Recurrent Physical Contact Network Relevant for Epidemic Spreading

Network-based epidemic models have been instrumental in understanding how contact structures shape infectious disease dynamics, yet widely used network modelling frameworks do not explicitly capture the combination of full (saturated) within-group interactions and constrained between-group links characteristic of many real-world settings. Here, we introduce the Multi-Clique (MC) network model to characterise recurrent physical contact networks, a framework in which individuals are organised into fully connected cliques representing stable contact groups (e.g., households, classrooms, or workplaces), with a limited number of external connections governing inter-group transmission. Using stochastic susceptible-infectious-recovered (SIR) simulations on degree-matched networks, we compare epidemic dynamics on MC networks with those on the Erdos-Renyi model, the configuration-model, and stochastic block networks. Despite having an identical mean degree, MC networks exhibit systematically distinct behaviour, including slower epidemic growth, reduced peak prevalence, increased fade-out probability, and delayed time to peak. These effects arise from rapid within but constrained between clique transmission, creating structural bottlenecks that standard models do not capture. By isolating the role of intergroup connectivity, the model offers a basis for evaluating targeted intervention strategies that reduce between-group mixing while preserving within-group interactions. Our results highlight the importance of explicitly representing real-life, clique-based network structure in epidemic models and suggest that classical degree-matched networks may systematically overestimate epidemic speed and intensity in structured populations.