Math. Model. Nat. Phenom.
Volume 9, Number 2, 2014Epidemics models on networks
|Page(s)||43 - 57|
|Published online||24 April 2014|
Approximate Master Equations for Dynamical Processes on Graphs
1 Institute of Mathematics, Eötvös
Loránd University Budapest, and
Numerical Analysis and Large Networks Research Group, Hungarian Academy of Sciences,
2 School of Mathematical and Physical Sciences, Department of Mathematics University of Sussex, Falmer, Brighton BN1 9QH, UK
Corresponding author. E-mail: email@example.com
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.
Mathematics Subject Classification: 05C82 / 37N25 / 60J28 / 90B15
Key words: SIS epidemic / ODE approximation / network process
© EDP Sciences, 2014
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.