<p>This research addresses existing shortcomings in epidemic-logistics studies by emphasizing the integration of multiple models to determine optimal strategies for medical resource allocation during public health emergencies, such as the COVID-19 outbreak. The authors develop a multi-model integrated epidemic-logistics model that seamlessly merges three specific sub-models: Optimal allocation, epidemic dynamics, and production-inventory. This model dynamically tracks the real-time varying in resource inventory levels at supply nodes and the storage capacities at transit hubs within a logistics network. Unique to the proposed research is the embedding of both the production-inventory mechanism and the impact of a social intervention (Traditional Chinese medicine as the background) within a logistics framework of resource allocation. Moreover, the authors also introduce an adaptive demand function that possesses learning ability and a probabilistic understanding, crucial for gauging real-time resource demands in affected regions. The proposed innovation extends to designing a recursive and linearizable structure, transforming the intricate multi-model system into solvable sub-models, while also offering a standardized method for creating demand functions. The numerical simulations and sensitivity analysis demonstrate the efficiency and robustness of the proposed model. The proposed framework not only enhances theoretical understandings of epidemic resource management but also provides policymakers with actionable strategies for future pandemics.</p>