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.
ASJC Scopus subject areas
- Artificial Intelligence