Multi-scale local network structure critically impacts epidemic spread and interventions — UC Berkeley

Author summary Epidemic spread is strongly dependent on the patterns and structures inherent in human contact networks. Contact patterns give rise to structural network features that can impact epidemic spread and control in complex ways. A subtle but prominent feature of real-world networks is rich multi-scale structure that manifests as groups of well-connected nodes at multiple size scales. We study this multi-scale structure and show that it can have a large impact on the control of epidemics in real networks. This structure also provides insight on what portions of the network are more resilient to infection while proving more reliable than traditional metrics such as average degree or the dominant eigenvalue. We also give generative models that reliably reproduce this structure as well as distinguish this structure from the closing of triangles and cleanly isolate the impact of this structure on epidemic spreading.