TY - GEN
T1 - Stability analysis and control of virus spread over time-varying networks
AU - Pare, Philip E.
AU - Beck, Carolyn L.
AU - Nedic, Angelia
N1 - Funding Information:
This material is based on research partially sponsored by the National Science Foundation, grants CCF 11-11342, DMS 13-12907, and ECCS 15-09302. All material in this paper represents the position of the authors and not necessarily that of NSF
Funding Information:
* Philip E. Paré, Carolyn L. Beck, and Angelia Angelia Nedić are with the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign and can be reached at philip.e.pare@gmail.com, clbeck50@gmail.com, and angelia@illinois.edu, respectively. This material is based on research partially sponsored by the National Science Foundation, grants CCF 11-11342, DMS 13-12907, and ECCS 15-09302. All material in this paper represents the position of the authors and not necessarily that of NSF.
Publisher Copyright:
© 2015 IEEE.
PY - 2015/2/8
Y1 - 2015/2/8
N2 - Virus models are used commonly for modeling and analysis of biological networks, computer networks, and human contact networks. The dynamic modeling of such systems in prior work has mainly been focused on networks with static graph structures, which we posit are unrealistic and/or oversimplified for the purpose of understanding and analyzing disease propagation of viruses. In this paper, we consider network models with dynamic graph structures, and investigate the propagation and inhibition of diseases in these systems. A stability analysis of the model we consider is performed, examining the disease free equilibrium conditions. Quarantine is proposed as one control technique. Various network simulations are presented and a number of conjectures are given based on these simulations.
AB - Virus models are used commonly for modeling and analysis of biological networks, computer networks, and human contact networks. The dynamic modeling of such systems in prior work has mainly been focused on networks with static graph structures, which we posit are unrealistic and/or oversimplified for the purpose of understanding and analyzing disease propagation of viruses. In this paper, we consider network models with dynamic graph structures, and investigate the propagation and inhibition of diseases in these systems. A stability analysis of the model we consider is performed, examining the disease free equilibrium conditions. Quarantine is proposed as one control technique. Various network simulations are presented and a number of conjectures are given based on these simulations.
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U2 - 10.1109/CDC.2015.7402769
DO - 10.1109/CDC.2015.7402769
M3 - Conference contribution
AN - SCOPUS:84962004076
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 3554
EP - 3559
BT - 54rd IEEE Conference on Decision and Control,CDC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 54th IEEE Conference on Decision and Control, CDC 2015
Y2 - 15 December 2015 through 18 December 2015
ER -