2019
DOI: 10.1007/s00285-019-01347-2
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SIR epidemics and vaccination on random graphs with clustering

Abstract: In this paper we consider Susceptible Infectious Recovered (SIR) epidemics on random graphs with clustering. To incorporate group structure of the underlying social network, we use a generalized version of the configuration model in which each node is a member of a specified number of triangles. SIR epidemics on this type of graph have earlier been investigated under the assumption of homogeneous infectivity and also under the assumption of Poisson transmission and recovery rates. We ex… Show more

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Cited by 8 publications
(4 citation statements)
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References 45 publications
(102 reference statements)
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“…The finding of Volz et al [25] corroborates the results of Badham and Stocker [23] by outlining the importance of considering the structural features of the networks when the aim is to predict the epidemic spreading. It is important for network interventions to halt epidemics, such as concerning the vaccination of individuals [26] or performing social distancing [9], to consider the extent of node clustering.…”
Section: Spreading Node Clustering Coefficient and Node Assortativitymentioning
confidence: 99%
“…The finding of Volz et al [25] corroborates the results of Badham and Stocker [23] by outlining the importance of considering the structural features of the networks when the aim is to predict the epidemic spreading. It is important for network interventions to halt epidemics, such as concerning the vaccination of individuals [26] or performing social distancing [9], to consider the extent of node clustering.…”
Section: Spreading Node Clustering Coefficient and Node Assortativitymentioning
confidence: 99%
“…The simplicial model here defined allows for a parameter for each simplex. Similarly, Fransson and Trapman [12] consider an SIR epidemic on a network with different transmission rates between nodes belonging to a same triangle. However, all triangles show this separate infection rate, in contrast to our model where triangles may not be included as simplices in the complex.…”
Section: Simplicial Stochastic Sirmentioning
confidence: 99%
“…Of note, 4 different types of models are often used in the literature, which are as follows: (1) differential equations, 50 , 75 (2) random graphs, 76 (3) difference equations, 77 and (4) simulation (agent-based) models. 45 , 73 In differential equation models, individuals are divided into one of the epidemiologic classes (eg, SIR model with susceptible [S], infected [I], or recovered [R] stages) and changes in the number of infected people are calculated based on the cumulative number of people in each step.…”
Section: Prediction Of and Responding To Disease Spread During An Infmentioning
confidence: 99%