Epidemic spread over networks with agent awareness and social distancing

Handle URI:
http://hdl.handle.net/10754/621315
Title:
Epidemic spread over networks with agent awareness and social distancing
Authors:
Paarporn, Keith; Eksin, Ceyhun; Weitz, Joshua S.; Shamma, Jeff S. ( 0000-0001-5638-9551 )
Abstract:
We study an SIS epidemic model over an arbitrary connected network topology when the agents receive personalized information about the current epidemic state. The agents utilize their available information to either reduce interactions with their neighbors (social distancing) when they believe the epidemic is currently prevalent or resume normal interactions when they believe there is low risk of becoming infected. The information is a weighted combination of three sources: 1) the average states of nodes in contact neighborhoods 2) the average states of nodes in an information network 3) a global broadcast of the average epidemic state of the network. A 2n-state Markov Chain is first considered to model the disease dynamics with awareness, from which a mean-field discrete-time n-state dynamical system is derived, where each state corresponds to an agent's probability of being infected. The nonlinear model is a lower bound of its linearized version about the origin. Hence, global stability of the origin (the diseasefree equilibrium) in the linear model implies global stability in the nonlinear model. When the origin is not stable, we show the existence of a nontrivial fixed point in the awareness model, which obeys a strict partial order in relation to the nontrivial fixed point of the dynamics without distancing. In simulations, we define two performance metrics to understand the effectiveness agent awareness has in reducing the spread of an epidemic. © 2015 IEEE.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Paarporn K, Eksin C, Weitz JS, Shamma JS (2015) Epidemic spread over networks with agent awareness and social distancing. 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton). Available: http://dx.doi.org/10.1109/ALLERTON.2015.7446985.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)
Conference/Event name:
53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015
Issue Date:
20-Apr-2016
DOI:
10.1109/ALLERTON.2015.7446985
Type:
Conference Paper
Sponsors:
W911NF-14-1-0402, ARO
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorPaarporn, Keithen
dc.contributor.authorEksin, Ceyhunen
dc.contributor.authorWeitz, Joshua S.en
dc.contributor.authorShamma, Jeff S.en
dc.date.accessioned2016-11-03T06:57:31Z-
dc.date.available2016-11-03T06:57:31Z-
dc.date.issued2016-04-20en
dc.identifier.citationPaarporn K, Eksin C, Weitz JS, Shamma JS (2015) Epidemic spread over networks with agent awareness and social distancing. 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton). Available: http://dx.doi.org/10.1109/ALLERTON.2015.7446985.en
dc.identifier.doi10.1109/ALLERTON.2015.7446985en
dc.identifier.urihttp://hdl.handle.net/10754/621315-
dc.description.abstractWe study an SIS epidemic model over an arbitrary connected network topology when the agents receive personalized information about the current epidemic state. The agents utilize their available information to either reduce interactions with their neighbors (social distancing) when they believe the epidemic is currently prevalent or resume normal interactions when they believe there is low risk of becoming infected. The information is a weighted combination of three sources: 1) the average states of nodes in contact neighborhoods 2) the average states of nodes in an information network 3) a global broadcast of the average epidemic state of the network. A 2n-state Markov Chain is first considered to model the disease dynamics with awareness, from which a mean-field discrete-time n-state dynamical system is derived, where each state corresponds to an agent's probability of being infected. The nonlinear model is a lower bound of its linearized version about the origin. Hence, global stability of the origin (the diseasefree equilibrium) in the linear model implies global stability in the nonlinear model. When the origin is not stable, we show the existence of a nontrivial fixed point in the awareness model, which obeys a strict partial order in relation to the nontrivial fixed point of the dynamics without distancing. In simulations, we define two performance metrics to understand the effectiveness agent awareness has in reducing the spread of an epidemic. © 2015 IEEE.en
dc.description.sponsorshipW911NF-14-1-0402, AROen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleEpidemic spread over networks with agent awareness and social distancingen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journal2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)en
dc.conference.date29 September 2015 through 2 October 2015en
dc.conference.name53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015en
dc.contributor.institutionSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United Statesen
dc.contributor.institutionSchool of Biology, Georgia Institute of Technology, Atlanta, United Statesen
dc.contributor.institutionSchool of Physics, Georgia Institute of Technology, Atlanta, GA, United Statesen
kaust.authorShamma, Jeff S.en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.