Network Epidemiology: from analytical insights to data-driven modeling of contagion

Modern network science has greatly contributed to our understanding of many processes in diverse fields of science Arguably, contagion dynamics -including network epidemiology- is the area in which network concepts have had a bigger practical impact. Nowadays, we can model how diseases unfold and spread with unprecedented precision, making it possible to analyze other spreading-like processes, such as social contagion. In this talk, we revise this area of research by discussing how the modeling of spreading processes has evolved in the last two decades. We start by analyzing contagion dynamics in single populations described by different network topologies. Next, we discuss cases in which a multilayer approach is needed. Finally, we discuss the recent COVID-19 pandemic using tools that combine theoretical models with data-driven simulations and network science tools. We conclude the talk by discussing the challenges that remain for the future.

Zoom link:

https://us06web.zoom.us/j/81775120266?pwd=Gov6IFt4L7SGRDGe1C3fqqe9cKgG2i.1