The SEVA framework proposes that COVID-19 mortality waves resulted from activity-driven depletion of finite vulnerable populations rather than traditional transmission feedback. Using logistic functions to model time-varying hazard rates, researchers reproduced both sharp-peaked and plateau-like epidemic patterns across European countries and U.S. states during spring 2020. The model suggests normalized epidemic trajectories from regions with vastly different mortality burdens shared remarkably similar temporal structures. This mathematical approach challenges conventional epidemiological thinking by demonstrating that essential wave features can emerge without explicit person-to-person transmission modeling. The framework has profound implications for understanding pandemic vulnerability patterns and could reshape how public health officials assess epidemic risk in aging populations. By focusing on vulnerable population dynamics rather than transmission rates, SEVA might better predict mortality outcomes in future health crises. However, this preprint awaits peer review, and the model's applicability beyond the specific COVID-19 timeframe remains untested. The approach represents a potentially paradigm-shifting perspective on epidemic modeling, though its practical utility for real-time pandemic response and intervention planning requires validation through independent research and broader temporal analysis.