Abstract

Cancer prevention and screening systems in the United States remain structurally reactive despite decades of epidemiological evidence demonstrating the value of early detection and targeted intervention. Large-scale analytic studies have revealed persistent spatial, demographic, and socioeconomic disparities in cancer incidence, mortality, and screening uptake, underscoring the limitations of static policy design and retrospective evaluation approaches (Hasan et al., 2021). Although predictive analytics has improved forecasting of healthcare costs and outcomes, most existing models operate in isolation from operational capacity, behavioral response, and policy feedback mechanisms, limiting their effectiveness in population-level prevention (Hasan et al., 2025).
This paper advances a digital twin framework for population-level cancer prevention and screening optimization that integrates epidemiological risk, healthcare system capacity, behavioral dynamics, and governance constraints into a continuously updating decision environment. Drawing on systems theory, operations research, and computational health analytics, the study addresses a critical gap in the literature: the absence of theoretically grounded digital twin architectures designed explicitly for preventive oncology rather than downstream treatment or logistics optimization. Prior digital twin applications in healthcare have focused primarily on supply chains, vaccine distribution, and infrastructure resilience rather than long-horizon preventive outcomes (Rasel et al., 2022; Shah et al., 2024).
Using structured analytical scenarios informed by empirical research in cancer analytics, healthcare optimization, patient engagement, and infrastructure security, the framework demonstrates how digital twins expose feedback mechanisms linking screening adherence, capacity constraints, and equity outcomes. The contribution is threefold. First, the paper extends digital twin theory into preventive public health. Second, it introduces a multi-horizon optimization logic that balances early detection, system utilization, and equity. Third, it articulates governance, privacy, and ethical requirements necessary for population-scale deployment. The framework provides a rigorous foundation for future empirical implementation of adaptive, equity-sensitive cancer prevention systems.