Modeling and forecast of socio-technical systems in the data-science age

In recent years the increasing availability of computer power and
informatics tools has enabled the gathering of reliable data
quantifying the complexity of socio-technical systems. Data-driven
computational models have emerged as appropriate tools to tackle the
study of contagion and diffusion processes as diverse as epidemic
outbreaks, information spreading and Internet packet routing. These
models aim at providing a rationale for understanding the emerging
tipping points and nonlinear properties that often underpin the most
interesting characteristics of socio-technical systems. Here I review
some of the recent progress in modelling contagion and epidemic
processes that integrates the complex features and heterogeneities of
real-world systems.