## 2 Route Network Construction
### Determination of Route Network Margins
The remaining three indicators were processed in the same way, as follows:  
\[AS_{j}=\frac{AS_{j}-\text{min}AS(e)}{\text{max}AS(e)-\text{min}AS(e)} \tag{7}\]  
In summary, the weighted sum of the four metrics is the combined edge weight of the route network connecting edges \(w_{j}\):  
\[w_{j}=0.5650\times AS_{j}+0.2622\times DF_{j}+0.1175\times WC_{j}+0.0553\times MA _{j} \tag{8}\]  
From the definitions of the four indicators, it can be seen that the larger the route network edge weight \(w_{j}\) is, the more complex the operational posture of the edge and the higher the degree of operational risk. As shown in Table 3, the route network contiguous edge weights are derived through weighting. Subsequently, according to the segment correlation analysis, it can be seen that there are different degrees of influence between segments, and when there are flight delays in the segments, these degrees of influence will be transformed into the corresponding weights, which will have a certain impact on the delays of the segment network, and the correlation network of East China is constructed based on the correlation analysis of the segments as shown in Figure 2.  
In Figure 2, the East China correlation network includes 195 segment nodes and 917 segment connecting edges. Combined with reality, we believe that the longer the segment distance in real segments, the larger the delay adjustable space of the segment, the higher the self-recovery probability of the nodes, and therefore invoke the logistic Steele growth model to quantify the distance to derive the contiguous edge recovery probability. The Logistic Growth Model (LGM), also known as the self-inhibitory equation, is a mathematical model widely used to describe population growth, market demand, resource consumption, and other areas. It assumes that the growth rate will gradually slow down with the finiteness of resources and eventually reach a steady state. For example, [PERSON] [22] et al. introduced the logistic Steele growth model to improve the accuracy of medium- and long-term forecasting and better predict the container throughput of container ports. In this paper, we believe that the logistic Steele growth model can be used to characterize the growth of segment distance, predict the saturation point and growth trend of the route, and quantify the infection probability. The formula is expressed as follows:  
\[P(L)=\frac{1}{1+e^{-\left[\alpha\times(L-L_{0})\right]}} \tag{9}\]  
where \(P(L)\) is the probability at a segment length of \(L\); \(\alpha\) is a positive coefficient controlling the growth rate. \(L_{0}\) is the half-saturation constant, i.e., \(P(L)\) reaches half of its maximum value when \(L\) peaks. The recovery probability of each segment node can be calculated by the above equation, as shown in \[W=[W_{1}W_{2}W_{3}W_{4}]=[0.56500.26220.11750.0553] \tag{3}\]0 \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Section Number** & **Name** & **Distance** & **Recovery Probability** \\ \hline
1 & AKARA -LAMEN & 48.93015096 & 0.266843177 \\
2 & KALBA -DYVOR & 155.2191865 & 0.513045005 \\
3 & HSVOR -POMOK & 69.68911026 & 0.309360889 \\... &... &... &... \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Nodal recovery probability values for each navigation segment. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Conception** & **Significance** & **Mathematical Expression** \\ \hline Node & Airport and navigation & Set of points \\  & station & \(V=\{v_{1},v_{2},\cdots,v_{104}\}\) \\ Side & Data & Side set \(E=\{e_{1},e_{2},\cdots,e_{195}\}\) \\ Weight & The passing probability of each & \(W_{ij}\) of the edges between points \(i\) \\  & route under the influencing factors & and \(j\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Route network factor. Figure 2: Weighted diagram of the network associated with the East China Segment. In Table 4, we derive the recovery probability based on the LGM from the known segment distance to provide data support for the next step in analyzing the spread of infectious disease delays.