Contagion-preserving compression for multiscale epidemic modeling
4 affiliations
- 1Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China.
- 2International Academic Center of Complex Systems, Beijing Normal University, Zhuhai 519087, China.
- 3School of Systems Science, Beijing Normal University, Beijing 100875, China.
- 4Department of Physics, Bar-Ilan University, Ramat-Gan 52900, Israel.
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.
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BibTeX
@article{xue2026contagionpreserving,
title = {{Contagion-preserving compression for multiscale epidemic modeling}},
author = {Leyang Xue and Zengru Di and An Zeng},
journal = {Science Advances},
year = {2026},
month = oct,
doi = {10.1126/sciadv.adz4710},
url = {https://www.science.org/doi/10.1126/sciadv.adz4710},
note = {CC BY},
}
RIS
TY - JOUR
TI - Contagion-preserving compression for multiscale epidemic modeling
AU - Leyang Xue
AU - Zengru Di
AU - An Zeng
PY - 2026
DA - 2026/10/09
JO - Science Advances
DO - 10.1126/sciadv.adz4710
UR - https://www.science.org/doi/10.1126/sciadv.adz4710
AB - 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.
SN - 2375-2548
ER -