A Network-based Analytics Framework for High-Resolution Agent-based Epidemic Simulation Ensembles

High-resolution network-based contagion models are being increasingly used to study complex disease scenarios. Due to network-induced heterogeneity and sophisticated disease and intervention models, even simple simulation exercises can lead to large volumes of complex simulation outcomes. New approaches are required to analyze them. Simulations of such network spread processes can be viewed as attributed temporal graphs. We describe a network-based analytics framework that enables a user to leverage this graphical viewpoint and apply graph mining methods to perform fine-grained analysis of the simulation outcomes and the underlying network. The framework is based on a microservices-oriented architecture, and is designed to be general, adaptable, and scalable. We demonstrate its utility through a case study motivated by the COVID-19 pandemic involving the spread of two variants on a large realistic population network with multiple interventions. We study the transmissions within and between age-groups, importance of non-essential interactions, and efficacy of interventions.