# 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  
In order to analyze the inherent risks of different shipping nodes, this paper uses Python 2021.3.3 programming to simulate risk propagation within the shipping node areas. Additionally, this study incorporates economic and health indicators through GDP data sourced from the Wind Database and the Global Health Security Index obtained from the website ([[https://www.ghsindex.org/](https://www.ghsindex.org/)]([https://www.ghsindex.org/](https://www.ghsindex.org/)) accessed on 11 August 2024). Mosquito surveillance data and climatic parameters are derived from project-specific monitoring datasets, while mosquito-borne transmission rates are calculated based on established literature. All datasets undergo normalization to ensure comparability across heterogeneous variables. Taking the example of Chinese nodes, the parameters in the simulation are set as shown in Table 1: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Description** & **Value** & **Source** \\ \hline \multirow{3}{*}{\(\beta_{1}\)} & Transmission rate from infected mosquito & \multirow{3}{*}{\(-0.097X_{4}-0.049X_{5}+0.313\)} & Calculated from \\  & population to the susceptible human & & original data \\  & population & & \\ \hline \multirow{3}{*}{\(\sigma_{1}\)} & Incidence rate from latent human & \multirow{3}{*}{\(1/5\)} & Literature [38] \\  & population to infected human population & & \\ \hline \multirow{3}{*}{\(\gamma\)} & Recovery rate from infected human & \multirow{3}{*}{\(1/6\)} & Literature [38] \\  & population to recovered human population & & \\ \hline \multirow{3}{*}{\(\beta_{2}\)} & Transmission rate from infected human & \multirow{3}{*}{\(3/5\)} & Literature [38] \\  & population to susceptible mosquito & & \\ \cline{1-1}  & population & & \\ \hline \multirow{3}{*}{\(\sigma_{2}\)} & Incidence rate from latent mosquito & \multirow{3}{*}{\(1/4\)} & Literature [38] \\  & population to infected mosquito population & & \\ \hline \multirow{3}{*}{\(N_{H}\)} & Total human population & \multirow{3}{*}{\(100\),\(000\)} & - \\  & & & \\ \hline \multirow{3}{*}{\(N_{M}\)} & Mosquito population size & \multirow{3}{*}{\((0.12X_{1}-0.12X_{2}-0.32X_{3}+10.03)S\)} & Calculated from \\  & & original data \\ \hline \multirow{3}{*}{\(\mu_{H}\)} & Natural birth rate of human population & \multirow{3}{*}{\(0.0068\)} & National Bureau of \\  & & & Statistics \\ \hline \multirow{3}{*}{\(d_{H}\)} & Natural death rate of human population & \multirow{3}{*}{\(0.0074\)} & National Bureau of \\  & & & Statistics \\ \hline \multirow{3}{*}{\(\mu_{M}\)} & Natural birth/death rate of mosquito & \multirow{3}{*}{\(1/15\)} & Literature [38] \\  & population & & \\ \hline \multirow{3}{*}{\(X_{1}\)} & Daily average temperature & Statistical data \\ \hline \multirow{3}{*}{\(X_{2}\)} & Daily average relative humidity & Statistical data \\ \hline \multirow{3}{*}{\(X_{3}\)} & Average wind speed & Statistical data \\ \hline \multirow{3}{*}{\(X_{4}\)} & Economic factors & Statistical data \\ \hline \multirow{3}{*}{\(X_{5}\)} & Health factors & Statistical data \\ \hline \multirow{3}{*}{\(S\)} & Area of the region & Statistical data \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter setting table. After conducting the simulation based on the above settings and the collected data, this section provides the results for the susceptible, latent, infected, and recovered populations within the nodes. The change in the number of recovered individuals over time and the comparison of the number of infected individuals with the number of infected mosquitoes over time are also examined. Firstly, Figure 1 below shows the change in the number of susceptible, exposed, infected, and recovered individuals in the population over time. It can be seen that the number of infected people increases initially, reaches a peak at around 30 days, and then begins to decline. This is consistent with typical trends.