# Transportation and Reserve of Emergency Medical Supplies during Public Health Events
## 5 System Simulation Based on Six Cities in Jiangsu Province  
### Simulation of the Urgency of Demand in Six Cities in Jiangsu Province  
In order to verify the feasibility, real time, and rationality of the model, the model in this paper is simulated. According to different locations (four cities in southern Jiangsu, one city in central Jiangsu, and one city in northern Jiangsu), the data of six cities in Jiangsu Province (Nanjing, Wuxi, Xuzhou, Changzhou, Suzhou, and Nantong) were selected for simulation, and six cities in Jiangsu Province were designed as six demand points. The urgency of the demand of six cities was calculated according to the epidemic data released by Jiangsu Health and Health Commission, Municipal Health and Health Commissions and CDC. Firstly, the SEIRD model is used to predict the number of infected people. According to the data released by the Jiangsu Provincial Health and Wellness Committee Office, the data released on 1 February 2020 is set as the initial data [47], as shown in Table 11. At this time, the epidemic had just begun to spread and emergency medical materials were in urgent demand. As emergency medical supplies stored in each city were sufficient in the previous month, the urgency of demand in each city was predicted on the 31 st day and emergency medical supplies were dispatched. According to the calculation steps in Section 3.1 of this paper, use MATLAB to write the code, and take the forecast data of the 31 st day as shown in Table 12. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicators** & **1** & **2** & **3** \\ \hline X1 & \(-\)0.041 & 0.41 & 0.001 \\ X2 & \(-\)0.108 & \(-\)0.028 & 0.355 \\ X3 & 0.015 & \(-\)0.075 & \(-\)0.455 \\ X4 & 0.164 & 0.012 & 0.212 \\ X5 & 0.198 & \(-\)0.027 & 0.131 \\ X6 & 0.156 & 0.104 & \(-\)0.256 \\ X7 & 0.03 & \(-\)0.063 & 0.335 \\ X8 & 0.186 & \(-\)0.001 & \(-\)0.004 \\ X9 & 0.23 & \(-\)0.37 & 0.031 \\ X10 & 0.01 & \(-\)0.389 & 0.023 \\ X11 & 0.215 & \(-\)0.02 & \(-\)0.066 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Composition matrix table. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Nanjing** & **Wuxi** & **Xuzhou** & **Changzhou** & **Suzhou** & **Nantong** \\ \hline \(S\) & 9.32 million people & 7.464 million people & 9.084 million people & 5.28 million people & 12.75 million people & 7.728 million people \\ \(E\) & 528 & 255 & 620 & 192 & 571 & 560 \\ \(I\) & 33 & 19 & 30 & 15 & 36 & 17 \\ \(R\) & 2 & 0 & 0 & 0 & 2 & 0 \\ \(D\) & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Initial epidemic data of six cities in Jiangsu Province. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Nanjing** & **Wuxi** & **Xuzhou** & **Changzhou** & **Suzhou** & **Nantong** \\ \hline \(S\) & 7,258,937 & 6,369,747 & 6,810,072 & 4,455,732 & 10,436,400 & 5,772,536 \\ \(E\) & 1,287,407 & 693,591 & 1,411,396 & 521,551 & 1,456,982 & 1,212,793 \\ \(I\) & 672,290 & 349,168 & 748,666 & 23,719 & 745,479 & 644,559 \\ \(R\) & 97,277 & 49,414 & 109,294 & 37,422 & 106,613 & 94,150 \\ \(D\) & 4093 & 2079 & 4598 & 1574 & 4846 & 3961 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Forecast data of six cities in Jiangsu Province on the 31 st day (person). After calculation, the forecast of the demand for emergency medical supplies is obtained on the 31 st day: Nanjing-2,881,025.25 kg, Wuxi-2,283,539.1 kg, Xuzhou-2,819,734.2 kg, Changzhou-1,509,471.75 kg, Suzhou-3,919,472.1 kg, and Nantong-2,399,772.75 kg. Based on Jiangsu Statistical Yearbook-2021 data and the data released by municipal health and health commissions, the urgency of demand \(Y_{1k}\) of epidemic prevention and control ability in each city is Nanjing-0.1861, Wuxi-0.1975, Xuzhou-0.0963, Changzhou-0.1766, Suzhou-0.1854, and Nantong-0.1851, respectively. Based on the forecast of emergency medical material demand on the 31 st day, the emergency medical materials demand urgency \(Y_{2k}\) of each city is Nanjing-0.1822, Wuxi-0.1444, Xuzhou-0.1783, Changzhou-0.0955, Suzhou-0.2479, and Nantong-0.1518, respectively.