This dissertation employs mathematical modeling to enhance public health policy, with a particular focus on behavioral and structural interventions for infectious disease control. Initially, a deterministic compartment model is developed for coronavirus disease 2019 (COVID-19) within a university setting, progressing beyond the effective reproductive number (R_0) to independently analyze transmission and contact rates. Although this model does not explicitly incorporate social conditions, it provides a foundation for evaluating nonpharmaceutical interventions (NPIs) and determining vaccination thresholds. The research then expands to integrate modeling of human immunodeficiency virus (HIV) and human papillomavirus (HPV), acknowledging their biological connections and shared social risk factors. By applying a recently developed mixed compartment and agent-based model, the study underscores the significance of behavioral and structural interventions in disease mitigation. Building upon the integrated HIV and HPV model, the study further incorporates social conditions to explore the impact of addressing the social needs of disadvantaged populations. This exploration examines hypothetical scenarios to assess how structural and behavioral interventions can influence both diseases via improvements in HIV care behavior and sexual behavior. Key findings indicate that interventions targeting social conditions can significantly reduce disease prevalence and incidence, particularly among high-risk groups. The study provides a comprehensive framework for policymakers to simulate and evaluate various intervention strategies, highlighting the necessity of integrating behavioral, structural, and pharmaceutical approaches for effective public health outcomes. Overall, this dissertation contributes to the field by developing and refining simulation models that offer practical tools for addressing complex health challenges, ultimately aiming to improve health outcomes and equity on a broader scale."