The End Time of SIS Epidemics Driven by Random Walks on Edge-Transitive Graphs

Daniel Figueiredo, Giulio Iacobelli, Seva Shneer

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
16 Downloads (Pure)


Network epidemics is a ubiquitous model that can represent different phenomena and finds applications in various domains. Among its various characteristics, a fundamental question concerns the time when an epidemic stops propagating. We investigate this characteristic on a SIS epidemic induced by agents that move according to independent continuous time random walks on a finite graph: agents can either be infected (I) or susceptible (S), and infection occurs when two agents with different epidemic states meet in a node. After a random recovery time, an infected agent returns to state S and can be infected again. The end of epidemic (EoE) denotes the first time where all agents are in state S, since after this moment no further infections can occur and the epidemic stops. For the case of two agents on edge-transitive graphs, we characterize EoE as a function of the network structure by relating the Laplace transform of EoE to the Laplace transform of the meeting time of two random walks. Interestingly, this analysis shows a separation between the effect of network structure and epidemic dynamics. We then study the asymptotic behavior of EoE (asymptotically in the size of the graph) under different parameter scalings, identifying regimes where EoE converges in distribution to a proper random variable or to infinity. We also highlight the impact of different graph structures on EoE, characterizing it under complete graphs, complete bipartite graphs, and rings.
Original languageEnglish
Pages (from-to)651-671
Number of pages21
JournalJournal of Statistical Physics
Issue number3
Early online date22 Apr 2020
Publication statusPublished - May 2020


  • Network epidemics
  • Random walks
  • SIS model

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics


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