Dynamics of epidemic spreading over networks with agent awareness
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Paarporn, Keith
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Abstract
We study an SIS (susceptible-infected-susceptible) model of disease spread over a contact network of n agents. The agents receive personalized information about the epidemic through their social network and a global broadcast of the current infected fraction among the population. They reduce interactions with their neighbors when they believe the epidemic is prevalent. The epidemic dynamics are described by a Markov chain, from which a mean-field approximation (MFA) is derived. We derive a threshold condition above which the epidemic is expected to persist for a long time, and below which it dies out quickly. Through a coupling argument, we also establish stochastic domination properties between the awareness model and the model with no awareness. The effect awareness has on the disease dynamics is studied on various random graph families.
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2016-08-01
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