A major frontier in disease ecology is understanding the transmission dynamics of generalist pathogens, where multiple host species are involved in ongoing circulation of a single pathogen. These dynamics violate assumptions of the simplest epidemic models and lead to challenges in characterizing the determinants of transmission because of the interwoven contributions of different host species. Since many pathogens (including all zoonoses, by definition) can infect multiple hosts, learning about complex multi-host systems from partial evidence is a pervasive problem in disease ecology. Understanding transmission dynamics of a generalist pathogen in a community of wildlife host species faces the additional challenges of analyzing sparse wildlife data. Biological and logistical sampling constraints in wildlife systems can create temporally and spatially coarse data, which can be difficult to analyze by conventional statistical methods. Thus, analyzing and integrating these data streams requires the development of novel methods.