TY - GEN
T1 - Transient analysis of a resource-limited recovery policy for epidemics
T2 - 37th IEEE Sarnoff Symposium, Sarnoff 2016
AU - Dadlani, Aresh
AU - Kumar, Muthukrishnan Senthil
AU - Kim, Kiseon
AU - Sahneh, Faryad Darabi
N1 - Funding Information:
This research was a part of the project titled "Development of an Automated Fish-counter System and Measurement of Underwater Farming-..sh", funded by the Ministry of Oceans and Fisheries, South Korea.
Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/2/7
Y1 - 2017/2/7
N2 - Knowledge on the dynamics of standard epidemic models and their variants over complex networks has been well-established primarily in the stationary regime, with relatively little light shed on their transient behavior. In this paper, we analyze the transient characteristics of the classical susceptible-infected (SI) process with a recovery policy modeled as a state-dependent retrial queueing system in which arriving infected nodes, upon finding all the limited number of recovery units busy, join a virtual buffer and try persistently for service in order to regain susceptibility. In particular, we formulate the stochastic SI epidemic model with added retrial phenomenon as a finite continuous-time Markov chain (CTMC) and derive the Laplace transforms of the underlying transient state probability distributions and corresponding moments for a closed population of size N driven by homogeneous and heterogeneous contacts. Our numerical results reveal the strong influence of infection heterogeneity and retrial frequency on the transient behavior of the model for various performance measures.
AB - Knowledge on the dynamics of standard epidemic models and their variants over complex networks has been well-established primarily in the stationary regime, with relatively little light shed on their transient behavior. In this paper, we analyze the transient characteristics of the classical susceptible-infected (SI) process with a recovery policy modeled as a state-dependent retrial queueing system in which arriving infected nodes, upon finding all the limited number of recovery units busy, join a virtual buffer and try persistently for service in order to regain susceptibility. In particular, we formulate the stochastic SI epidemic model with added retrial phenomenon as a finite continuous-time Markov chain (CTMC) and derive the Laplace transforms of the underlying transient state probability distributions and corresponding moments for a closed population of size N driven by homogeneous and heterogeneous contacts. Our numerical results reveal the strong influence of infection heterogeneity and retrial frequency on the transient behavior of the model for various performance measures.
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U2 - 10.1109/SARNOF.2016.7846752
DO - 10.1109/SARNOF.2016.7846752
M3 - Conference contribution
AN - SCOPUS:85015227351
T3 - 37th IEEE Sarnoff Symposium, Sarnoff 2016
SP - 187
EP - 192
BT - 37th IEEE Sarnoff Symposium, Sarnoff 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 September 2016 through 21 September 2016
ER -