Edge-based compartmental modelling of an SIR epidemic on a dual-layer static-dynamic multiplex network with tunable clustering

Barnard, Rosanna C, Kiss, Istvan Z, Berthouze, Luc and Miller, Joel C (2018) Edge-based compartmental modelling of an SIR epidemic on a dual-layer static-dynamic multiplex network with tunable clustering. Bulletin of Mathematical Biology, 80 (10). pp. 2698-2733. ISSN 0092-8240

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The duration, type and structure of connections between individuals in real-world populations play a crucial role in how diseases invade and spread. Here, we incorporate the aforementioned heterogeneities into a model by considering a dual-layer static-dynamic multiplex network. The static network layer affords tunable clustering and describes an individual’s permanent community structure. The dynamic network layer describes the transient connections an individual makes with members of the wider population by imposing constant edge rewiring. We follow the edge-based compartmental modelling approach to derive equations describing the evolution of a susceptible - infected - recovered (SIR) epidemic spreading through this multiplex network of individuals. We derive the basic reproduction number, measuring the expected number of new infectious cases caused by a single infectious individual in an otherwise susceptible population. We validate model equations by showing convergence to pre-existing edge-based compartmental model equations in limiting cases and by comparison with stochastically simulated epidemics. We explore the effects of altering model parameters and multiplex network attributes on resultant epidemic dynamics. We validate the basic reproduction number by plotting its value against associated final epidemic sizes measured from simulation and predicted by model equations for a number of setups. Further, we explore the effect of varying individual model parameters on the basic reproduction number. We conclude with a discussion of the significance and interpretation of the model and its relation to existing research literature. We highlight intrinsic limitations and potential extensions of the present model and outline future research considerations, both experimental and theoretical.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Engineering and Design
School of Mathematical and Physical Sciences > Mathematics
Research Centres and Groups: Centre for Computational Neuroscience and Robotics
Mathematics Applied to Biology Research Group
Subjects: Q Science > QA Mathematics
T Technology > TA Engineering (General). Civil engineering (General)
Depositing User: Jodie Bacon
Date Deposited: 24 Aug 2018 12:59
Last Modified: 17 Oct 2018 16:16
URI: http://srodev.sussex.ac.uk/id/eprint/78267

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Project NameSussex Project NumberFunderFunder Ref
UnsetUnsetEngineering and Physical Sciences esearch CouncilUnset
UnsetUnsetGlobal Good FundUnset
UnsetUnsetInstitute of Disease ModelingUnset