Emerging infectious diseases in wildlife have become a growing concern to human health and biological systems with more than 75 percent of known emerging pathogens being transmissible from animals to humans. Highly pathogenic avian influenza (HPAI) H5N1 has caused major global concern over a potential pandemic and since its emergence in 1996 has become the longest persisting HPAI virus in history. HPAI viruses are generally restricted to domestic poultry populations, however, their origins are found in wild bird reservoirs (Anatidae waterfowl) in a low-pathogenic or non-lethal form. Understanding the spatial and temporal interface between wild and domestic populations is fundamental to taking action against the virus, yet this information is lacking. My dissertation takes two approaches to increase our understanding of wild bird and H5N1 transmission. The first includes a field component to track the migratory patterns of bar-headed geese (Anser indicus) and ruddy shelduck (Tadorna ferruginea) from the large H5N1 outbreak at Qinghai Lake, China. The satellite telemetry study revealed a new migratory connection between Qinghai Lake and outbreak regions in Mongolia, and provided ecological data that supplements phylogenetic analyses of virus movement. The second component of my dissertation research took a modeling approach to identify areas of high transmission risk between domestic poultry and wild waterfowl in China, the epicenter of H5N1. This effort required the development of spatial models for both the poultry and wild waterfowl species of China. Using multivariate regression and AIC to determine statistical relationships between poultry census data and remotely-sensed environmental predictors, I generated spatially explicit distribution models for China's three main poultry species: chickens, ducks, and geese. I then developed spatially explicit breeding and wintering season models of presence-absence, abundance, and H5N1 prevalence for each of China's 42 Anatidae waterfowl species. The poultry and waterfowl datasets were used as the main inputs for the transmission risk models. Distinct patterns in both the spatial and temporal distributions of H5N1 risk was observed in the model predictions. All models included estimates of uncertainty, and sensitivity analyses were performed for the risk models.