13,014 research outputs found
A general class of spreading processes with non-Markovian dynamics
In this paper we propose a general class of models for spreading processes we
call the model. Unlike many works that consider a fixed number of
compartmental states, we allow an arbitrary number of states on arbitrary
graphs with heterogeneous parameters for all nodes and edges. As a result, this
generalizes an extremely large number of models studied in the literature
including the MSEIV, MSEIR, MSEIS, SEIV, SEIR, SEIS, SIV, SIRS, SIR, and SIS
models. Furthermore, we show how the model allows us to model
non-Poisson spreading processes letting us capture much more complicated
dynamics than existing works such as information spreading through social
networks or the delayed incubation period of a disease like Ebola. This is in
contrast to the overwhelming majority of works in the literature that only
consider spreading processes that can be captured by a Markov process. After
developing the stochastic model, we analyze its deterministic mean-field
approximation and provide conditions for when the disease-free equilibrium is
stable. Simulations illustrate our results
Epidemic processes in complex networks
In recent years the research community has accumulated overwhelming evidence
for the emergence of complex and heterogeneous connectivity patterns in a wide
range of biological and sociotechnical systems. The complex properties of
real-world networks have a profound impact on the behavior of equilibrium and
nonequilibrium phenomena occurring in various systems, and the study of
epidemic spreading is central to our understanding of the unfolding of
dynamical processes in complex networks. The theoretical analysis of epidemic
spreading in heterogeneous networks requires the development of novel
analytical frameworks, and it has produced results of conceptual and practical
relevance. A coherent and comprehensive review of the vast research activity
concerning epidemic processes is presented, detailing the successful
theoretical approaches as well as making their limits and assumptions clear.
Physicists, mathematicians, epidemiologists, computer, and social scientists
share a common interest in studying epidemic spreading and rely on similar
models for the description of the diffusion of pathogens, knowledge, and
innovation. For this reason, while focusing on the main results and the
paradigmatic models in infectious disease modeling, the major results
concerning generalized social contagion processes are also presented. Finally,
the research activity at the forefront in the study of epidemic spreading in
coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio
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