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Consistent approximation of epidemic dynamics on degree-heterogeneous clustered networks
conference contribution
posted on 2023-06-09, 15:53 authored by A Bishop, Istvan Kiss, T HouseRealistic 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. [28] 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 [40], providing evidence that this novel model can be a more accurate approximation to epidemic dynamics on complex networks than existing approaches.
History
Publication status
- Published
File Version
- Accepted version
Journal
Complex Networks and Their Applications VIIISSN
1860-949XPublisher
SpringerExternal DOI
Volume
812Page range
376-391Event name
COMPLEX NETWORKS 2018 The 7th International Conference on Complex Networks and Their ApplicationsEvent location
Cambridge, United KingdomEvent type
conferenceEvent date
December 11-13, 2018ISBN
9783030054106Series
Studies in Computational IntelligenceDepartment affiliated with
- Mathematics Publications
Research groups affiliated with
- Mathematics Applied to Biology Research Group Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2018-11-19First Open Access (FOA) Date
2019-12-02First Compliant Deposit (FCD) Date
2018-11-12Usage metrics
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