# Data-Driven Forecasting of Acute and Chronic Hepatitis B in Ukraine with Recurrent Neural Networks
###### Abstract  
Reliable short-term forecasts of hepatitis B incidence are indispensable for sizing national vaccine and antiviral procurement. However, predictive modelling is complicated when surveillance streams experience reporting delays and episodic under-reporting, as has occurred in Ukraine since 2022. We address this challenge by training a deliberately compact two-layer long short-term memory (LSTM) network on 72 monthly observations (January 2018-December 2023) drawn from the Public Health Center electronic registry and evaluating performance on a strictly held-out 12-month horizon (January-December 2024). Grid-search optimisation selected a 12-month sliding input window, 64 hidden units per layer, 0.20 dropout, the Adam optimiser, and early stopping. Walk-forward validation showed that the network attained mean squared errors of 411 for acute infection and 76 for chronic infection on the monthly series. When forecasts were aggregated to the cumulative scale, the mean absolute percentage error remained below 1%. This study presents the first peer-reviewed hepatitis B forecasts calibrated on Ukraine's registry during a period of pronounced reporting instability, demonstrating that robust accuracy is attainable without missing-value imputation. update estimated 254 million people living with chronic HBV in 2022 and 1.2 million new infections annually [7]. HBV-related complications caused -820,000 deaths in 2019, surpassing tuberculosis and rivalling HIV mortality trajectories [8]. Progress toward the WHO's 2030 elimination targets has stalled; in 2022, only 13% of people with chronic HBV were diagnosed, and a mere 3% received antiviral therapy [7]. The burden is unevenly distributed; the African and Western Pacific Regions account for >75% of prevalent cases, yet health system coverage of birth dose vaccination and nucleotide therapy remains sub-optimal [9]. HBV is also a pressing public health concern for Ukraine. A joint European Centre for Disease Prevention and Control (ECDC) and WHO assessment placed the adult HBsAg prevalence at 1% in 2020, categorising the country as having low-to-moderate endemicity [10]. Vaccination coverage, however, lags behind elimination benchmarks; infant three-dose coverage was 80.9% in 2020, and catch-up immunisation has been inconsistent. Coverage of opioid agonist therapy, critical for preventing parenteral transmission among people who inject drugs (PWID), remains below 6% [10]. Disruptions linked to the ongoing Russian invasion--population displacement, strained laboratory capacity, and interruptions in routine immunisation--risk reversing recent gains, underscoring the need for data-driven surveillance and forward-looking resource allocation. Data-driven public health leverages digital epidemiology, routine health information systems, and large-scale analytics to transform granular data into actionable insights, enabling a shift from reactive to proactive disease control [11]. For HBV, electronic case-based surveillance, laboratory information management systems, and geospatial vaccination dashboards can identify micro-regions of low coverage and guide targeted birth dose campaigns [12]. Yet translating raw data into policy requires robust analytic frameworks capable of correcting reporting delays, assimilating heterogeneous data streams, and quantifying forecast uncertainty. Simulation modelling is a cornerstone of modern infectious disease forecasting. Compartmental models (e.g., Susceptible-Exposed-Infectious-Recovered frameworks), agent-based simulations, and increasingly, deep-learning architectures can estimate unobserved transmission parameters, test intervention scenarios, and generate short-term incidence forecasts [13]. A recent review of post-COVID-19 research highlights how big data-enabled simulation combines mobility, social media, and electronic health record data to anticipate epidemic waves [14].