Presentation (CTI)

OBJECTIVES: Seasonal influenza causes substantial morbidity, mortality, and economic burden. Evaluation of vaccination programmes and resource allocation depends on quantitative models, yet many are calibrated to specific settings and may not be readily accessible or adaptable. The study aimed to develop a transparent, comprehensive, and user-friendly platform integrating epidemiological and economic analyses via an interactive web application.
METHODS: The framework combines a deterministic SEIR (Susceptible-Exposed-Infected-Recovered) dynamic transmission model (DTM) with a decision tree health economic model (HEM) within an interactive Shiny application. The SEIR model is a globally recognised best‑practice framework for infectious diseases, providing a biologically plausible structure and mathematically tractable representation of transmission dynamics, with flexibility to incorporate age structure, multiple strains, vaccination, and immunity propagation. The DTM, aligned with POLYMOD contact matrices, estimates infection rates and vaccination effects. The HEM translates epidemiological outputs into clinical and economic outcomes (costs, quality-adjusted life years), cost-effectiveness, and budget impact. The platform adheres to best practice modelling standards, with structured validation (face validity, internal verification, external comparison) and was implemented as an R package with documentation, automated testing, and validation checks.
RESULTS: Configured for Romania, Spain, and Austria, the platform supports rapid scenario analysis of vaccination strategies across age groups, vaccine types, and coverage levels over an influenza season. It enables rapid configuration across countries and scenarios without code changes, and is accessible via internet browser without requiring local R installation.
CONCLUSIONS: This modular, user-friendly framework integrates transmission modelling and health economic evaluation to support evidence-based influenza vaccination policy and is readily adaptable to new settings, influenza strains, and policy questions. Future work will aim to incorporate functionality for multi-season modelling, uncertainty analysis and user-accessible calibration tools, enabling alignment of model outputs with seasonal influenza incidence data from the World Health Organization FluNet.