title: A general model for stochastic SIR epidemics with two levels of mixing.
abstract: This paper is concerned with a general stochastic model for susceptible-->infective-->removed epidemics, among a closed finite population, in which during its infectious period a typical infective makes both local and global contacts. Each local contact of a given infective is with an individual chosen independently according to a contact distribution 'centred' on that infective, and each global contact is with an individual chosen independently and uniformly from the whole population. The asymptotic situation in which the local contact distribution remains fixed as the population becomes large is considered. The concepts of local infectious clump and local susceptibility set are used to develop a unified approach to the threshold behaviour of this class of epidemic models. In particular, a threshold parameter R(*) governing whether or not global epidemics can occur, the probability that a global epidemic occurs and the mean proportion of initial susceptibles ultimately infected by a global epidemic are all determined. The theory is specialised to (i) the households model, in which the population is partitioned into households and local contacts are chosen uniformly within an infective's household; (ii) the overlapping groups model, in which the population is partitioned in several ways, with local uniform mixing within the elements of the partitions; and (iii) the great circle model, in which individuals are equally spaced on a circle and local contacts are nearest-neighbour.
authors.affiliation: School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK. frank.ball@nottingham.ac.uk
authors.forename: Frank
authors.initials: F
authors.lastname: Ball
authors.forename: Peter
authors.initials: P
authors.lastname: Neal
mesh_terms: D003141:Communicable Diseases / Q000453:epidemiology* / Q000635:transmission; D016358:Contact Tracing; D004196:Disease Outbreaks; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D016012:Poisson Distribution; D011157:Population Dynamics; D013269:Stochastic Processes