title: Interdependency and hierarchy of exact and approximate epidemic models on networks.
abstract: Over the years numerous models of S I S (susceptible --> infected --> susceptible) disease dynamics unfolding on networks have been proposed. Here, we discuss the links between many of these models and how they can be viewed as more general motif-based models. We illustrate how the different models can be derived from one another and, where this is not possible, discuss extensions to established models that enables this derivation. We also derive a general result for the exact differential equations for the expected number of an arbitrary motif directly from the Kolmogorov/master equations and conclude with a comparison of the performance of the different closed systems of equations on networks of varying structure.
authors.affiliation: Centre for Computational Neuroscience and Robotics, University of Sussex, Falmer, Brighton, BN1 9QH, UK.
authors.forename: Timothy J
authors.initials: TJ
authors.lastname: Taylor
authors.forename: Istvan Z
authors.initials: IZ
authors.lastname: Kiss
mesh_terms: D003141:Communicable Diseases / Q000453:epidemiology / Q000635:transmission*; D003198:Computer Simulation; D058872:Epidemics; D006801:Humans; D008954:Models, Biological