Titlebook: Mathematics of Epidemics on Networks; From Exact to Approx István Z. Kiss,Joel C. Miller,Péter L. Simon Textbook 2017 Springer Internationa

恶心

Interdisciplinary Applied Mathematicshttp://image.papertrans.cn/m/image/626935.jpg

灰心丧气

https://doi.org/10.1007/978-3-319-50806-1Dynamic Processes; Mathematical Modeling; Propagation Models; Epidemics; Stochastic processes; Mean-field

积云

Rustproof

Disease spread in networks with large-scale structure,This book has developed analytic models of disease spread on networks. All of our tractable models require closure assumptions. The closure process assumes that we can explain the dynamics at the network scale by understanding the dynamics locally at the level of small units with random connections between these units.

作呕

978-3-319-84494-7Springer International Publishing AG 2017

Occipital-Lobe

Mathematics of Epidemics on Networks978-3-319-50806-1Series ISSN 0939-6047 Series E-ISSN 2196-9973

Osmosis

Propagation models on networks: bottom-up,ger structures are represented in terms of smaller structures, in order to create a closed system of equations. In most cases, this representation involves an approximation, but in the case of SIR dynamics on trees or networks with ., it is possible to reduce the number of equations considerably while keeping the model exact.

Eeg332

Mean-field approximations for homogeneous networks,re widespread in the physics and mathematical biology literature. These are used to approximate stochastic processes, with the potential to be exact in the large system or “thermodynamic” limit, see (for example, [., ., ., .] and the literature overview in Section .).

懒惰民族

Non-Markovian epidemics, many studies [66, 230, 231]. More recently, it has been shown that one can readily apply results from queueing [19] or branching process [233] theory, or use martingales [65] to cast the same questions within a different framework and obtain results more readily.

的阐明

Textbook 2017atical origin and explains how these relate to each other with special focus on epidemic spread on networks. The content of the book is at the interface of graph theory, stochastic processes and dynamical systems. The authors set out to make a significant contribution to closing the gap between mode