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Network epidemic simulation holds the promise of enabling fine-grainedunderstanding of epidemic behavior, beyond that which is possible withcoarse-grained compartmental models. Key inputs to these epidemic simulationsare the networks themselves. However, empirical measurements and samples ofrealistic interaction networks typically display properties that arechallenging to capture with popular synthetic models of networks. Our empiricalresults show that epidemic spread behavior is very sensitive to a subtle butubiquitous form of multi-scale local structure that is not present in commonbaseline models, including (but not limited to) uniform random graph models(Erdos-Renyi), random configuration models (Chung-Lu), etc. Such structure isnot necessary to reproduce very simple network statistics, such as degreedistributions or triangle closing probabilities. However, we show that thismulti-scale local structure impacts, critically, the behavior of more complexnetwork properties, in particular the effect of interventions such asquarantining; and it enables epidemic spread to be halted in realisticinteraction networks, even when it cannot be halted in simple synthetic models.Key insights from our analysis include how epidemics on networks withwidespread multi-scale local structure are easier to mitigate, as well ascharacterizing which nodes are ultimately not likely to be infected. Wedemonstrate that this structure results from more than just local trianglestructure in the network, and we illustrate processes based on homophily orsocial influence and random walks that suggest how this multi-scale localstructure arises.

Multi-scale Local Network Structure Critically Impacts Epidemic Spread and Interventions