In:
Proceedings of the Royal Society B: Biological Sciences, The Royal Society, Vol. 278, No. 1724 ( 2011-12-07), p. 3544-3550
Abstract:
Recent studies have increasingly turned to graph theory to model more realistic contact structures that characterize disease spread. Because of the computational demands of these methods, many researchers have sought to use measures of network structure to modify analytically tractable differential equation models. Several of these studies have focused on the degree distribution of the contact network as the basis for their modifications. We show that although degree distribution is sufficient to predict disease behaviour on very sparse or very dense human contact networks, for intermediate density networks we must include information on clustering and path length to accurately predict disease behaviour. Using these three metrics, we were able to explain more than 98 per cent of the variation in endemic disease levels in our stochastic simulations.
Type of Medium:
Online Resource
ISSN:
0962-8452
,
1471-2954
DOI:
10.1098/rspb.2011.0290
Language:
English
Publisher:
The Royal Society
Publication Date:
2011
detail.hit.zdb_id:
1460975-7
SSG:
12
SSG:
25
