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 | |
| 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 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Hardware and Architecture
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
}
TY - GEN
T1 - Transient analysis of a resource-limited recovery policy for epidemics
T2 - A retrial queueing approach
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
SP - 187
EP - 192
BT - 37th IEEE Sarnoff Symposium, Sarnoff 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -