Optimizing Within‐Host Ebola Control Through Combination Antiviral Therapy: A Mathematical Modeling Approach Identifying Critical Therapeutic Components

Ebola virus disease (EVD) is a serious and highly infectious hemorrhagic fever with significant public health implications across sub‐Saharan Africa and remains a severe public health calamity characterized by rapid within‐host disease progression and high mortality. While preventive vaccination efforts are vital, postexposure therapeutic options targeting within‐host viral replication dynamics are limited, and monotherapies frequently suffer from rapid drug resistance and incomplete clearance. Under the assumptions of a deterministic within‐host system, constant model parameters, bounded time‐dependent controls, and continuous state trajectories, this study develops a distinct within‐host mathematical model governed by Pontryagin’s maximum principle to investigate the synergistic impact of antiviral combination therapies. The framework explicitly incorporates three time‐dependent control mechanisms: a release‐blocking agent ( u ₁ ), an immune‐enhancing therapeutic ( u ₂ ), and an entry‐inhibition mechanism ( u ₃ ). The distinctive feature of the framework is the simultaneous consideration of these three complementary control mechanisms within a single time‐dependent optimal control setting. Evaluating five intervention configurations over a 100‐day simulation period, the optimal control analysis reveals that the full triple‐control strategy delivers the most rapid and complete viral clearance while preserving susceptible target cells at near‐capacity levels. Significantly, the dual‐control combination pairing entry inhibition and immune enhancement ( u ₂ + u ₃ ) emerges as a highly efficient, near‐optimal alternative that closely mirrors triple‐therapy outcomes. Furthermore, the findings identify the immune‐enhancing control as the single most indispensable component for durable disease suppression, emphasizing that antiviral intervention must operate synergistically with the host’s endogenous response. By moving beyond traditional single‐agent evaluations and static binary assumptions, this time‐dependent framework aligns with quantitative systems pharmacology (QSP) and clinical trial simulation (CTS) principles. Under the stated assumptions, the results establish the relative effectiveness of the considered control configurations and provide a basis for distinguishing the triple‐ and dual‐control strategies within the modeled therapeutic setting. Therefore, this study offers a rigorous theoretical roadmap to guide future clinical trial design, therapeutic scheduling, and resource allocation in disease outbreak settings.