Contagion-preserving compression for multiscale epidemic modeling.

Authors

Leyang Xue, Zengru Di, An Zeng

Published in

Science advances. Volume 12. Issue 41. Pages eadz4710. Oct 09, 2026. Epub Oct 09, 2026.

Abstract

Understanding how contagion unfolds on large, heterogeneous networks is essential for predicting and controlling spreading processes, yet structural complexity often obscures the mechanisms governing transmission across scales. Here, we show that contagion is naturally organized around dense local structures that become dynamical spreading units once infection saturates within them. Building on this principle, we introduce iterative structural coarse-graining (ISCG), a framework that compresses large networks into interpretable multiscale representations while retaining the contagion dynamics of the original system. In this saturation regime, these representations reproduce macroscopic outbreak sizes, node-level infection risks, and spatiotemporal infection trajectories across scales. Beyond this regime, ISCG enables controlled trade-offs between dynamical fidelity and structural compression. The resulting multiscale representations support mechanism-driven intervention strategies, including influence maximization, immunization, and surveillance, that consistently outperform adaptive centrality-based methods. These results establish a general multiscale framework for representing, understanding, and controlling contagion in networked systems.

PMID:
42853948
Bibliographic data and abstract were imported from PubMed on 10 Oct 2026.

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