People

John Drake is an ecologist at the University of Georgia. John’s past projects in infectious disease dynamics have included West Nile virus, whooping cough, avian influenza, and White Nose syndrome (an emerging fungal pathogen of bats). Click here for more information about his lab.

Andrew Park is an evolutionary biologist and modeler at the University of Georgia. His area of expertise is emerging pathogens with a focus on the host-parasite interaction. More about his lab’s work is available here.

Matt Ferrari is the statistical expert in the group. One of Matt’s areas of expertise—the dynamics of measles in developing countries—is central to this project. More from his team at Penn State can be found at his lab’s website.

Pej Rohani is from the University of Georgia. Pej wrote the book on modeling infectious diseases. Literally. It’s called Modeling Infectious Diseases in Humans and Animals. Pej is a world class disease modeler and expert on the dynamics of pertussis, the pathogen that causes whooping cough. His lab’s work can be found here.

Bogdan Epureanu (University of Michigan) studies vibrations and acoustics. This may seem to be rather unrelated to infectious disease dynamics, but it’s not. Bogdan is one of the best when it comes to detecting when mechanical systems are near to criticality. More about his research group at the University of Michigan can be found here.

Suzanne O’Regan (North Carolina A&T) has developed theory for anticipating infectious disease emergence and elimination. At NIMBioS, she is developing a mathematical framework to elucidate the influence of changing environmental drivers on infectious disease risk.

Toby Brett is a postdoc at the University of Georgia working on mathematical models of infectious diseases, early-warning signals for disease emergence, theory and application of stochastic processes, and the science of complex systems.

Eamon O’Dea (University of Georgia) is a computational biologist at the University of Georgia who has previously worked on building and fitting models for the spread of several infectious diseases. He is interested in understanding when predictions based on CSD are accurate and especially when such predictions are more accurate than those obtained using traditional methods. More on his research can be found here.

Andrew Tredennick (University of Georgia) is a quantitative ecologist interested in the predictability of complex dynamics in space and time. He is working on confronting the theory of critical slowing down with data. More about his research can be found here.

Amalie McKee is a postdoc at Penn State. She uses mathematical models to compare vaccination strategies to achieve and maintain disease elimination. She is recently interested in using data to find signatures of progress towards disease elimination at different levels of aggregation.

Chris Dibble worked on the project as a postdoc at the University of Georgia from 2015-2016. His prior research examined the consequences of intraspecific variation in host traits for disease dynamics. Chris is interested in applying CSD theory to real-world outbreak data in order to better utilize its many practical implications.

Shiyang Chen is a PhD student in the College of Engineering at the University of Michigan. His research focuses on developing data driven methods to detect the instability of complex nonlinear systems. His contributions to Project AERO are concerned with developing early warning signals for systems with spatial distribution.

Paige Miller is a PhD student in the Odum School of Ecology at the University of Georgia. Her past work concerned looking for signals of critical slowing down in periodically forced systems. Her contributions to Project AERO are concerned with separating seasonality from bifurcation-induced noise.

Éric Marty (University of Georgia) is a data visualization professional with a background in graphic design, interactive art and computer music. He is interested in novel and multi-modal data representation. More about his work can be found here.

Spencer Hall (University of Georgia) is a statistical analyst familiar with machine learning techniques such as gradient boosting, neural networks, support vector classifiers, decision trees, and random forests. He develops in R, R Markdown, and R Shiny.