Technological networks

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Table of contents

1 Introduction
2 Theoretical Framework: Networks and Graphs 
2.1 Basic definitions
2.2 Real networks
2.2.1 Social networks
2.2.2 Technological networks
2.2.3 Biological networks
2.3 Network models
2.3.1 Random networks: Erd¨os-R´enyi (ER) model
2.3.2 Small world networks: Watts-Strogatz (WS) model
2.3.3 Scale free networks: Barab´asi-Albert (BA) model
2.4 Dynamical networks
2.5 Conclusions
3 Theoretical Framework: Epidemic Models 
3.1 Compartmental models
3.2 Epidemic spreading on graphs
3.3 Metapopulation models
3.4 Conclusion
4 GLobal Epidemic and Mobility model 
4.1 Global Population and subpopulations definition
4.2 World Airport Network
4.3 Commuting Networks
4.4 Epidemic model
4.5 Stochastic and discrete integration of the disease dynamics
4.6 The integration of the transport operator
4.7 Time-scale separation and the integration of the commuting flows
4.8 Effective force of infection
5 Global spread of H1N1 pandemic influenza 
5.1 Background
5.2 Disease parameters estimation
5.3 Real time predictions
5.4 Estimating the early number of cases in Mexico
5.5 Intervention strategies
5.5.1 Vaccination campaign
5.5.2 Modeling the critical care demand
5.5.3 Travel restrictions
5.6 Assessment of model predictions and discussion
6 Dynamical network analysis and spreading simulations 
6.1 Background
6.2 Data description
6.3 Daily and aggregated networks
6.4 Network microscopic dynamics
6.4.1 Activity timescales
6.4.2 Fluctuations of nodes and links properties
6.4.3 Evolution of the network backbone
6.4.4 Dynamical motifs
6.5 Spreading processes on dynamical networks
6.5.1 Percolation analysis
6.5.2 Epidemic spreading simulations
6.5.3 Invasion paths and seeds’ cluster detection
6.5.4 Longitudinal stability of the seeds’ clusters
6.5.5 Disease sentinels
6.5.6 Generalization to the stochastic case
6.6 Conclusions
7 Conclusions and perspectives 
References