TY - GEN
T1 - On a continuous-time multi-group bi-virus model with human awareness
AU - Liu, Ji
AU - Paré, Philip E.
AU - Nedić, Angelia
AU - Beck, Carolyn L.
AU - Başar, Tamer
N1 - Ji Liu is with the Department of Electrical and Computer Engineering at Stony Brook University ([email protected]). Philip E. Paré, Carolyn L. Beck, and Tamer Bas¸ar are with the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign ({philpare, beck3, basar1}@illinois.edu). Angelia Nedić is with the School of Electrical, Computer and Energy Engineering at Arizona State University ([email protected]). The work of Liu and Bas¸ar was supported in part by Office of Naval Research (ONR) MURI Grant N00014-16-1-2710, and in part by NSF under grant CCF 11-11342. The work of Paré and Beck was sponsored in part by the National Science Foundation grant CPS 1544953. All material in this paper represents the position of the authors and not necessarily that of NSF.
PY - 2017/6/28
Y1 - 2017/6/28
N2 - This paper studies the effect of human awareness on a distributed continuous-time bi-virus model in which two competing viruses diffuse over a network comprised of multiple groups of individuals. When contacting infected individuals in their own and neighboring groups, individuals may either be infected by one of the two viruses with a virus-dependent infection rate or become alert. Alert individuals may be infected by either virus but with a smaller virus-dependent infection rate, and the alert state also diffuses over the network. Limiting behaviors of the model are studied by analyzing the equilibria of the system and their stability. Both equilibria and their stability are compared with those of the model without human awareness.
AB - This paper studies the effect of human awareness on a distributed continuous-time bi-virus model in which two competing viruses diffuse over a network comprised of multiple groups of individuals. When contacting infected individuals in their own and neighboring groups, individuals may either be infected by one of the two viruses with a virus-dependent infection rate or become alert. Alert individuals may be infected by either virus but with a smaller virus-dependent infection rate, and the alert state also diffuses over the network. Limiting behaviors of the model are studied by analyzing the equilibria of the system and their stability. Both equilibria and their stability are compared with those of the model without human awareness.
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U2 - 10.1109/CDC.2017.8264265
DO - 10.1109/CDC.2017.8264265
M3 - Conference contribution
AN - SCOPUS:85046287718
T3 - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
SP - 4124
EP - 4129
BT - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 56th IEEE Annual Conference on Decision and Control, CDC 2017
Y2 - 12 December 2017 through 15 December 2017
ER -