Abstract
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
Original language | English (US) |
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Pages (from-to) | 93-103 |
Number of pages | 11 |
Journal | IEEE Transactions on Computational Social Systems |
Volume | 4 |
Issue number | 3 |
DOIs | |
State | Published - Jul 20 2017 |