Emerging infectious diseases are diseases that appear in new hosts or in a new place. They can result when new strains of a pathogen arise, or from a pathogen being introduced into a new area, and can threaten wildlife, livestock, and humans through illness and death. Because most emerging infectious diseases that shift to humans come from wildlife it is important to understand wildlife diseases. Often, scientists use mathematical models of disease as tools to understand existing patterns of how a disease spreads or to help predict future trends. This research focuses on understanding differences among hosts in how they behave, or how their bodies respond to infection affect the next step in a disease epidemic: when the pathogen spreads among hosts. This research will help scientists better understand how individual differences in what happens after infection should be incorporated into disease models. With this information, scientists and managers will be able to better design data collection and disease models for the targeted control of emerging infectious diseases. The project will also train a graduate student and result in the development of new teaching tools for grade school and undergraduate students, including a website that could be used at many other colleges and universities.Given the potential for severe consequences of emerging infections diseases with wildlife sources and the time and resource-intensive nature of gathering pathogen data in wildlife populations, improved disease models are necessary tools for providing insight into the research effort necessary to capture the transmission process. The objective of this research is to link empirical and modeling approaches to better understand how among-host variation in traits affects pathogen transmission in a wildlife system. It will integrate a theoretical model with empirical data from the host-pathogen system of house finches and their bacterial pathogen, Mycoplasma gallisepticum. This work will address how the behavioral and physiological phenotypes affect disease dynamics and will also explore the effects of infection-induced behavioral changes. Experimental M. gallisepticum infection data combined with empirical contact network data will be used to parameterize a dynamic network disease model. This research will provide novel insights into the potential role of covariation between behavioral and immune competence in epidemic dynamics, and will serve as one of the first models to apply dynamic networks to an emerging infectious disease system in wildlife.