# Study on Early Warning Methods for Shipping Input Risks Under Consideration of Public Health Events
## 3 Design of Source Country Risk Propagation Model Based on Environmental Factors
### Numerical Simulation
The S, E, I, and R curves represent the proportion of the population in the network in susceptible, exposed, infected, and recovered states at each time step during the simulation. In the figure above, the nodes in the S state show a decreasing trend because most of the susceptible population at the start will be infected or exposed after being bitten by infected mosquitoes until there are no infected nodes in the network, at which point the transmission stops. The curves for the E and I states both show a trend of rising first and then falling, and the curve for the E state reaches its peak before the I state curve. This is because there is a time lag from exposure to infection. The infected population will continue to spread after being bitten by mosquitoes, and the exposed and infected nodes eventually recover and are removed. Therefore, the R state curve shows a gradually rising trend and eventually levels off. Figure 2 shows the comparison of the number of infected people and the number of infected mosquitoes over time. Although the trends of both are roughly similar, the number of infected mosquitoes remains higher throughout the simulation. The simulation results also show a lag between the infected population and the infected mosquitoes, which is also consistent with the transmission mechanism, meaning that an infected person needs to be bitten by mosquitoes to trigger an infection. This also illustrates the important role of vector mosquitoes in the transmission of mosquito-borne diseases like dengue. Figure 1: SEIR simulation curve results of the population. Figures 3-8, respectively, display the relationships over time between the susceptible population and the infected population, the latent population and the infected population, the susceptible mosquito population and the infected mosquito population, and the latent mosquito population and the infected mosquito population. From these, it can be observed that over time, the number of susceptible individuals decreases, while the number of infected individuals increases, reaching a peak before starting to decline, and the number of latent individuals gradually decreases over time. The number of susceptible and latent mosquitoes decreases over time, while the number of infected mosquitoes increases. Figure 3: Dynamic curve chart of humans and mosquitoes-1. Figure 2: Simulation curve results of infected humans and infected mosquitoes. Figure 4: Dynamic curve chart of humans and mosquitoes-2. Figure 5: Dynamic curve chart of humans and mosquitoes-3. Figure 6: Dynamic curve chart of humans and mosquitoes-4. Figure 7: Dynamic curve chart of humans and ants-5. Different initial infection rates were set to explore the earliest stabilization time for the system, i.e., the time by which all have finally reached a recovery state. Initial infection rates were set at 1%, 2%, \(\ldots\), and 10% to simulate the system's earliest stabilization time under different initial infection rates. The results, shown in Figure 9 below, indicate that even with different initial infection rates, the system stabilizes at around 70 days, meaning the entire population within the system recovers. Figure 8: Dynamic curve chart of humans and ants-6. Figure 9: Simulation curve chart of recovered population under different initial infection rates. The number of dengue cases obtained through simulations can be used to assess the inherent risk of shipping nodes. Meanwhile, in the construction of regional transmission models, the impacts of local climatic, economic, and health factors are considered, allowing for a more accurate measurement of the intra-regional risks at shipping nodes.