Transient analysis of a resource-limited recovery policy for epidemics: A retrial queueing approach

Aresh Dadlani; Muthukrishnan Senthil Kumar; Kiseon Kim; Faryad Darabi Sahneh

doi:10.1109/SARNOF.2016.7846752

Transient analysis of a resource-limited recovery policy for epidemics: A retrial queueing approach

Aresh Dadlani, Muthukrishnan Senthil Kumar, Kiseon Kim, Faryad Darabi Sahneh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	37th IEEE Sarnoff Symposium, Sarnoff 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	187-192
Number of pages	6
ISBN (Electronic)	9781509015405
DOIs	https://doi.org/10.1109/SARNOF.2016.7846752
Publication status	Published - Feb 7 2017
Externally published	Yes
Event	37th IEEE Sarnoff Symposium, Sarnoff 2016 - Newark, United States Duration: Sep 19 2016 → Sep 21 2016

Conference

Conference	37th IEEE Sarnoff Symposium, Sarnoff 2016
Country	United States
City	Newark
Period	9/19/16 → 9/21/16

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture

Access to Document

10.1109/SARNOF.2016.7846752

Fingerprint Dive into the research topics of 'Transient analysis of a resource-limited recovery policy for epidemics: A retrial queueing approach'. Together they form a unique fingerprint.

Cite this

Transient analysis of a resource-limited recovery policy for epidemics : A retrial queueing approach. / Dadlani, Aresh; Kumar, Muthukrishnan Senthil; Kim, Kiseon; Sahneh, Faryad Darabi.

37th IEEE Sarnoff Symposium, Sarnoff 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 187-192 7846752.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Dadlani, A, Kumar, MS, Kim, K & Sahneh, FD 2017, Transient analysis of a resource-limited recovery policy for epidemics: A retrial queueing approach. in 37th IEEE Sarnoff Symposium, Sarnoff 2016., 7846752, Institute of Electrical and Electronics Engineers Inc., pp. 187-192, 37th IEEE Sarnoff Symposium, Sarnoff 2016, Newark, United States, 9/19/16. https://doi.org/10.1109/SARNOF.2016.7846752

@inproceedings{34cfc220072a45aeab8aef0a79680d9a,

title = "Transient analysis of a resource-limited recovery policy for epidemics: A retrial queueing approach",

abstract = "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.",

author = "Aresh Dadlani and Kumar, {Muthukrishnan Senthil} and Kiseon Kim and Sahneh, {Faryad Darabi}",

year = "2017",

month = feb,

day = "7",

doi = "10.1109/SARNOF.2016.7846752",

language = "English",

pages = "187--192",

booktitle = "37th IEEE Sarnoff Symposium, Sarnoff 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "37th IEEE Sarnoff Symposium, Sarnoff 2016 ; Conference date: 19-09-2016 Through 21-09-2016",

}

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

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.

UR - http://www.scopus.com/inward/record.url?scp=85015227351&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85015227351&partnerID=8YFLogxK

U2 - 10.1109/SARNOF.2016.7846752

DO - 10.1109/SARNOF.2016.7846752

M3 - Conference contribution

AN - SCOPUS:85015227351

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 -