机读格式显示(MARC)

• 000 10371nam a2200409 i 4500

• 001 0000506462

• 003 SCNU

• 005 20141105144200.0

• 008 140524s2014 cc ab b 001 0 eng d

• 020 __ |a 9787040307177 (hardback)

• 040 __ |a PUL |b eng |c PUL |e rda

• 050 _4 |a RA652.2.M3 |b F8

• 082 04 |a 614.401/5118 |2 23

• 093 __ |a Q-332 |2 5

• 099 __ |a CAL 022014035304

• 100 1_ |a Fu, Xinchu |9 (傅新楚)

• 245 10 |a Propagation dynamics on complex networks : |b models, methods and stability analysis = 复杂网络传播动力学 : 模型、方法与稳定性分析/ |c 傅新楚, Michael Small, 陈关荣著.

• 246 31 |a 复杂网络传播动力学 : |b 模型、方法与稳定性分析

• 264 _1 |a Beijing : |b Higher Education Press, |c 2014.

• 300 __ |a xii, 314 pages : |b illustrations, maps ; |c 24 cm.

• 336 __ |a text |2 rdacontent

• 337 __ |a unmediated |2 rdamedia

• 338 __ |a volume |2 rdacarrier

• 490 0_ |a 网络科学与工程丛书 ; |v 6

• 500 __ |a "国家科学技术学术著作出版基金资助."

• 500 __ |a "本书由高等教育出版社与Wiley公司合作出版……海外发行ISBN为978-1-118-534502."

• 504 __ |a Includes bibliographical references and index.

• 520 __ |a "Providing an introduction of general epidemic models, Propagation Dynamics on Complex Networks explores emerging topics of epidemic dynamics on complex networks, including theories, methods, and real-world applications with elementary and wide-coverage. This valuable text for researchers and students explores models evolving over complex networks and presents results concerning dynamics of Network-based models on a macroscopic scale. The text presents the fundamental knowledge needed to demonstrate how epidemic dynamical networks can be modeled, analyzed, and controlled along the state-of-the-art and recent progress in the field and related issues arising from various epidemic systems"--Provided by publisher.

• 520 __ |a "This book explores the emerging topics of epidemic dynamics on complex networks, including theories, methods, and real-world applications"--Provided by publisher.

• 504 __ |a Includes bibliographical references and index.

• 505 8_ |a Machine generated contents note: 1 Introduction 1.1 Motivation and background 1.2 A brief history of mathematical epidemiology 1.2.1 Compartmental modelling 1.2.2 Epidemic modelling on complex networks 1.3 Organization of the book 2 Epidemic Models on Complex Networks 2.1 Multiple status models 2.1.1 Multiple susceptible individuals 2.1.2 Multiple infected individuals 2.1.3 Multiple-staged infected individuals 2.2 Staged progression models 2.2.1 Simple-staged progression model 2.2.2 Staged progression model on homogeneous networks 2.2.3 Staged progression model on heterogenous networks 2.2.4 Staged progression model with birth and death 2.2.5 Staged progression model with birth and death on homogeneous networks 2.2.6 Staged progression model with birth and death on heterogenous networks 2.3 Stochastic SIS model 2.4 Models with population mobility 2.4.1 Epidemic spreading without mobility of individuals 2.4.2 Spreading of epidemic diseases among different cities 2.4.3 Epidemic spreading within and between cities 2.5 Models in meta-populations 2.6 Models with effective contacts 2.6.1 Epidemics with effectively uniform contact 2.6.2 Epidemics with effective contacts 2.7 Models with two distinct routes 2.8 Models with competing strains 2.8.1 SIS model with competing strains 2.8.2 Remarks and discussions 2.9 Models with competing strains and saturated infectivity 2.9.1 SIS model with mutation mechanism 2.9.2 SIS model with super-infection mechanism 2.10 Models with birth and death of nodes and links 2.11 Models on weighted networks 2.12 Models on directed networks 2.13 Models on colored networks 2.13.1 SIS epidemic models on colored networks 2.13.2 Microscopic Markov-chain analysis 2.14 Discrete epidemic models 2.14.1 Discrete SIS model with nonlinear contagion scheme 2.14.2 Discrete-time epidemic model in heterogenous networks 2.14.3 A generalized model 3 Threshold Analysis 3.1 Threshold analysis by the direct method 3.1.1 Epidemics on homogeneous networks 3.1.2 Epidemics on heterogeneous networks 3.2 Epidemic spreading efficiency threshold and epidemic threshold 3.2.1 The case of λ1 ̸= λ2 3.2.2 The case of λ1 = λ2 3.2.3 Epidemic threshold in finite populations 3.2.4 Epidemic threshold in infinite populations 3.3 Epidemic thresholds and basic reproduction numbers 3.3.1 Threshold from a self-consistency equation 3.3.2 Threshold unobtainable from a self-consistency equation 3.3.3 Threshold analysis for SIS model with mutation 3.3.4 Threshold analysis for SIS model with super-infection 3.3.5 Epidemic thresholds for models on directed networks 3.3.6 Epidemic thresholds on technological and social networks 3.3.7 Epidemic thresholds on directed networks with immunization 3.3.8 Comparisons of epidemic thresholds with immunizations 3.3.9 Thresholds for colored network models 3.3.10 Thresholds for discrete epidemic models 3.3.11 Basic reproduction number and existence of a positive equilibrium 4 Networked Models for SARS and H5N1 4.1 Network models of real diseases 4.2 Plausible models for propagation of the SARS virus 4.3 Applications to epidemic control and risk assessment 4.4 Small-world and scale-free models for SARS transmission 4.5 Super-spreaders and the rate of transmission 4.6 Scale-free distribution of avian influenza outbreaks 4.7 Stratified model of ordinary influenza 5 Infectivity Functions 5.1 A model with nontrivial infectivity function 5.1.1 Epidemic threshold for SIS model with piecewise-linear infectivity 5.1.2 Piecewise smooth and nonlinear infectivity 5.2 Saturated infectivity 5.3 Nonlinear infectivity for SIS model on scale-free networks 5.3.1 Epidemic threshold for SIS model with nonlinear infectivity 5.3.2 Discussions and remarks 6 SIS Models with an Infective Medium 6.1 SIS model with an infective medium 6.1.1 Homogeneous complex networks 6.1.2 Scale-free networks: the Barab´asi-Albert model 6.1.3 Uniform immunization strategy 6.1.4 Optimized immunization strategies 6.2 A modified SIS model with an infective medium 6.2.1 The modified model 6.2.2 Epidemic threshold for the modified model with an infective medium 6.3 Epidemic models with vectors between two separated networks 6.3.1 Model formulation 6.3.2 Basic reproduction number 6.3.3 Sensitivity analysis 6.4 Epidemic transmission on interdependent networks 6.4.1 Theoretical modeling 6.4.2 Mathematical analysis of epidemic dynamics 6.4.3 Effect of model parameters on the basic reproduction number 6.4.4 Effect of model parameters on infected node densities 6.5 Discussions and remarks 7 Epidemic Control and Awareness 7.1 SIS model with awareness 7.1.1 Background 7.1.2 The model 7.1.3 Epidemic threshold 7.1.4 Conclusions and discussions 7.2 Discrete-time SIS model with awareness 7.2.1 SIS model with awareness interactions 7.2.2 Theoretical analysis: basic reproduction number 7.2.3 Remarks and discussions 7.3 Spreading dynamics of a disease-awareness SIS model on complex networks 7.3.1 Model formulation 7.3.2 Derivation of limiting systems 7.3.3 Basic reproduction number and local stability 7.4 Remarks and discussions 8 Adaptive Mechanism between Dynamics and Epidemics 8.1 Adaptive mechanism between dynamical synchronization and epidemic behavior 8.1.1 Models of complex dynamical network and epidemic network 8.1.2 Models of epidemic synchronization and its analysis 8.1.3 Local stability of epidemic synchronization 8.1.4 Global stability of epidemic synchronization 8.2 Interplay between collective behavior and spreading dynamics 8.2.1 A general bidirectional model 8.2.2 Global synchronization and spreading dynamics 8.2.3 Stability of global synchronization and spreading dynamics 8.2.4 Phase synchronization and spreading dynamics 8.2.5 Control of spreading networks 8.2.6 Discussions and remarks 9 Epidemic Control and Immunization 9.1 SIS model with immunization 9.1.1 Proportional immunization 9.1.2 Targeted immunization 9.1.3 Acquaintance immunization 9.1.4 Active immunization 9.2 Edge targeted strategy for controlling epidemic spreading on scale-free networks 9.3 Remarks and discussions 10 Global Stability Analysis 10.1 Global stability analysis of the modified model with an infective medium. 10.2 Global dynamics of the model with vectors between two separated networks. 10.2.1 Global stability and existence of disease-free and endemic equilibria. 10.2.2 Uniqueness and global attractivity of the endemic equilibrium. 10.3 Global behavior of disease transmission on interdependent networks 10.4 Global behavior of epidemic transmissions 10.4.1 Stability of the model equilibria 10.4.2 Stability analysis for discrete epidemic models 10.4.3 Global stability of the disease-free equilibrium 10.4.4 Global attractiveness of epidemic disease 10.5 Global attractivity of a network-based epidemic SIS model 10.5.1 Positiveness, boundedness and equilibria 10.5.2 Global attractivity of the model 10.5.3 Remarks and discussions 10.6 Global stability of a model with adaptive weights 10.6.1 Global dynamics of the model 10.6.2 Discussions and remarks 10.7 Global dynamics of a generalized epidemic model 10.7.1 Model formulation 10.7.2 Global dynamics of the model 10.7.3 Discussions and remarks 11 Propagation Dynamics on Complex Networks 11.1 Information diffusion and propagation on complex networks 11.1.1 Information diffusion on complex networks 11.1.2 Differences between information propagation and epidemic spreading 11.2 Interplay between information of disease spreading and epidemic dynamics 11.2.1 Preliminaries 11.2.2 Theoretical analysis of the model 11.3 Discussions and remarks A Proofs of Theorems A.1 Transition from discrete-time linear system to continuous-time linear system A.2 Proof of Lemma 6.1.1 A.3 Proof of Theorem 10.1.4 A.4 Proof of Theorem 10.5.5 A.5 Proof of Theorem 10.7.4 B Further Proofs of Results B.1 Eigenvalues of the matrix e F in (6.3.2) B.2 The matrix [gamma] in (6.4.5) B.3 Proof of (7.1.6) B.4 The positiveness of σ′ B.5 The relation between Λ and κ Index .

• 650 _0 |a Epidemiology |x Mathematical models.

• 650 _0 |a Epidemiology |x Methodology.

• 700 1_ |a Small, Michael, |c (Professor)

• 700 1_ |a Chen, G. |q (Guanrong) |9 (陈关荣)

• 920 __ |a 242430 |z 2

• 950 __ |a SCNU |f Q-332/F949