  Realistic human contact networks capable of spreading infectious disease, for
example studied in social contact surveys, exhibit both significant degree
heterogeneity and clustering, both of which greatly affect epidemic dynamics.
To understand the joint effects of these two network properties on epidemic
dynamics, the effective degree model of Lindquist et al. is reformulated with a
new moment closure to apply to highly clustered networks. A simulation study
comparing alternative ODE models and stochastic simulations is performed for
SIR (Susceptible-Infected-Removed) epidemic dynamics, including a test for the
conjectured error behaviour in Pellis et al., providing evidence that this
novel model can be a more accurate approximation to epidemic dynamics on
complex networks than existing approaches.
