Network epidemic models with two levels of mixing

Ball, Frank and Neal, Peter (2008) Network epidemic models with two levels of mixing. Mathematical Biosciences, 212 (1). pp. 69-87. ISSN 0025-5564

Full text not available from this repository.


The study of epidemics on social networks has attracted considerable attention recently. In this paper, we consider a stochastic SIR (susceptible → infective → removed) model for the spread of an epidemic on a finite network, having an arbitrary but specified degree distribution, in which individuals also make casual contacts, i.e. with people chosen uniformly from the population. The behaviour of the model as the network size tends to infinity is investigated. In particular, the basic reproduction number R0, that governs whether or not an epidemic with few initial infectives can become established is determined, as are the probability that an epidemic becomes established and the proportion of the population who are ultimately infected by such an epidemic. For the case when the infectious period is constant and all individuals in the network have the same degree, the asymptotic variance and a central limit theorem for the size of an epidemic that becomes established are obtained. Letting the rate at which individuals make casual contacts decrease to zero yields, heuristically, corresponding results for the model without casual contacts, i.e. for the standard SIR network epidemic model. A deterministic model that approximates the spread of an epidemic that becomes established in a large population is also derived. The theory is illustrated by numerical studies, which demonstrate that the asymptotic approximations work well, even for only moderately sized networks, and that the degree distribution and the inclusion of casual contacts can each have a major impact on the outcome of an epidemic.

Item Type:
Journal Article
Journal or Publication Title:
Mathematical Biosciences
Uncontrolled Keywords:
ID Code:
Deposited By:
Deposited On:
05 Nov 2012 10:07
Last Modified:
21 Nov 2022 23:03