# Data-Driven Forecasting of Acute and Chronic Hepatitis B in Ukraine with Recurrent Neural Networks
## 6 Conclusions  
This study demonstrates that a mid-depth RNN, trained exclusively on routinely collected surveillance data, can forecast cumulative acute and chronic hepatitis B in Ukraine with a MAPE below 1% despite profound wartime disruption of the reporting system. The model's dual-scale evaluation shows that monthly incidence, although harder to predict, remains within an accuracy envelope comparable to pre-war studies from East Asia once data shocks are smoothed.  
Scintifically, the work supplies the first conflict-calibrated evidence that sequence-learning architectures retain predictive value when standard notification pipelines fracture, a setting in which classical compartmental or hybrid grey models had not been tested. This study also contributes a transparent benchmark against which future mechanistic or covariate-rich extensions can be judged by documenting parameter counts, learning curves, and auto-regressive generation in detail.  
The practical novelty lies in translating those forecasts into procurement margins that match the safety stocks held by the national antiviral programme. This offers a quantitative alternative to expert judgement for vaccine ordering under uncertainty.  
Future research should embed a differentiable epidemiological core to enable scenario analysis of booster vaccination and birth dose uptake; incorporate conflict-responsive covariates such as population movement, electricity outages, and clinic reopening schedules; and adopt Bayesian or ensemble techniques to propagate input uncertainty through actionable prediction intervals. Such advances would strengthen the scientific foundation and operational utility of HBV forecasting in Ukraine and other settings where health surveillance must function amid complex humanitarian emergencies.  
Conceptualization, M.B. and D.C.; methodology, M.B. and D.C.; software, M.B.; validation, M.B., S.Y., and D.C.; formal analysis, M.B., S.Y., and D.C.; investigation, M.B., S.Y., and D.C.; resources, S.Y. and D.C.; data curation, M.B. and D.C.; writing--original draft preparation, M.B. and D.C.; writing--review and editing, S.Y.; visualisation, M.B.; supervision, D.C.; project administration, D.C.; funding acquisition, S.Y. and D.C. All authors have read and agreed to the published version of the manuscript.  
This study was funded by National Science Centre of Poland (project No. 2023/05/Y/ST6/00263), Office of Naval Research (ONR), US National Academy of Science (US NAS) through Science & Technology Center in Ukraine (STCU) Project No. 7136, within the joint IMPRESS-U initiative entitled \"Modeling and Forecasting of Infection Spread in War and Post War Settings Using Epidemiological, Behavioral and Genomic Surveillance Data\".  
Not applicable.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} HBV & Hepatitis B Virus \\ HBsAgAg & Hepatitis B Surface Antigen \\ WHO & World Health Organization \\ ECDC & European Centre for Disease Prevention and Control \\ PWID & People Who Inject Drugs \\ SEIR & Susceptible--Exposed--Infectious--Recovered \\ COVID-19 & Coronavirus Disease 2019 \\ SARIMA & Seasonal Autoregressive Integrated Moving Average \\ SEIRI & Susceptible--Exposed--Infectious--Recovered--Infectious \\ BPNN & Back Propagation Neural Network \\ ARIMA & Autoregressive Integrated Moving Average \\ MAPE & Mean Absolute Percentage Error \\ cccDNA & Covalently Closed Circular Deoxyribonucleic Acid \\ LSTM & Long Short-Term Memory \\ A-LSTM & Attention-Enhanced Long Short-Term Memory \\ RMSE & Root Mean Square Error \\ CDA & Center for Disease Analysis \\ SEICR & Susceptible--Exposed--Infectious--Chronic--Recovered \\ \end{tabular}