Epidemic centrality - is there an underestimated epidemic impact of network peripheral nodes?
Mile Sikic, Alen Lancic, Nino Antulov-Fantulin, Hrvoje Stefancic

TL;DR
This paper introduces epidemic centrality, an average measure over disease spreading regimes, revealing that peripheral nodes can have an epidemic impact comparable to central nodes, challenging traditional focus on network hubs.
Contribution
The study proposes epidemic centrality as a new metric for ranking nodes by epidemic impact, accounting for different dynamical regimes in disease spreading.
Findings
Epidemic centrality varies less than structural properties like degree.
Peripheral nodes can have epidemic impact similar to central nodes.
Epidemic centrality informs resource allocation strategies.
Abstract
In the study of disease spreading on empirical complex networks in SIR model, initially infected nodes can be ranked according to some measure of their epidemic impact. The highest ranked nodes, also referred to as "superspreaders", are associated to dominant epidemic risks and therefore deserve special attention. In simulations on studied empirical complex networks, it is shown that the ranking depends on the dynamical regime of the disease spreading. A possible mechanism leading to this dependence is illustrated in an analytically tractable example. In systems where the allocation of resources to counter disease spreading to individual nodes is based on their ranking, the dynamical regime of disease spreading is frequently not known before the outbreak of the disease. Therefore, we introduce a quantity called epidemic centrality as an average over all relevant regimes of disease…
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.
