Networked SIS Epidemics with Awareness

Keith Paarporn, Ceyhun Eksin, Joshua S. Weitz, Jeff S. Shamma

Research output: Contribution to journalArticlepeer-review


We study a susceptible-infected-susceptible epidemic process over a static contact network where the nodes have partial information about the epidemic state. They react by limiting their interactions with their neighbors when they believe the epidemic is currently prevalent. A node's awareness is weighted by the fraction of infected neighbors in their social network, and a global broadcast of the fraction of infected nodes in the entire network. The dynamics of the benchmark (no awareness) and awareness models are described by discrete-time Markov chains, from which mean-field approximations (MFAs) are derived. The states of the MFA are interpreted as the nodes' probabilities of being infected. We show a sufficient condition for the existence of a 'metastable,' or endemic, state of the awareness model coincides with that of the benchmark model. Furthermore, we use a coupling technique to give a full stochastic comparison analysis between the two chains, which serves as a probabilistic analog to the MFA analysis. In particular, we show that adding awareness reduces the expectation of any epidemic metric on the space of sample paths, e.g., eradication time or total infections. We characterize the reduction in expectations in terms of the coupling distribution. In simulations, we evaluate the effect social distancing has on contact networks from different random graph families (geometric, Erds-Rényi, and scale-free random networks).

Original languageEnglish (US)
Article number7987055
Pages (from-to)93-103
Number of pages11
JournalIEEE Transactions on Computational Social Systems
Issue number3
StatePublished - Sep 2017
Externally publishedYes


  • Epidemics
  • Markov processes
  • networks
  • stochastic processes

ASJC Scopus subject areas

  • Modeling and Simulation
  • Social Sciences (miscellaneous)
  • Human-Computer Interaction


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