Abstract
Epidemiology has long used human interaction patterns to understand spreading dynamics. Recently network scientists have embraced the notion that these pattern are best described using a complex multi-layered system, a network of networks, yielding a stream of literature focused on understanding spreading in such coupled systems. Adding this macro level perspective to disease spreading, focusing on the interaction among systems, has shifted focus away from the role of local (within-system) structure. In this paper, using a multi-level Agent-based model, we highlight the importance of the local structure in determining spreading dynamics in coupled settings. We show that the local dynamics in both the focal and neighboring networks, play a significant role in determining focal dynamics. As both are driven by the local structure this highlights a need for incorporating structural details across all levels for accurate modeling of disease spreading dynamics.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 487-498 |
| Number of pages | 12 |
| Journal | Studies in Computational Intelligence |
| Volume | 693 |
| DOIs | |
| State | Published - 2017 |
Funding
Research reported in this publication was supported by the National Institute On Drug Abuse of the National Institutes of Health under Award Number P30DA027828, the National Science Foundation under Award Number NSFIIS-1441552, and the Northwestern Institute on Complex Systems. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the supporting agencies.
ASJC Scopus subject areas
- Artificial Intelligence
