Francesco Chiodo

Comparing Mpox epidemic controls through simulations and explainable graph convolution networks: A case study in the Republic of the Congo and Nigeria

The study supports the potential usefulness of combining contact-based models with explainable graph neural networks for scenario-based epidemic analysis and suggests that the k-GCN model captures relevant temporal and structural dependencies in the simulated graph-organized data.