Program information is available at  http://qbios.gatech.edu

Abstract
The growing crisis of antibiotic resistance is placing increasing urgency on the search for novel therapeutics, and raises the new challenge of identifying drugs that will continue to work in the face of inevitable pathogen evolution. I will discuss new theory and data highlighting how interventions targeting bacterial social behaviours can cause selection for resistance to be reduced, or even reversed, opening a path towards the development of evolutionarily robust novel therapeutics.

The Ebola epidemic in West Africa has spurred an international response. This response has been strongly influenced by epidemiological models that predicted a devastating rise in cases without large-scale changes in behavior and intervention. In this talk, I introduce the mathematical principles underlying predictions of the rate and scope of a disease epidemic.  I then explain how such principles have been applied to forecasting Ebola virus disease (EVD) dynamics and identifying the type and scale of necessary control. One control mechanism involves influencing behavior and social norms to limit post-death transmission, e.g., during burial ceremonies of individuals who died from EVD. Post-death transmission for EVD has been recognized for over 10 years, yet its relative importance in the current epidemic remains uncertain.  I conclude my talk with an analysis of ongoing challenges in estimating the relative importance of post-death transmission from early-stage epidemic data.  I show why such estimation is hard and yet, nonetheless, why controlling post-death transmission is likely to have a substantial effect on short- and long-term epidemic outcomes.