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Approximate Master Equations for Dynamical Processes on Graphs

Published online by Cambridge University Press:  24 April 2014

N. Nagy ,
I.Z. Kiss  and
P.L. Simon
N. Nagy
Affiliation:
Institute of Mathematics, Eötvös Loránd University Budapest, and Numerical Analysis and Large Networks Research Group, Hungarian Academy of Sciences, Hungary
I.Z. Kiss
Affiliation:
School of Mathematical and Physical Sciences, Department of Mathematics University of Sussex, Falmer, Brighton BN1 9QH, UK
P.L. Simon*
Affiliation:
Institute of Mathematics, Eötvös Loránd University Budapest, and Numerical Analysis and Large Networks Research Group, Hungarian Academy of Sciences, Hungary
*
⋆ Corresponding author. E-mail: [email protected]
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Abstract

We extrapolate from the exact master equations of epidemic dynamics on fully connected graphs to non-fully connected by keeping the size of the state space N + 1, where N is the number of nodes in the graph. This gives rise to a system of approximate ODEs (ordinary differential equations) where the challenge is to compute/approximate analytically the transmission rates. We show that this is possible for graphs with arbitrary degree distributions built according to the configuration model. Numerical tests confirm that: (a) the agreement of the approximate ODEs system with simulation is excellent and (b) that the approach remains valid for clustered graphs with the analytical calculations of the transmission rates still pending. The marked reduction in state space gives good results, and where the transmission rates can be analytically approximated, the model provides a strong alternative approximate model that agrees well with simulation. Given that the transmission rates encompass information both about the dynamics and graph properties, the specific shape of the curve, defined by the transmission rate versus the number of infected nodes, can provide a new and different measure of network structure, and the model could serve as a link between inferring network structure from prevalence or incidence data.

Keywords

SIS epidemicODE approximationnetwork process
Type
Research Article
Information
Mathematical Modelling of Natural Phenomena , Volume 9 , Issue 2: Epidemics models on networks , 2014 , pp. 43 - 57
Copyright
© EDP Sciences, 2014

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