Timely and effective decision-making is critical during epidemics to reduce preventable infections and deaths. This demands integrated models that jointly capture disease dynamics, vaccine distribution, regional disparities, and behavioral responses. However, most existing approaches decouple epidemic forecasting from logistics planning, hindering adaptive and regionally responsive interventions. We propose a novel epidemiological-optimization framework that jointly models epidemic progression and a multiscale vaccine supply chain. The model incorporates spatio-temporally varying effective infection rates to reflect regional policy and behavioral dynamics. It supports coordinated, data-driven decision-making across spatial scales through two formulations: a multi-objective Gini-based model and a knapsack-based model that leverages regional vulnerability indicators for tractability and improved mitigation. To address computational complexity, we design two scalable heuristic decomposition algorithms inspired by the Benders decomposition. The model is validated using COVID-19 data in the U.S.. We introduce SARIMA-based forecasting as a novel approach for validating epidemic-optimization models under data limitations. The results show that our approach can prevent more than 2 million infections and 30,000 deaths in just six months while significantly improving the accessibility of vaccines in underserved regions. Our framework demonstrates that integrating fairness and epidemic dynamics with vaccine logistics leads to superior outcomes compared to traditional myopic policies. Fairness improves overall efficiency in the long term by prioritizing the most vulnerable populations, leading to better long-term public health outcomes. The model offers policymakers a scalable and operationally relevant tool to strengthen preparedness and ensure a more effective and equitable response to epidemics.