# Data-Driven Forecasting of Acute and Chronic Hepatitis B in Ukraine with Recurrent Neural Networks
## 2 Current Research Analysis
The attention mechanism captures temporal dependencies more effectively than traditional sequence-to-sequence baselines, yielding closer alignment between predicted and observed incidence for most months in the 2018 hold-out period. [PERSON] et al. [21] expanded traditional SEIR-style hepatitis B frameworks by adding a distinct \"potentially infectious\" compartment to represent latent carriers and fitting the resulting seven-class differential equation model to China's 2003-2021 surveillance data. Parameter estimation combined nonlinear least squares with a genetic algorithm, yielding a control reproduction number \(\mathrm{R_{c}}\) = 1.741 and an estimate of roughly 450,000 latent carriers. Sensitivity and scenario analyses showed that vaccination failure, baseline transmissibility, and the latent-to-carrier conversion rate drive \(\mathrm{R_{c}}\). Simulations further indicated that boosting adult vaccination coverage and improving vaccine efficacy would curb incidence. [PERSON] and [PERSON]. [22] formulated and analysed a four-compartment stochastic hepatitis B model that couples saturated, nonlinear transmission with a media coverage function that dampens effective contact as public awareness rises. Environmental variability was introduced through independent Gaussian white noise perturbations assigned to each state variable, and the authors derived threshold conditions for almost-sure extinction and the existence of a unique ergodic stationary distribution. Numerical experiments calibrated to Chinese Center for Disease Control surveillance data (2005-2021) reproduced the historical incidence trajectory and suggested that, under current control intensity, nationwide rates will stabilise at roughly 50-60 cases per 100,000 people. The study by [PERSON] et al. [23] analysed eight years of monthly hepatitis B case counts from 31 provinces in mainland China (2013-2020) and showed that incidence displays a clear annual cycle with troughs in February and peaks in March. After confirming stationarity through logarithmic transformation and seasonal differencing, the authors identified SARIMA(1,0,0)(0,1,1) as the best fitting model, verified that its residuals formed white noise, and demonstrated good in-sample agreement between fitted and observed values. Validation against 2021 data indicated that all forecast points fell within the 95% confidence band. This suggests that a simple seasonal ARIMA specification can provide reliable short-term national surveillance and resource planning projections. [PERSON] et al. [24] conducted a head-to-head comparison of the Imperial HBV model and the CDA Foundation's PRoGReSs model, feeding both with harmonised demographic, prevalence, and vaccination-coverage inputs for Ethiopia, India, Nigeria, and Pakistan to explore how scaling the infant three-dose series and the timely birth dose to 95% coverage would influence hepatitis B trajectories to 2099. Despite similar baseline fits, the models diverged once interventions were varied; PRoGReSs projected larger gains from expanding the infant series because it allowed that schedule to block a substantial share of perinatal infections, whereas Imperial attributed greater benefit to the birth dose, owing to its assumption that perinatal transmission was curtailed only by vaccination in the first 24 h. This structural contrast yielded a ten-fold difference in estimated cases, and deaths averted in some scenarios. It illustrates how policy conclusions can hinge on unobserved mechanisms embedded in simulation frameworks. [PERSON] and [PERSON] [25] developed a stochastic hepatitis B transmission model that coupled continuous environmental noise with discrete regime changes governed by a finite-state Markov chain. The framework partitioned the population into susceptible, acutely infected, chronically infected, and recovered classes; added multiplicative white-noise perturbations to each equation; and allowed key parameters (e.g., transmission, vaccination, mortality) to switch between alternative environmental states.