Influential spreaders for recurrent epidemics on networks

2019/12/31 by Gaël Poux-Médard, Romualdo Pastor‐Satorras, Romualdo Pastor-Satorras +1
Computer Science · Mathematics · Medicine · Physics and Astronomy · #Centrality #Complex Network Analysis Techniques #Computer science #Degree (music) #Epidemic model #Identification (biology) #Mathematical and Theoretical Epidemiology and Ecology Models #Mathematics #Node (physics) #Observable #Opinion Dynamics and Social Influence #Physics #Power law #Quantum mechanics #Statistical physics #Statistics #Supercritical fluid #cond-mat.stat-mech #cs.SI #physics.soc-ph

paper · pdf · doi:10.1103/physrevresearch.2.023332

published as Phys. Rev. Research 2, 023332 (2020) · 10 pages, 8 figures

openalex created_date 2019/12/26 · openalex publication_date 2020/06/12 · arxiv created 2020/07/16 · arxiv updated 2020/07/17 · openalex updated_date 2026/08/05

Abstract

This paper investigates how the duration and extent of a recurrent epidemic started from a given node in a network depend on the position of the node in the structure. An analytical approach provides detailed predictions in good agreement with numerical simulations except close to the epidemic threshold. Degree centrality is the main indicator of which are influential spreaders.

Citations

• Identifying influential spreaders by weighted LeaderRank
• Discrete-time Markov chain approach to contact-based disease spreading in complex networks
• Spectral redemption in clustering sparse networks
• Epidemic thresholds of the susceptible-infected-susceptible model on networks: A comparison of numerical and theoretical results
• Identification of influential spreaders in complex networks
• Network structure and minimum degree