# Study on Early Warning Methods for Shipping Input Risks Under Consideration of Public Health Events
## 2 Literature Review
[29], using China maritime areas and major accident investigation reports as data sources, combined with complex network theory to construct a maritime accident risk network to analyze the network's robustness, providing maritime managers with possibilities to adopt control measures to avoid accidents. [PERSON] et al. [30] constructed a dynamic maritime accident risk management framework for sea routes, which can dynamically consider the risk performance of maritime trade routes and identify key influencing factors during different time intervals. Recent years have witnessed concentrated scholarly efforts in investigating risk propagation pathways in single-layer networks, with significant applications and theoretical advancements across disciplines. However, as research deepens, the inherent limitations of single-layer network models in characterizing risk transmission mechanisms have become progressively evident. This has prompted an academic pivot toward multilayer complex networks, where researchers now systematically explore risk diffusion trajectories and impact magnitudes across interconnected layers [31; 32; 33]. The emergence of multilayer frameworks stems from empirical realities: most real-world complex systems inherently comprise multiple interdependent subsystems. Multilayer networks formally characterize interconnections between these subsystems or network strata. Unlike conventional single-layer paradigms, multilayer architectures expand analytical dimensions through three critical innovations: (1) explicit modeling of cross-layer interactions and mapping relationships; (2) hierarchical integration of subsystem dynamics; and (3) multiscale representation of complex system behaviors [34]. These structural enhancements enable superior modeling fidelity, particularly in simulating natural system complexities where traditional networks prove inadequate. The multilayer approach demonstrates unparalleled advantages in capturing emergent phenomena and cascading effects across network hierarchies [35]. Current research predominantly focuses on risk propagation in single-modal air or maritime networks, while integrated analyses of multilayer risk transmission across coupled air-sea networks under public health emergencies remain understudied. To address this gap, this study develops a threefold framework: (1) an enhanced SEIR-SEI model integrating climatic, economic, and health factors to quantify inherent nodal risks through intra-regional and cross-border transmission mechanisms; (2) a global air-maritime coupled network model capturing synergistic risk interactions using complex network theory;and (3) an entropy-weighted TOPSIS evaluation system combining network metrics (clustering coefficient, centrality measures) to objectively prioritize critical nodes. Methodologically, entropy weighting [36] eliminates subjective bias in parameter prioritization, while TOPSIS [37] enables optimal trade-offs between conflicting metrics through multi-criteria decision-making. Simulation experiments reveal that risk propagation intensity depends on the combined effects of nodal vulnerability (inherent risks) and connectivity (transmission probabilities), with cross-modal coupling amplifying cascade effects compared to isolated network analyses. The study contributes mechanistic insights into maritime network risk dynamics, advances multilayer network modeling methodologies, and provides implementable tools for preemptive risk identification and resource allocation. Practically, these findings support the targeted hardening of critical transportation nodes and inform the development of dynamic early-warning systems, offering scientific foundations for cross-border emergency coordination in global health crises.