title: Complex dynamics of synergistic coinfections on realistically clustered networks.
abstract: We investigate the impact of contact structure clustering on the dynamics of multiple diseases interacting through coinfection of a single individual, two problems typically studied independently. We highlight how clustering, which is well known to hinder propagation of diseases, can actually speed up epidemic propagation in the context of synergistic coinfections if the strength of the coupling matches that of the clustering. We also show that such dynamics lead to a first-order transition in endemic states, where small changes in transmissibility of the diseases can lead to explosive outbreaks and regions where these explosive outbreaks can only happen on clustered networks. We develop a mean-field model of coinfection of two diseases following susceptible-infectious-susceptible dynamics, which is allowed to interact on a general class of modular networks. We also introduce a criterion based on tertiary infections that yields precise analytical estimates of when clustering will lead to faster propagation than nonclustered networks. Our results carry importance for epidemiology, mathematical modeling, and the propagation of interacting phenomena in general. We make a call for more detailed epidemiological data of interacting coinfections.
authors.affiliation: Santa Fe Institute, Santa Fe, NM 87501; laurent@santafe.edu.
authors.forename: Laurent
authors.initials: L
authors.lastname: Hébert-Dufresne
authors.affiliation: Santa Fe Institute, Santa Fe, NM 87501; Institute for Disease Modeling, Bellevue, WA 98005.
authors.forename: Benjamin M
authors.initials: BM
authors.lastname: Althouse
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D060085:Coinfection / Q000453:epidemiology*; D003141:Communicable Diseases / Q000453:epidemiology*; D003198:Computer Simulation; D058872:Epidemics; D006801:Humans; D008954:Models, Biological
keywords: discontinuous transitions; epidemiology; influenza; interacting coinfections; network theory