期刊
PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
卷 278, 期 1724, 页码 3544-3550出版社
ROYAL SOC
DOI: 10.1098/rspb.2011.0290
关键词
contact networks; clustering coefficient; mean path length; exponential degree distribution; disease modelling
资金
- NSF IGERT [DGE-0221595]
- Science and Technology Directorate, Department of Homeland Security [ST-108-000017]
- Department of Defense-Science, Mathematics And Research for Transformation (SMART)
- Fogarty International Center, National Institutes of Health
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.
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