# Data-Driven Forecasting of Acute and Chronic Hepatitis B in Ukraine with Recurrent Neural Networks
## 5 Discussion  
The RNN framework presented in current research produced cumulative forecasts for acute and chronic hepatitis B in Ukraine with MAPE below 1%. At the same time, monthly errors remained in the low-to-middle-teen range (13-17%). These metrics are striking against the severe reporting instability that followed the Russian full-scale invasion of Ukraine on 24 February 2022. From that date onward, more than seven hundred docu  
Figure 13: Forecast for cumulative cases of chronic hepatitis B (January 2024–December 2024). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Measure** & **MAPE (\%)** & **MSE** \\ \hline Acute (monthly) & 13.78 & 411.20 \\ Chronic (monthly) & 17.12 & 76.26 \\ Acute (cumulative) & 0.85 & 3362.44 \\ Chronic (cumulative) & 0.82 & 3932.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Metrics (test set). mented attacks damaged or destroyed healthcare facilities, forcing thousands of clinicians to evacuate and interrupt regional laboratory capacity [28]. Routine notification of all communicable diseases, including HBV, fell sharply in the first half of 2022 and recovered only partially once mobile units and volunteer networks restored specimen transport and electronic reporting links [29]. A recent national review of 2018-2023 surveillance corroborates these patterns, showing abrupt troughs for several vaccine-preventable infections during frontline shifts and a corresponding surge in chronic viral hepatitis after catch-up data uploads [30]. The RNN's stronger performance on cumulative counts, therefore, reflects not merely statistical smoothing but its capacity to absorb data shocks without wholesale recalibration, a property crucial for epidemic intelligence under conflict conditions. The present results compare favourably with pre-war studies in stable data environments. In Xiamen City, a multi-model ensemble reached a cumulative MAPE of 2.2% using twenty years of uninterrupted records, a figure only marginally lower than that we achieved despite Ukraine's compressed six-year history and multiple months of underreported or delayed entries [31]. On a monthly scale, our acute series MAPE of 13.8% approaches the 8.7% reported for a SARIMA-BPNN hybrid in Hainan Province, although the latter study did not face large structural breaks [16]. The gap widens for the chronic series, where unexplained jumps in late 2023 inflated error to 17% [32]. Field reports attribute those jumps to targeted screening campaigns that accompanied clinics reopening in de-occupied territories [33]. Such campaign-driven spikes resemble the step changes observed in tuberculosis notifications in Kharkiv and Mykolaiv and are notoriously difficult to capture with autoregressive inputs alone [34]. Modern infectious disease forecasting increasingly augments neural backbones with mechanistic or spatio-temporal constraints [35]. Dynamics-informed neural networks that embed SEIR-type equations within a trainable architecture have improved short-term COVID-19 prediction, and recent extensions incorporating vaccination compartments have delivered similar gains for measles and dengue [36]. In HBV research, hybrid frameworks that fuse grey models, attention-based LSTMs, or physics-guided residual nets to reproduce seasonal patterns and long-memory effects are emerging [37]. Interpretable machine learning instruments are also being rolled out to project downstream outcomes such as hepatocellular carcinoma in chronic carriers [38]. Our purely data-driven network occupies a complementary niche; it demonstrates that when surveillance resources collapse, a mid-depth sequence learner retrained on recent observations can still furnish strategic projections accurate enough to guide national vaccine and antiviral procurement. The selected two-layer LSTM offers several practical advantages. First, with approximately 171,000 trainable parameters, it can be trained in about 40 s on a single laptop class CPU, eliminating any dependency on specialised GPU hardware.