# Study on Early Warning Methods for Shipping Input Risks Under Consideration of Public Health Events
## 2 Literature Review  
International trade, transportation, and regional economic development are inherently interconnected. A strong bidirectional relationship exists between international trade and transportation systems. [PERSON] and [PERSON] [1] employed a vector autoregression (VAR) model to investigate the causal relationship between trade and the development of Australia's transportation and logistics sector, whose primary markets include China, Japan, and the United States. Their findings revealed mutual reinforcement: trade drives transportation growth, and vice versa. This conclusion is corroborated by [PERSON] and [PERSON] [2], who posit that trade fosters international integration and stimulates investments in transportation infrastructure, language proficiency, and technology--all critical enablers of transportation advancement. [PERSON] et al. [3] further validated this linkage, demonstrating that interregional trade across Spanish provinces benefits significantly from transportation networks. Thus, advanced transportation systems serve as prerequisites for efficient resource allocation, specialized division of labor, and sustainable spatial organization of economic activities. Extensive empirical studies have examined transportation's pivotal role in urban emergence, demographic shifts, and regional economic dynamics. For instance, [PERSON] and [PERSON] [4] analyzed the spatial distribution of European cities over a millennium (800-1800 CE), concluding that proximity to navigable waterways or land routes fundamentally shaped urban development. Notably, most high-density, prosperous urban areas in modern Europe remain aligned with ancient Roman road networks [5]. However, public health emergencies pose severe disruptions to transportation systems, escalating operational costs and triggering supply chain fractures, which inflict substantial economic losses on trade-dependent cities [6]. To mitigate these impacts, this study focuses on risk propagation within global shipping networks--a critical transportation modality--to optimize resilience against public health crises. In recent decades, scholars have conducted extensive research on the rules of risk propagation within networks. [PERSON] et al. [7] examined the risk of delay propagation of container ships in shipping networks. They proposed an effective method for constructing a causal network for container ship delay propagation risks based on efficient multivariate transfer clipping. Additionally, many risk propagation studies have adopted epidemic models; [PERSON] et al. [8] considered the transfer of recurrent risks based on infectious disease models. By calculating risk equilibrium points, they concluded that risks could persist in supply chains over the long term. Moreover, [PERSON] et al. [9] developed a new fuzzy dynamic Bayesian network method to improve the accuracy of dynamic risk assessment and propagation. [PERSON] et al. [10] innovatively introduced SoV (a method that starts from the causes of errors, considers the transmission and accumulation effects of errors in different processes, and uses mathematical analysis to describe this effect) into the study of quality risk propagation in the collaborative manufacturing supply chain network of complex products, proposing a method to identify key quality risk factors in supply chain networks based on the risk propagation effect. In multimodal transport networks, it is also necessary to explore the laws of risk propagation to mitigate cascading failures within the network. [PERSON] et al. [11] pioneered the integration of cascading failure theory into risk propagation analysis of multimodal transportation networks, aiming to unveil the intrinsic mechanisms of risk transmission. This approach provides a theoretical foundation for enhancing safety management in interconnected transportation systems. [PERSON] et al. [12] focused on multimodal transport networks and proposed a quantitative method based on improved percolation theory to explore the dynamics of risk transmission.