Stochastic oscillations of adaptive networks: application to epidemic modelling

Tim Rogers, William Clifford-Brown, Catherine Mills, Tobias Galla

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Adaptive-network models are typically studied using deterministic differential equations which approximately describe their dynamics. In simulations, however, the discrete nature of the network gives rise to intrinsic noise which can radically alter the system's behaviour. In this article we develop a method to predict the effects of stochasticity in adaptive networks by making use of a pair-based proxy model. The technique is developed in the context of an epidemiological model of a disease spreading over an adaptive network of infectious contact. Our analysis reveals that in this model the structure of the network exhibits stochastic oscillations in response to fluctuations in the disease dynamic.
Original languageEnglish
Article numberP08018
Number of pages11
JournalJournal of Statistical Mechanics-Theory and Experiment
Volume2012
Issue numberAugust
DOIs
Publication statusPublished - Aug 2012

Fingerprint

Oscillation
oscillations
Modeling
Epidemiological Model
Stochasticity
Network Model
Contact
Fluctuations
Differential equation
differential equations
Predict
Model
Simulation
simulation
Context
Network model
Differential equations
Intrinsic

Cite this

Stochastic oscillations of adaptive networks: application to epidemic modelling. / Rogers, Tim; Clifford-Brown, William; Mills, Catherine; Galla, Tobias.

In: Journal of Statistical Mechanics-Theory and Experiment, Vol. 2012, No. August, P08018, 08.2012.

Research output: Contribution to journalArticle

Rogers, Tim ; Clifford-Brown, William ; Mills, Catherine ; Galla, Tobias. / Stochastic oscillations of adaptive networks: application to epidemic modelling. In: Journal of Statistical Mechanics-Theory and Experiment. 2012 ; Vol. 2012, No. August.
@article{12df988f29264c799a4a1ef0553ffb3b,
title = "Stochastic oscillations of adaptive networks: application to epidemic modelling",
abstract = "Adaptive-network models are typically studied using deterministic differential equations which approximately describe their dynamics. In simulations, however, the discrete nature of the network gives rise to intrinsic noise which can radically alter the system's behaviour. In this article we develop a method to predict the effects of stochasticity in adaptive networks by making use of a pair-based proxy model. The technique is developed in the context of an epidemiological model of a disease spreading over an adaptive network of infectious contact. Our analysis reveals that in this model the structure of the network exhibits stochastic oscillations in response to fluctuations in the disease dynamic.",
author = "Tim Rogers and William Clifford-Brown and Catherine Mills and Tobias Galla",
year = "2012",
month = "8",
doi = "10.1088/1742-5468/2012/08/P08018",
language = "English",
volume = "2012",
journal = "Journal of Statistical Mechanics-Theory and Experiment",
issn = "1742-5468",
publisher = "IOP Publishing",
number = "August",

}

TY - JOUR

T1 - Stochastic oscillations of adaptive networks: application to epidemic modelling

AU - Rogers, Tim

AU - Clifford-Brown, William

AU - Mills, Catherine

AU - Galla, Tobias

PY - 2012/8

Y1 - 2012/8

N2 - Adaptive-network models are typically studied using deterministic differential equations which approximately describe their dynamics. In simulations, however, the discrete nature of the network gives rise to intrinsic noise which can radically alter the system's behaviour. In this article we develop a method to predict the effects of stochasticity in adaptive networks by making use of a pair-based proxy model. The technique is developed in the context of an epidemiological model of a disease spreading over an adaptive network of infectious contact. Our analysis reveals that in this model the structure of the network exhibits stochastic oscillations in response to fluctuations in the disease dynamic.

AB - Adaptive-network models are typically studied using deterministic differential equations which approximately describe their dynamics. In simulations, however, the discrete nature of the network gives rise to intrinsic noise which can radically alter the system's behaviour. In this article we develop a method to predict the effects of stochasticity in adaptive networks by making use of a pair-based proxy model. The technique is developed in the context of an epidemiological model of a disease spreading over an adaptive network of infectious contact. Our analysis reveals that in this model the structure of the network exhibits stochastic oscillations in response to fluctuations in the disease dynamic.

UR - http://www.scopus.com/inward/record.url?scp=84866342665&partnerID=8YFLogxK

UR - http://dx.doi.org/10.1088/1742-5468/2012/08/P08018

UR - http://arxiv.org/abs/1206.2768

U2 - 10.1088/1742-5468/2012/08/P08018

DO - 10.1088/1742-5468/2012/08/P08018

M3 - Article

VL - 2012

JO - Journal of Statistical Mechanics-Theory and Experiment

JF - Journal of Statistical Mechanics-Theory and Experiment

SN - 1742-5468

IS - August

M1 - P08018

ER -