title: Parameterization of Keeling's network generation algorithm.
abstract: Simulation is increasingly being used to examine epidemic behaviour and assess potential management options. The utility of the simulations rely on the ability to replicate those aspects of the social structure that are relevant to epidemic transmission. One approach is to generate networks with desired social properties. Recent research by Keeling and his colleagues has generated simulated networks with a range of properties, and examined the impact of these properties on epidemic processes occurring over the network. However, published work has included only limited analysis of the algorithm itself and the way in which the network properties are related to the algorithm parameters. This paper identifies some relationships between the algorithm parameters and selected network properties (mean degree, degree variation, clustering coefficient and assortativity). Our approach enables users of the algorithm to efficiently generate a network with given properties, thereby allowing realistic social networks to be used as the basis of epidemic simulations. Alternatively, the algorithm could be used to generate social networks with a range of property values, enabling analysis of the impact of these properties on epidemic behaviour.
authors.affiliation: Artificial Life and Adaptive Robotics Laboratory, School of ITEE, Australian Defence Force Academy, Northcott Drive, Canberra 2600, Australia. jbadham@bigpond.net.au
authors.forename: Jennifer
authors.initials: J
authors.lastname: Badham
authors.forename: Hussein
authors.initials: H
authors.lastname: Abbass
authors.forename: Rob
authors.initials: R
authors.lastname: Stocker
mesh_terms: D000465:Algorithms; D004196:Disease Outbreaks / Q000706:statistics & numerical data; D018562:Disease Transmission, Infectious / Q000706:statistics & numerical data; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D006801:Humans; D012944:Social Support