Math. Model. Nat. Phenom.
Volume 9, Number 2, 2014Epidemics models on networks
|Page(s)||58 - 81|
|Published online||24 April 2014|
Pairwise and Edge-based Models of Epidemic Dynamics on Correlated Weighted Networks
1 School of Mathematical and Physical
Sciences, Department of Mathematics University of Sussex, Falmer, Brighton
2 School of Mathematical Sciences, School of Biological Sciences, and the Monash Academy for Cross & Interdisciplinary Mathematics, Monash University, , VIC 800, Australia
Corresponding author. E-mail: firstname.lastname@example.org
In this paper we explore the potential of the pairwise-type modelling approach to be extended to weighted networks where nodal degree and weights are not independent. As a baseline or null model for weighted networks, we consider undirected, heterogenous networks where edge weights are randomly distributed. We show that the pairwise model successfully captures the extra complexity of the network, but does this at the cost of limited analytical tractability due the high number of equations. To circumvent this problem, we employ the edge-based modelling approach to derive models corresponding to two different cases, namely for degree-dependent and randomly distributed weights. These models are more amenable to compute important epidemic descriptors, such as early growth rate and final epidemic size, and produce similarly excellent agreement with simulation. Using a branching process approach we compute the basic reproductive ratio for both models and discuss the implication of random and correlated weight distributions on this as well as on the time evolution and final outcome of epidemics. Finally, we illustrate that the two seemingly different modelling approaches, pairwise and edge-based, operate on similar assumptions and it is possible to formally link the two.
Mathematics Subject Classification: 05C82 / 37N25 / 91D30 / 92D30
Key words: weighted network / pairwise model / edge-based compartmental modelling / probability generating function
© EDP Sciences, 2014
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