Effect of network clustering on mutually cooperative coinfections
Peng-Bi Cui, Francesca Colaiori, and Claudio Castellano

TL;DR
This study investigates how local clustering in contact networks influences the abruptness of cooperative epidemic transitions, revealing that increased clustering tends to smooth out discontinuities and significantly affects the role of influential spreaders.
Contribution
It demonstrates through simulations that local clustering reduces the abruptness of cooperative epidemic transitions and impacts the influence of initial spreaders.
Findings
High cooperativity leads to abrupt epidemic transitions.
Increased clustering diminishes the discontinuity of the transition.
Clustering and cooperativity together strongly influence spreading dynamics.
Abstract
The spread of an infectious disease can be promoted by previous infections with other pathogens. This cooperative effect can give rise to violent outbreaks, reflecting the presence of an abrupt epidemic transition. As for other diffusive dynamics, the topology of the interaction pattern of the host population plays a crucial role. It was conjectured that a discontinuous transition arises when there are relatively few short loops and many long loops in the contact network. Here we focus on the role of local clustering in determining the nature of the transition. We consider two mutually cooperative pathogens diffusing in the same population: an individual already infected with one disease has an increased probability of getting infected by the other. We look at how a disease obeying the susceptible-infected-removed dynamics spreads on contact networks with tunable clustering. Using…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
