# Data-Driven Forecasting of Acute and Chronic Hepatitis B in Ukraine with Recurrent Neural Networks
## 2 Current Research Analysis
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\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Task** & **Method** & **Findings** \\ \hline [PERSON] et al. [18] & To construct and analyse a mathematical model that captures the spread of hepatitis B in Makassar and to identify conditions leading to disease elimination or persistence. & The study formulates a nonlinear SEIRI ordinary-differential-equation system, applies graph theory to obtain R\({}_{0}\), conducts local stability of diseases via eigenvalues, and performs scenario simulations with Maple. & The model yields an analytic expression for R\({}_{0}\); simulations show that when R\({}_{0}\) \(<\) 1 the infection declines, whereas modest increases in transmission parameters push R\({}_{0}\) \(>\) 1 and sustain electricity, highlighting the importance of targeted interventions. & The SARIMA–BPNN model outperformed the single-method alternatives and projected a gradual decrease in hepatitis B incidence during 2022, with the largest month-to-month reduction occurring in early spring. \\ \hline [PERSON] et al. [19] & To compile a pedagogical yet critical roadmap of HBV disease-progression models that guides practitioners in selecting, refining and applying mechanistic frameworks for treatment evaluation. & The authors proposed reformulated representative models of increasing biological detail, analysed their dynamics, and packaged into openly accessible R Shiny applications for interactive simulation. & The SAPIMA–BPNN outperformed the single-method alternatives and projected a gradual decrease in hepatitis B incidence during 2022, with the largest month-to-month reduction occurring in early spring. \\ \hline [PERSON] et al. [20] & To build an accurate model for short-term prediction of monthly hepatitis B cases in China. & The study trains and compares an attention-based two-layer LSTM network and a multi-layer BPNN using 2004–2017 surveillance data. & The A-LSTM outperforms BPNN and prior time series benchmarks, reducing prediction error by roughly 50\% and explaining 87\% of the variance in held-out monthly counts. \\ \hline [PERSON] et al. [21] & To quantify the hidden burden of latent HBV carriers in China and evaluate how alternative vaccination strategies affect long-term transmission dynamics. & The authors built a seven-compartment deterministic model calibrated to 19 years of national case data using nonlinear least squares and a genetic algorithm. & Model fitting suggests 449,535 (95\% CI 415,651–483,420) latent carriers and shows that lowering vaccine failure rates and extending adult immunisation can push R\({}_{\text{c}}\) below unity, driving incidence downward. \\ \hline \hline \end{tabular}
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Table 1: The current state of research on hepatitis B simulation. The current literature shows that forecast accuracy and policy relevance depend as much on transparent structural assumptions and robust calibration as on algorithmic sophistication. Machine learning models excel at short-term prediction, whereas mechanistic simulators remain indispensable for exploring counterfactual scenarios. Few studies bridge these strengths in a single tool or address data disruptions typical of conflict-affected contexts such as Ukraine. Moreover, discrepancies between leading vaccination models underscore persistent structural uncertainty, and limited attention to latent carriers, adult booster programmes, and adherence dynamics leaves important questions unanswered. The present study advances by developing recurrent neural networks to forecast acute and chronic hepatitis B in Ukraine under conditions of ongoing disruption.