An Efficient Framework for Epidemiological Parameter Estimation via Graph Reduction and Graph Neural Networks

We propose an epidemiological parameter estimation framework based on contact networks and graph neural networks (GNNs). Contact network-based epidemiological models allow us to capture heterogeneity and individual-level details more effectively. Parameter estimation involves fitting real-world disease data to mathematical models. Traditionally, several likelihood-based methods that focus on compartment-based simulation models have been widely used to perform parameter estimation. However, these methods suffer from making assumptions such as homogeneous traits among the individuals of the population under consideration, which may cause them to fail in handling the complexity and diversity of real-world data. Our proposed framework estimates epidemiological parameters based on the availability of contact network data and individual-level disease time series data. We use supervised as well as self-supervised GNN architectures to incorporate the contact network information into the model. We also employed graph reduction methods such as sampling and coarsening to study scaling behavior and computational efficiency. We formulated the parameter estimation in two ways to study the predictive behavior better: classification and inference problems. We experimentally confirm improvements over the baselines chosen in this article. We also conducted ablation studies, explainability quantification, and scalability experiments to generate further insights into the GNN models.