NDLI: Spreading dynamics on complex networks: a general stochastic approach

Content Provider	Springer Nature Link
Author	Marceau, Vincent ël, Pierre André Dubé, Louis J. Allard, Antoine Hébert Dufresne, Laurent
Copyright Year	2013
Abstract	Dynamics on networks is considered from the perspective of Markov stochastic processes. We partially describe the state of the system through network motifs and infer any missing data using the available information. This versatile approach is especially well adapted for modelling spreading processes and/or population dynamics. In particular, the generality of our framework and the fact that its assumptions are explicitly stated suggests that it could be used as a common ground for comparing existing epidemics models too complex for direct comparison, such as agent-based computer simulations. We provide many examples for the special cases of susceptible-infectious-susceptible and susceptible-infectious-removed dynamics (e.g., epidemics propagation) and we observe multiple situations where accurate results may be obtained at low computational cost. Our perspective reveals a subtle balance between the complex requirements of a realistic model and its basic assumptions.
Ending Page	1660
Page Count	34
Starting Page	1627
File Format	PDF
ISSN	03036812
e-ISSN	14321416
Journal	Journal of Mathematical Biology
Issue Number	6-7
Volume Number	69
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-12-24
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Epidemics Mathematical modeling (models of systems, model-matching, etc.) Population dynamics (general) Mathematical and Computational Biology Stochastic processes Epidemiology Complex networks Spreading dynamics Markov processes Applications of continuous-time Markov processes on discrete state spaces Applications of Mathematics Contact networks Small world graphs, complex networks
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Agricultural and Biological Sciences Modeling and Simulation

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