Networks provide a mathematically rich framework to represent social contacts sufficient for the transmission of disease. Social networks are often highly clustered and fail to be locally tree-like. In this paper, we study the effects of clustering on the spread of sequential strains of a pathogen using the generating function formulation under a complete cross immunity coupling. We derive conditions for the epidemic threshold of the first strain and the threshold of coexistence of the second strain. We find that clustering has a dual affect on the first strain, reducing the epidemic threshold but also decreasing the final outbreak size at large transmissibilities. Clustering reduces the coexistence threshold of the second strain and its outbreak size . We apply our model to the study of multilayer clustered networks and observe the fracturing of the residual graph experimentally .
Networks provide a mathematically rich framework to represent social contacts sufficient for the transmission of disease. Social networks are often highly clustered and fail to be locally tree-like. In this paper, we study the effects of clustering on the spread of sequential strains of a pathogen using the generating function formulation under a complete cross-immunity coupling, deriving conditions for the threshold of coexistence of the second strain. We show that clustering reduces the coexistence threshold of the second strain and its outbreak size in Poisson networks, whilst exhibiting the opposite effects on uniform-degree models. We conclude that clustering within a population must increase the ability of the second wave of an epidemic to spread over a network. We apply our model to the study of multilayer clustered networks and observe the fracturing of the residual graph at two distinct transmissibilities .
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cross immunity coupling. We derive
cross-immunity coupling, deriving
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epidemic threshold of the first strain and the threshold of
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find that clustering has a dual affect on the first strain, reducing the epidemic threshold but also decreasing the final outbreak size at large transmissibilities. Clustering
show that clustering
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in Poisson networks, whilst exhibiting the opposite effects on uniform-degree models. We conclude that clustering within a population must increase the ability of the second wave of an epidemic to spread over a network. We
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experimentally
at two distinct transmissibilities