Mathematical models are essential for shaping vaccination strategies, but traditional SIR frameworks often oversimplify vaccine-induced immunity as a static value. In reality, an individual's immune state is dynamically shaped by their unique history of pathogen exposures. Crucially, silent exposures to a pathogen can lead to "immune boosting," reinforcing immunity without causing symptomatic infection. Because a primed immune system responds to lower antigen doses, even these sub-clinical exposures trigger this effect. While the biological mechanisms of immune boosting are well-documented, and recent pertussis studies highlight its significance, its broader epidemiological consequences remain largely unexplored. Therefore, this work focuses on evaluating the population-level impact of immune boosting on overall vaccine effectiveness. In this work, we employ analytical and numerical methods to explore the impact of immune boosting on population-level vaccine effectiveness (VE). Our results demonstrate that immune boosting significantly alters epidemic outcomes, revealing dynamics that diverge from classical expectations. We find that in regimes with weak or no boosting, VE increases with vaccine coverage due to standard indirect protection. However, we identify a critical threshold where VE remains constant regardless of coverage. Most notably, under strong boosting conditions, a paradoxical crossover occurs where increased vaccine coverage actually reduces vaccine effectiveness. Furthermore, under high transmission rates, population-level VE becomes entirely independent of coverage. We elucidate these counterintuitive findings by disentangling the direct and indirect effects of vaccination and utilizing an approximation model that isolates the role of multiple exposures in maintaining vaccine protection. Advisor: Prof. Nir Gavish