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| Title |
Transient Analysis of a Resource-limited Recovery Policy for Epidemics: a Retrial Queueing Approach |
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| Conf. Name |
IEEE Sarnoff Symposium |
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| First Author |
Aresh Dadlani |
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| Coauthor |
Muthukrishnan Senthil Kumar, Kiseon Kim, Faryad D. Sahneh |
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| Publication Date |
2016.09.21 |
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| Acceptance Rate |
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Date |
2016-11-04 13:18 |
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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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