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dc.contributor.authorPaarporn, Keith
dc.contributor.authorEksin, Ceyhun
dc.contributor.authorWeitz, Joshua S.
dc.contributor.authorShamma, Jeff S.
dc.date.accessioned2017-02-15T08:32:16Z
dc.date.available2017-02-15T08:32:16Z
dc.date.issued2017-01-05
dc.identifier.citationPaarporn 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: http://dx.doi.org/10.1109/CDC.2016.7798394.
dc.identifier.doi10.1109/CDC.2016.7798394
dc.identifier.urihttp://hdl.handle.net/10754/622909
dc.description.abstractWe 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.
dc.description.sponsorshipThis work is supported by ARO grant #W911NF-14-1-0402 (to J.S.W), and supported in part by KAUST.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/7798394/
dc.titleThe effect of awareness on networked SIS epidemics
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journal2016 IEEE 55th Conference on Decision and Control (CDC)
dc.conference.date2016-12-12 to 2016-12-14
dc.conference.name55th IEEE Conference on Decision and Control, CDC 2016
dc.conference.locationLas Vegas, NV, USA
dc.contributor.institutionSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, United States
dc.contributor.institutionSchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States
dc.contributor.institutionSchool of Physics, Georgia Institute of Technology, Atlanta, GA, United States
kaust.personShamma, Jeff S.
dc.date.published-online2017-01-05
dc.date.published-print2016-12


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