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The duration, type and structure of connections between individuals inreal-world populations play a crucial role in how diseases invade and spread.Here, we incorporate the aforementioned heterogeneities into a model byconsidering a dual-layer static-dynamic multiplex network. The static networklayer affords tunable clustering and describes an individual's permanentcommunity structure. The dynamic network layer describes the transientconnections an individual makes with members of the wider population byimposing constant edge rewiring. We follow the edge-based compartmentalmodelling approach to derive equations describing the evolution of asusceptible - infected - recovered (SIR) epidemic spreading through thismultiplex network of individuals. We derive the basic reproduction number,measuring the expected number of new infectious cases caused by a singleinfectious individual in an otherwise susceptible population. We validate modelequations by showing convergence to pre-existing edge-based compartmental modelequations in limiting cases and by comparison with stochastically simulatedepidemics. We explore the effects of altering model parameters and multiplexnetwork attributes on resultant epidemic dynamics. We validate the basicreproduction number by plotting its value against associated final epidemicsizes measured from simulation and predicted by model equations for a number ofsetups. Further, we explore the effect of varying individual model parameterson the basic reproduction number. We conclude with a discussion of thesignificance and interpretation of the model and its relation to existingresearch literature. We highlight intrinsic limitations and potentialextensions of the present model and outline future research considerations,both experimental and theoretical.

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