## 3 The Model Processing and Analysis Forecasting Result
### Forecasting Results of Individual and Combined Models
Therefore, the combination model in the forecasting and analysis of tuberculosis prevalence rate trend shows good forecasting performance. Therefore, infectious disease control has great significance. Figure 2: Stack bars of forecast errors for the four income groups. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Low Income Group**} & \multicolumn{6}{c}{**Testing Processing**} \\ \hline \multicolumn{1}{c}{**Low Income Group**} & \multicolumn{6}{c}{**Lower Middle Income Group**} & \multicolumn{6}{c}{**Upper-Middle Income Group**} & \multicolumn{6}{c}{**High Income Group**} \\ \hline
**Model** & **RMSE** & **MAPE** & **Model** & **RMSE** & **MAPE** & **Model** & **RMSE** & **MAPE** & **Model** & **RMSE** & **MAPE** \\ \hline Ploy2 & 3.1923 & 0.9025\% & Poly2 & 1.5813 & 0.5273\% & Poly2 & 1.1996 & 1.095\% & Poly2 & 0.3085 & 2.0628\% \\ Sin2 & 2.6980 & 0.7571\% & Sin1 & 1.4919 & 0.4954\% & Sin1 & 1.1222 & 1.0376\% & Sin2 & 0.2800 & 2.0474\% \\ Reci-ploy2 & 2.3285 & 0.7158\% & Reci-ploy2 & 1.0076 & 0.3792\% & Reci-ploy2 & 0.6435 & 0.6489\% & Reci-ploy2 & 0.3099 & 1.8779\% \\ Reci-exp2 & 2.2721 & 0.7642\% & Reci-exp2 & 0.4466 & 0.1717\% & Reci-exp2 & 0.6599 & 0.7147\% & Reci-exp2 & 0.3483\% & 2.3453\% \\ Power2-ploy2 & 2.3781 & 0.6274\% & Power2-ploy2 & 1.2326 & 0.4219\% & Power2-exp2 & 0.9197 & 0.8701\% & Power2-ploy & 0.3062 & 1.9825\% \\ Power2-exp2 & 2.8064 & 0.8106\% & Power2-exp2 & 0.3445 & 0.1268\% & Power2-exp2 & 0.5781 & 0.6270\% & Power2-exp2 & 0.3324 & 2.2989\% \\ CS-combined & 1.3301 & 0.3155\% & CS-combined & 0.2758 & 0.1007\% & CS-combined & 0.2113 & 0.2270\% & CS-combined & 0.2702 & 1.9000\% \\ \hline \multicolumn{1}{c}{**Training processing**} & & & & & & & & & & \\ \hline \multicolumn{1}{c}{**Low Income Group**} & \multicolumn{6}{c}{**Lower-Middle Income Group**} & \multicolumn{6}{c}{**Upper-Middle Income Group**} & \multicolumn{6}{c}{**High Income Group**} \\ \hline
**Model** & **RMSE** & **MAPE** & **Model** & **RMSE** & **MAPE** & **Model** & **RMSE** & **MAPE** & **Model** & **RMSE** & **MAPE** \\ \hline Ploy2 & 1.3914 & 0.2877\% & Poly2 & 1.7279 & 0.5545\% & Poly2 & 0.8165 & 0.6009\% & Poly2 & 0.5688 & 2.7638\% \\ Sin2 & 1.5892 & 0.3397\% & Sin1 & 1.6366 & 0.5284\% & Sin1 & 0.7662 & 0.5456\% & Sin2 & 0.4895 & 2.2767\% \\ Reci-ploy2 & 2.6287 & 0.6260\% & Reci-ploy2 & 1.0975 & 0.5389\% & Reci-ploy2 & 0.6106 & 0.4378\% & Reci-ploy2 & 0.5749 & 2.7788\% \\ Reci-exp2 & 1.4388 & 0.3297\% & Reci-exp2 & 0.8591 & 0.2454\% & Reci-exp2 & 0.6374 & 0.4783\% & Reci-exp2 & 0.5570 & 2.7130\% \\ Power2-ploy2 & 1.7908 & 0.3871\% & Power2-ploy2 & 1.4388 & 0.4657\% & Power2-ploy2 & 0.6763 & 0.4440\% & Power2-ploy2 & 0.5713 & 2.7738\% \\ Power2-exp2 & 1.7537 & 0.4302\% & Power2-exp2 & 0.6922 & 0.1823\% & Power2-exp2 & 0.6323 & 0.4855\% & Power2-exp2 & 0.5595 & 2.7401\% \\ CS-combined & 1.0503 & 0.2186\% & CS-combined & 0.3556 & 0.1028\% & CS-combined & 0.6307 & 0.4883\% & CS-combined & 0.4875 & 2.1993\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Root mean square error (RMSE) and mean absolute percentage error (MAPE) values of forecasting models.