System Dynamics of a Refined Epidemic Model for Infection Propagation over Complex Networks

Aresh Dadlani, Muthukrishnan Senthil Kumar, Suvi Murugan, Kiseon Kim

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


The ability to predict future epidemic threats, both in real and digital worlds, and to develop effective containment strategies heavily leans on the availability of reliable infection spreading models. The stochastic behavior of such processes makes them even more demanding to scrutinize over structured networks. This paper concerns the dynamics of a new susceptible-infected-susceptible (SIS) epidemic model incorporated with multistage infection (infection delay) and an infective medium (propagation vector) over complex networks. In particular, we investigate the critical epidemic thresholds and the infection spreading pattern using mean-field approximation (MFA) and results obtained through extensive numerical simulations. We further generalize the model for any arbitrary number of infective media to mimic existing scenarios in biological and social networks. Our analysis and simulation results reveal that the inclusion of multiple infective medium and multiple stages of infection significantly alleviates the epidemic threshold and, thus, accelerates the process of infection spreading in the population.

Original languageEnglish
Pages (from-to)1316-1325
Number of pages10
JournalIEEE Systems Journal
Issue number4
Publication statusPublished - Dec 1 2016
Externally publishedYes


  • Complex networks
  • epidemic threshold
  • infection delay
  • infective medium
  • mean-field approximation (MFA) propagation vector
  • susceptible-infected-susceptible (SIS) model

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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