Publication
Modelling epidemics using contact networks provides a significant improvement over classi-cal compartmental models by explicitly incorporating the network of contacts. However, whilenetwork-based models describe disease spread on a given contact structure, their potential forinferring the underlying network from epidemic data remains largely unexplored. In this work,we consider the edge-based compartmental model (EBCM), a compact and analytically tractableframework, and we integrate it within dynamical survival analysis (DSA) to infer key networkproperties along with parameters of the epidemic itself. Despite correlations between struc-tural and epidemic parameters, our framework demonstrates robustness in accurately inferringcontact network properties from synthetic epidemic simulations. Additionally, we apply theframework to real-world outbreaks—the 2001 UK foot-and-mouth disease outbreak and theCOVID-19 epidemic in Seoul— to estimate both disease parameters and network characteris-tics. Our results show that our framework achieves good fits to real-world epidemic data andreliable short-term forecasts. These findings highlight the potential of network-based inferenceapproaches to uncover hidden contact structures, providing insights that can inform the designof targeted interventions and public health strategies.
