The COVID-19 pandemic demonstrates the devastation that infectious diseases can leave on modern society. Deciphering the dynamics of disease is essential to earlier and more effective interventions to mitigate disease spread. To fully understand these dynamics, information across systems that operate on multiple spatial and time scales, as well as evolving via feedback, must be combined. This research project aims to develop a mathematical framework to combine and analyze such information to further the understanding of infectious disease spread, providing insight for ongoing and future disease outbreaks. The results are also intended to be applicable to multi-scale questions in ecology and immunology more generally. The educational component will serve to train a diverse generation of interdisciplinary scientists, building the capacity of the STEM workforce. Through focused short courses aimed at providing specific, relevant skills in quantitative techniques and cross-disciplinary communication, graduate students in the mathematical and life sciences will learn to collaborate and communicate effectively. Training and mentoring of undergraduate and graduate students will take place through focused research projects and opportunities to garner translational skills, for example developing and implementing project-based work and participating in outreach activities. Additionally, interactive presentations will engage middle school students through exposure to cutting-edge mathematical biology research. The current COVID-19 pandemic highlighted the importance of interpretable, quantitative models that link mechanisms with data while accounting for variability. However, infectious disease dynamics remain incompletely understood, in part due to the lack of heterogeneity considered in models of immunological, ecological, and epidemiological aspects. Complex, non-linear feedbacks arise from heterogeneities; thus, novel quantitative frameworks are needed to better understand and control infectious disease. This project builds on work in ecology involving integral projection models to develop a framework incorporating trait-based variation. This framework aims to describe development of immunity in a population and, thus, determine disease risk over time. Results will be useful for monitoring infectious disease, selecting interventions, and informing public policy. The educational component aims to strengthen quantitative literacy to help produce a more interdisciplinary workforce. A group of graduate students, across applied, mathematical, and computational disciplines, will be introduced to fundamental quantitative skills such as statistical analyses, model building, parameter estimation, data visualization, and coding. Participants will connect with this material using a project-based approach with examples related to infectious disease dynamics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.