# Data-Driven Forecasting of Acute and Chronic Hepatitis B in Ukraine with Recurrent Neural Networks
## 5 Discussion
Second, this computational frugality enables rapid retraining whenever new registry records are uploaded, a capability that is indispensable when conflict-related shocks periodically disrupt surveillance. Finally, the architecture maintains a cumulative mean absolute percentage error below one per cent despite the underlying data instability. This accuracy level equals or surpasses results reported in studies based on uninterrupted, multi-decade time series. The model's 400-case error margin over a year horizon lies well within the safety stock held by Ukraine's central medical warehouse, suggesting immediate operational utility. Several analytic insights emerge from the residual structure. First, peak underprediction in the acute series coincides temporally with documented back-entry of withheld case forms, indicating that the network rightly interprets these bursts as exogenous shocks rather than endogenous contagion waves. Second, chronic series overruns late in 2024 track renewed displacement from frontline regions, reinforcing evidence that migration distorts denominator data and inflates apparent prevalence. Similar migration-inducedbiases were noted in hepatitis C care-cascade studies conducted after the invasion and must be accounted for before applying the forecasts to sub-national planning [39]. Third, seasonality appears damped in Ukraine relative to East Asian settings, perhaps reflecting colder climates and lower perinatal transmission [40; 41]. The RNN captures a faint spring peak in acute notifications that aligns with school-entry screening campaigns. These findings carry methodological and policy implications. Methodologically, they show that missing value imputation is not a prerequisite for obtaining reliable cumulative forecasts when the objective is medium-range resource allocation, min-max scaling, and frequent retraining suffice. For tactical outbreak detection, however, covariate-rich hybrids are advisable. Mobility streams, energy grid telemetry, and social media sentiment indices already collected by humanitarian agencies could be concatenated with lagged case counts through attention layers or graph convolution blocks to sharpen one-month-ahead alerts. Recent work on hybrid SEIR-deep neural networks reports up to 30% error reduction after adding such exogenous signals [42]. Policy-wise, the study underscores the feasibility of maintaining evidence-based procurement and vaccination planning even when war erodes classical surveillance. It also suggests that Ukraine's 2030 HBV-elimination trajectory remains within reach, provided that cumulative forecasts stay inside the 1% uncertainty band and that antiviral delivery lapses are minimised. These targets can now be monitored quantitatively rather than by expert judgement alone. Table 6 presents the predictive accuracy of our two-layer LSTM against state-of-the-art hepatitis B forecasting studies. Table 6 presents a comparative summary of forecasting performance between the proposed two-layer LSTM model and a selection of recent hepatitis B prediction studies. These studies represent a range of modelling approaches, including traditional statistical methods such as ARIMA, machine learning models such as neural network autoregression (NNAR), and Bayesian structured time series (BSTS) models. Although several of these models were trained on longer historical series (up to 180 monthly observations in the Xiamen dataset), the proposed model, trained on only 72 observations, demonstrates comparable or superior predictive accuracy. In particular, the proposed LSTM achieves RMSE of 20.3 and 8.7 cases for acute and chronic monthly incidence, respectively, alongside MAPE values of 13.78% and 17.12%. When aggregated to the cumulative scale, directly relevant for annual vaccine and antiviral procurement, the model maintains a MAPE below 1% for both clinical forms, with