title: Large epidemic thresholds emerge in heterogeneous networks of heterogeneous nodes.
abstract: One of the famous results of network science states that networks with heterogeneous connectivity are more susceptible to epidemic spreading than their more homogeneous counterparts. In particular, in networks of identical nodes it has been shown that network heterogeneity, i.e. a broad degree distribution, can lower the epidemic threshold at which epidemics can invade the system. Network heterogeneity can thus allow diseases with lower transmission probabilities to persist and spread. However, it has been pointed out that networks in which the properties of nodes are intrinsically heterogeneous can be very resilient to disease spreading. Heterogeneity in structure can enhance or diminish the resilience of networks with heterogeneous nodes, depending on the correlations between the topological and intrinsic properties. Here, we consider a plausible scenario where people have intrinsic differences in susceptibility and adapt their social network structure to the presence of the disease. We show that the resilience of networks with heterogeneous connectivity can surpass those of networks with homogeneous connectivity. For epidemiology, this implies that network heterogeneity should not be studied in isolation, it is instead the heterogeneity of infection risk that determines the likelihood of outbreaks.
authors: Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.|Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK.
authors: Hui
authors: H
authors: Yang
authors: Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors: Ming
authors: M
authors: Tang
authors: Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK.
authors: Thilo
authors: T
authors: Gross
mesh_terms: D003141:Communicable Diseases / Q000453:epidemiology*; D004198:Disease Susceptibility / Q000453:epidemiology*; D058872:Epidemics; D006801:Humans; D008954:Models, Biological