Lookup NU author(s): Dr Mark Shirley,
Professor Stephen Rushton
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
Individuals in a population susceptible to a disease may be represented as vertices in a network, with the edges that connect vertices representing social and/or spatial contact between individuals. Networks, which explicitly included six different patterns of connection between vertices, were created. Both scale-free networks and random graphs showed a different response in path level to increasing levels of clustering than regular lattices. Clustering promoted short path lengths in all network types, but randomly assembled networks displayed a logarithmic relationship between degree and path length; whereas this response was linear in regular lattices. In all cases, small-world models, generated by rewiring the connections of a regular lattice, displayed properties, which spanned the gap between random and regular networks. Simulation of a disease in these networks showed a strong response to connectance pattern, even when the number of edges and vertices were approximately equal. Epidemic spread was fastest, and reached the largest size, in scale-free networks, then in random graphs. Regular lattices were the slowest to be infected, and rewired lattices were intermediate between these two extremes. Scale-free networks displayed the capacity to produce an epidemic even at a likelihood of infection, which was too low to produce an epidemic for the other network types. The interaction between the statistical properties of the network and the results of epidemic spread provides a useful tool for assessing the risk of disease spread in more realistic networks. © 2005 Elsevier B.V. All rights reserved.
Author(s): Shirley MDF, Rushton SP
Publication type: Article
Publication status: Published
Journal: Ecological Complexity
Print publication date: 01/09/2005
ISSN (print): 1476-945X
ISSN (electronic): 1476-9840
Altmetrics provided by Altmetric