title: Using network properties to predict disease dynamics on human contact networks.
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.
authors.affiliation: Department of Biology, Colorado State University, Fort Collins, CO 80523, USA. gregames@lamar.colostate.edu
authors.forename: Gregory M
authors.initials: GM
authors.lastname: Ames
authors.forename: Dylan B
authors.initials: DB
authors.lastname: George
authors.forename: Christian P
authors.initials: CP
authors.lastname: Hampson
authors.forename: Andrew R
authors.initials: AR
authors.lastname: Kanarek
authors.forename: Cayla D
authors.initials: CD
authors.lastname: McBee
authors.forename: Dale R
authors.initials: DR
authors.lastname: Lockwood
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Achter
authors.forename: Colleen T
authors.initials: CT
authors.lastname: Webb
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D016358:Contact Tracing; D018562:Disease Transmission, Infectious; D058872:Epidemics; D006801:Humans; D008962:Models, Theoretical; D011156:Population Density; D013269:Stochastic Processes