  In this paper, a branching process approximation for the spread of a
Reed-Frost epidemic on a network with tunable clustering is derived. The
approximation gives rise to expressions for the epidemic threshold and the
probability of a large outbreak in the epidemic. It is investigated how these
quantities varies with the clustering in the graph and it turns out for
instance that, as the clustering increases, the epidemic threshold decreases.
The network is modelled by a random intersection graph, in which individuals
are independently members of a number of groups and two individuals are linked
to each other if and only if they share at least one group.
