Combining Models and Data to Explore Infectious Disease Dynamics Across Time and Space

Infectious disease dynamics are shaped by complex interactions across biological scales, from within-host immune processes to host-to-host transmission and broader environmental influences such as climate and landscape. These dynamics vary across host-pathogen systems: some infections drive host population cycles, while others persist asymptomatically. Understanding the mechanisms behind disease persistence, spread, and evolution is increasingly urgent in the face of rapid global change. However, empirical data limitations present challenges, especially in systems with patchy pathogen distributions or difficult-to-measure environmental drivers. To address these gaps, this dissertation leverages mechanistic modeling and phylogenetic tools to explore pathogen dynamics in two distinct wildlife systems: spongy moths in North America and fruit bats in Madagascar. First, I investigate competition between a virus and a fungus co-circulating in the spongy moth (Lymantria dispar). Using field data and fitted mechanistic models, I show how dispersal, weather, and initial conditions jointly influence infection outcomes. I then use these insights to predict how pathogen competition and may shift under climate change, with implications for forest health. Next, I focus on viruses in Malagasy fruit bats. I model the spatiotemporal dynamics of henipavirus infection in Eidolon dupreanum, integrating novel predictions of birth pulse timing, habitat suitability, and bat movement. These infection maps are linked with spatial layers of human and environmental factors to assess zoonotic risk. I then shift to evolutionary questions, using molecular surveillance to document and phylogenetically characterize astroviruses across Madagascar's three endemic fruit bat species. The results reveal both host-specific and geographic viral patterns and contribute to the growing understanding of bat virus diversity and evolution in the Southwest Indian Ocean region. Together, these studies span biological and ecological scales, from within-host viral diversity to landscape-level infection modeling. By integrating field data with ecological and evolutionary modeling frameworks, this dissertation highlights the diversity of infectious disease dynamics and provides tools to better understand wildlife disease systems of importance to public and environmental health.