, Italy). Invited presentation. Joint work with Lee Richardson, Samuel Ventura, and Bill Eddy.
Generating Synthetic Ecosystems: A Tutorial (International Conference on Synthetic Populations. 2/2017, Lucca, Italy). Invited presentation. Joint work with Lee Richardson, Samuel Ventura, and Bill Eddy.
Statistical Modelling of Infectious Diseases: Influenza and the “Next Disease” (Women in Statistics and Data Science. 10/2016, Charlotte, NC). Joint work with Ryan Tibshirani, Roni Rosenfeld, Bill Eddy, Sam Ventura, and Lee Richardson
Services for the MIDAS Network: Visualization and Synthetic Ecosystems (MIDAS, 5/2016, Washington D.C.). Joint work with Sam Ventura, and Lee Richardson.
From Forecasting the Flu to Predicting the “Next” Disease (UP-Stat; 4/2016; Buffalo, NY). 2nd place student presentation. Joint work with Ryan Tibshirani, Roni Rosenfeld, Bill Eddy, Sam Ventura, and Lee Richardson
Prediction Fever: Modeling Influenza with Regional Effects (ADA; 12/2016; Pittsburgh, PA). Joint work with Ryan Tibshirani, Roni Rosenfeld, and Bill Eddy.
As a part of the MIDAS Informatics Services Group, I am producing tools and analyzing data for infectious disease modeling. Specifically, we have made SPEW, an
R package to create agents for use in agent-based models.
I am a member of the CMU Sports Analytics Graduate Student group. Most recently, my collaborators and I won an Honorable Mention for our submission for CMSAC Reproducible Research Competition.
For my first-second year Advanced Data Analysis Project, I predicted the incidence of the flu using an Empirical Bayes model with regional dependencies as a part of CMU DELPHI.
As an undergraduate at CMU, I worked with the NSF Census Research Network on a project about record linkage of multiple databases.
Catalyst: Compartment and agent-based models temporal analysis and testing (Catalyst). A software suite for easy analysis of compartment and agent-based models under varying assumptions of population heterogeneity, disease conditions, environmental conditions, and agent features.