title: Network inoculation: Heteroclinics and phase transitions in an epidemic model.
abstract: In epidemiological modelling, dynamics on networks, and, in particular, adaptive and heterogeneous networks have recently received much interest. Here, we present a detailed analysis of a previously proposed model that combines heterogeneity in the individuals with adaptive rewiring of the network structure in response to a disease. We show that in this model, qualitative changes in the dynamics occur in two phase transitions. In a macroscopic description, one of these corresponds to a local bifurcation, whereas the other one corresponds to a non-local heteroclinic bifurcation. This model thus provides a rare example of a system where a phase transition is caused by a non-local bifurcation, while both micro- and macro-level dynamics are accessible to mathematical analysis. The bifurcation points mark the onset of a behaviour that we call network inoculation. In the respective parameter region, exposure of the system to a pathogen will lead to an outbreak that collapses but leaves the network in a configuration where the disease cannot reinvade, despite every agent returning to the susceptible class. We argue that this behaviour and the associated phase transitions can be expected to occur in a wide class of models of sufficient complexity.
authors: Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors: Hui
authors: H
authors: Yang
authors: Centre for Networks and Collective Behaviour, Department of Mathematical Sciences, University of Bath, Claverton Down, BA2 7AY Bath, United Kingdom.
authors: Tim
authors: T
authors: Rogers
authors: Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, United Kingdom.
authors: Thilo
authors: T
authors: Gross
mesh_terms: D003198:Computer Simulation; D004196:Disease Outbreaks; D004198:Disease Susceptibility; D058872:Epidemics; D006801:Humans; D008954:Models, Biological