Mesoscale community organization governs epidemic onset and spread in metapopulations

Abstract

Understanding how internal community structure shapes the course of epidemics remains a fundamental challenge in modeling real-world populations. Standard metapopulation models often assume uniform mixing within communities, overlooking how internal heterogeneity affects global outcomes. Here, we develop a general framework for epidemic spreading in hierarchically structured metapopulations, where individuals interact locally within dense communities and move across a broader network. We show that transmission dynamics are governed by the mesoscale organization of these communities: highly connected groups accelerate and amplify outbreaks, while less connected ones dampen spread. Through a combination of mean-field theory, spectral analysis, and stability methods, we reveal a direct link between internal connectivity and the emergence of uneven, spatially structured epidemic patterns. We further validate these predictions using real-world data, where social contact networks capture the local scale of transmission while spatial transport networks govern global connectivity, confirming the robustness of our framework across scales. These results demonstrate how community structure fundamentally governs the shape of epidemics in complex, networked populations, offering new insights into vulnerability, containment, and epidemic control.