# Data-Driven Forecasting of Acute and Chronic Hepatitis B in Ukraine with Recurrent Neural Networks
## 4 Results
### Forecasting Results  
Figure 10 shows the forecast for monthly cases of acute hepatitis B over the 16-month hold-out horizon (September 2023-December 2024). The model tracks the overall downward trend but fails to reproduce the sharp local maxima. This smoothing bias results in a test-set MAPE of 13.78% and an MSE of 411.20.  
Figure 11 shows the forecast for monthly cases of chronic hepatitis B over the 12-month hold-out horizon (January 2024-December 2024). The model underestimates the late-autumn peak by roughly 18 cases, giving a MAPE of 17.13% and an MSE of 76.26.  
Error is substantially smaller for the cumulative targets. Figure 12 shows the forecast for monthly cases of acute hepatitis B over the 16-month hold-out horizon (September 2023-December 2024). The acute cumulative forecasts deviate from the empirical trajectory by <1% in absolute terms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measure** & **Epochs** & **MAPE (\%)** & **MSE** \\ \hline Acute (monthly) & 170 & 5.40 & 15.0 \\ Chronic (monthly) & 50 & 13.77 & 411.2 \\ Acute (cumulative) & 312 & 2.86 & 8.4 \\ Chronic (cumulative) & 305 & 2.31 & 6.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Convergence diagnostics (training set).  
Figure 9: Learning trajectory of chronic hepatitis B cumulative cases.  
Figure 13 shows the forecast for monthly cases of chronic hepatitis B over the 12-month hold-out horizon (January 2024-December 2024). The chronic cumulative series is reproduced almost exactly, with the prediction curve lying marginally above the empirical counts throughout the test period. Corresponding MAPEs are 2.9% and 2.1%, respectively.  
Figure 11: Forecast for monthly cases of chronic hepatitis B (January 2024–December 2024).  
Figure 12: Forecast for cumulative cases of acute hepatitis B (September 2023–December 2024).  
Figure 10: Forecast for monthly cases of acute hepatitis B (September 2023–December 2024).  
The model's performance is presented in Table 5.  
Table 5 reveals a clear distinction between level and cumulative accuracy. For the acute monthly series, MAPE fluctuates in the low-to-middle-teen range (14-17%), with the largest absolute departures aligning with the sharp local maxima visible in Figures 6 and 7. In contrast, the cumulative series in Figures 8 and 9 stay within a \(\pm\)1% envelope for the entire 2024 test horizon, yielding sub-1% MAPE. This pattern confirms that the network tracks long-term burden reliably even when short-term shocks momentarily inflate the point-wise error. Given that the highest monthly total exceeds 1000 registrations for chronic infection, this equates to a relative deviation below 8% in the worst months and below 5% on average. Cumulative forecasts, which are policy-relevant for national procurement, remain within a 1% band throughout the 12-month horizon.  
Inspection of the residuals confirms that large absolute errors coincide with episodes of abrupt incidence change, indicating that the current architecture captures smooth temporal dependencies but not sporadic shocks. Aggregation to the cumulative scale attenuates this limitation, dropping test MAPE by an order of magnitude for both disease forms. The results demonstrate that the proposed deep recurrent network is suitable for near-term planning of vaccine procurement and antiviral stock when forecasts are expressed as a cumulative burden. At the same time, additional covariates or regime-switching mechanisms may be required to anticipate short-lived spikes in monthly incidence.