<p dir="ltr">Antigenically variable pathogens (AVPs) pose a significant infectious disease burden, but vaccine development is extremely difficult due to their ability to quickly evolve beyond host immunity. Existing models of AVP spread have not been able to sufficiently account for host immune history, population mobility patterns, and pathogen evolutionary dynamics. This thesis aims at creating a computational framework built from the concept of antibody landscapes to overcome these issues, thereby increasing the understanding of how these pathogens spread and evolve in order to improve vaccine design.</p><p><br></p><p dir="ltr">Briefly, the proposed stochastic framework is built from "the ground up'' using principles of antibody landscapes, in which we begin by devising a mechanism to describe how the landscape changes due to repeated pathogen exposure. Extending this to a (sub)population-level permits integration into a meta-population model that is further parameterized by geographic influences. Virus evolution is driven by a statistically meaningful model of antigenic drift in the underlying antigenic space. While the framework is robust and, in principle, capable of modeling a variety of AVPs, we focus on influenza H3N2 as a case study due to its data availability and persistently low and unpredictable vaccine efficacy.</p><p><br></p><p dir="ltr">Experimental results demonstrate that we can statistically significantly predict various properties of H3N2 evolution and population level immunity, including prevalence level, the timing of emergence of new antigenic clusters, the positions of unseen strains in antigenic space, as well as the geographic locations where new strains and antigenic clusters emerge. Through analysis of the simulated outcomes, we identified a population level of immune protection against circulating strains (titre value of approximately 5 units), which when approached, seems to signal an upcoming antigenic drift. Using this insight, we propose a new vaccine strain selection strategy that shows notable improvements in vaccine effectiveness and stability. Additionally, we estimate that it could reduce annual morbidity by 73.4 ± 40.8 million (17% ± 9%) in the Northern Hemisphere and 56.7 ± 38.0 million (10% ± 6%) in the Southern Hemisphere. In summary, this novel framework can accurately replicate the interplay between pathogen evolution and population-level immune responses decades into the future from a mechanistic perspective, and be used to design improved vaccines.</p>