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• W2896434905 abstract "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." @default.
• W2896434905 created "2018-10-26" @default.
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• W2896434905 date "2018-12-02" @default.
• W2896434905 modified "2023-09-27" @default.
• W2896434905 title "Consistent Approximation of Epidemic Dynamics on Degree-Heterogeneous Clustered Networks" @default.
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• W2896434905 doi "https://doi.org/10.1007/978-3-030-05411-3_31" @default.
• W2896434905 hasPublicationYear "2018" @default.
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