The effect of awareness on networked SIS epidemics

Conference Paper

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

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program

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Print Publication Date


We study an SIS epidemic model over an arbitrary fixed network topology where the n agents, or nodes of the network, have partial information about the epidemic state. The agents react by distancing themselves from their neighbors when they believe the epidemic is currently prevalent. An agent's awareness is weighted from three sources of information: the fraction of infected neighbors in their contact network, 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 2-state Markov chains. Through a coupling technique, we establish monotonicity properties between the benchmark and awareness models. Particularly, we show that the expectation of any increasing random variable on the space of sample paths, e.g. eradication time or total infections, is lower for the awareness model. In addition, we give a characterization for this difference of expectations in terms of the coupling distribution. In simulations, we evaluate how different sources of information affect the spread of an epidemic.

Paarporn K, Eksin C, Weitz JS, Shamma JS (2016) The effect of awareness on networked SIS epidemics. 2016 IEEE 55th Conference on Decision and Control (CDC). Available:

This work is supported by ARO grant #W911NF-14-1-0402 (to J.S.W), and supported in part by KAUST.

Institute of Electrical and Electronics Engineers (IEEE)

2016 IEEE 55th Conference on Decision and Control (CDC)

Conference/Event Name
55th IEEE Conference on Decision and Control, CDC 2016


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