Accurately modelling diffusion dynamics in complex networks is essential for improving medical outcomes, guiding pandemic preparedness, and optimizing resource allocation in public health. However, existing approaches often face a trade-off between predictive performance and model interpretability, limiting their utility for clinical decision-making and strategic planning. This study presents a modular computational methodology that integrates classical compartmental models with graph neural networks (GNNs) and explainable artificial intelligence (XAI) to simulate, analyse, and interpret the spread of contagion across heterogeneous network topologies. The approach captures both structural and temporal dimensions of diffusion processes, enabling granular insights into transmission pathways. Simulations are applied to critical public health scenarios, including the identification of super-spreaders and the assessment of targeted containment strategies. By combining mechanistic models with data-driven learning and explainability techniques, the methodology supports outcome forecasting, scenario comparison, and the interpretation of network-based risk factors. Results demonstrate the ability to predict diffusion trajectories with high accuracy while preserving transparency in decision-relevant variables. The approach is intended as a generalizable tool to support medical modelling and simulation with applications ranging from epidemic control to personalized risk assessment and cost-effective intervention planning.
Branda et al. (Fri,) studied this question.