# Data-Driven Forecasting of Acute and Chronic Hepatitis B in Ukraine with Recurrent Neural Networks
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Data** & **Training** & **Horizon** & **RMSE** & **MAPE** \\ \hline LSTM (proposed) & Acute monthly (Ukraine) & Jan 2018–Dec 2023 & Jan 2024–Dec 2024 & 20.28 & 13.78 \\ LSTM (proposed) & Chronic monthly (Ukraine) & Jan 2018–Dec 2023 & Jan 2024–Dec 2024 & 8.73 & 17.12 \\ LSTM (proposed) & Acute cumulative (Ukraine) & Jan 2018–Dec 2023 & Jan 2024–Dec 2024 & 57.98 & 0.85 \\ LSTM (proposed) & Chronic cumulative (Ukraine) & Jan 2018–Dec 2023 & Jan 2024–Dec 2024 & 62.71 & 0.82 \\ ARIMA [43] & Cases monthly (Henan) & Jan 2013–Sep 2021 & Oct 2021–Sep 2022 & 961.12 & 14.39 \\ BSTS [43] & Cases monthly (Henan) & Jan 2013–Sep 2021 & Oct 2021–Sep 2022 & 680.05 & 10.03 \\ NNAR [31] & Cases monthly (Xiamen) & Jan 2004–Dec 2019 & Jan 2020–Dec 2022 & 121.87 & - \\ ETS [31] & Cases monthly (Xiamen) & Jan 2004–Dec 2019 & Jan 2020–Dec 2022 & 158.05 & - \\ SARIMA [31] & Cases monthly (Xiamen) & Jan 2004–Dec 2019 & Jan 2020–Dec 2022 & 141.30 & - \\ SARIMA-ETS-STL- & Cases monthly (Xiamen) & Jan 2004–Dec 2019 & Jan 2020–Dec 2022 & 130.15 & - \\ NNAR [31] & Cases monthly (Xiamen) & Jan 2004–Dec 2019 & Jan 2020–Dec 2022 & 131.51 & - \\ BSTS [31] & Cases monthly (Xiamen) & Jan 2004–Dec 2019 & Jan 2020–Dec 2022 & 131.51 & - \\ Prophet [31] & Cases monthly (Xiamen) & Jan 2004–Dec 2019 & Jan 2020–Dec 2022 & 178.22 & - \\ ARIMA [44] & Cases monthly (China) & Mar 2010–May 2017 & Jun 2017–Oct 2017 & 3849.72 & 3.39 \\ GM (1,1) [44] & Cases monthly (China) & Aug 2016–May 2017 & Jun 2017–Oct 2017 & 16991.99 & 15.69 \\ SARIMA [23] & Cases monthly (China) & Jan 2013–Dec 2020 & Jan 2021–Dec 2021 & 7005.57 & 5.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison of hepatitis B models’ performance. RMSE values of 58.0 (acute) and 62.7 (chronic). These results compare favourably to the Henan BSTS model, which reports a monthly MAPE of 10.03% and RMSE of 680.05 and significantly outperforms classical ARIMA implementations, which exhibit higher error magnitudes across all evaluated contexts. Several methodological distinctions contribute to the improved performance. First, the proposed model explicitly separates acute and chronic forms of hepatitis B, allowing it to capture distinct temporal patterns associated with each disease stage. In contrast, most prior work aggregates all cases into a single time series, potentially obscuring important epidemiological signals. Second, the model forecasts absolute case counts rather than incidence rates. This design choice reflects current data limitations in Ukraine, where post-2022 disruptions have affected the availability of reliable population statistics. By avoiding incidence-based scaling, the model produces forecasts that remain directly actionable for public health planning. Finally, the compact size of the network enables rapid retraining on standard hardware without the need for GPU acceleration or cloud infrastructure, in contrast to more computationally intensive methods such as BSTS or ensemble pipelines. Overall, the comparative results highlight the value of a lightweight, domain-adapted LSTM architecture in resource-constrained forecasting environments. Despite a shorter training horizon and limited input features, the model achieves high accuracy across both monthly and cumulative prediction tasks, offering a practical and scalable solution for hepatitis B burden estimation in settings where data continuity and computational capacity may be limited. The research has several limitations. The six-year training horizon encloses only five complete seasonal cycles and omits any pre-2018 secular trend. Once digitised, incorporating earlier Ministry of Health statistics could improve long-period learning. Without an embedded transmission kernel, the model cannot evaluate counterfactual scenarios such as scaling adult booster programmes or introducing birth dose vaccination in displaced populations. However, such data is not available.