John 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 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 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 wrote the book on modeling infectious diseases. Literally. Pej is a world class disease modeler and expert on the dynamics of pertussis, the pathogen that causes whooping cough. Pej’s lab can be found at the University of Georgia.
Bodgan’s area of expertise — vibrations and acoustics — 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 on this can be found at his University of Michigan website.
Suzanne 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. More about her work can be found here.
Chris is an ecologist at the University of Georgia. 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. More on his research can be found here.
Eamon 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.