Abstract
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.
| Original language | English |
|---|---|
| Article number | 062308 |
| Number of pages | 8 |
| Journal | Physical Review. E, Statistical, nonlinear, and soft matter physics |
| Volume | 103 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 17 Jun 2021 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Complex networks
- Percolation
- Epidemic spreading
- Co-infection
- Clustered networks
Fingerprint
Dive into the research topics of 'Two-pathogen model with competition on clustered networks'. Together they form a unique fingerprint.Student theses
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On the use of generating functions for topics in clustered networks
Mann, P. S. (Author), Dobson, S. A. (Supervisor), 15 Jun 2022Student thesis: Doctoral Thesis (PhD)
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