June 24-26, 2020

Track 1

Session 2

Course Description

In the last two decades, with the advent of modern network science and the availability of large datasets, it has been possible to significantly progress toward a better characterization of spreading processes and contagion phenomena. These processes include the propagation of diseases, the diffusion of rumors and the dissemination of information on top of structured populations. The mathematical characterization and the computational description of such phenomena are key not only for a better understanding of the mechanisms behind them, but also to produce detailed and highly realistic models that are helping to make decisions in critical situations, such as the spreading of large-scale diseases in real populations.

This course teaches the main theoretical and numerical methods developed for the study of spreading processes on complex networked systems. Specifically, we formally define these processes on single and multilayer networks and discuss in detail the main methods used to perform numerical simulations. Throughout the course, we will deal with disease and rumor models, and their description using both continuous and discrete-time approximations, as well as a set of analytical frameworks that include heterogeneous, quenched and pair-quenched, mean-field approaches. Finally, we discuss in more detail recent cases of large-scale disease spreading, including scenario simulations and evaluation of possible contingency measures. In summary, the course presented here offers a whole suite of methods, from theory to practice, to study and describe epidemic-like processes in structured populations, both for researchers without previous experience in the subject and for experts.


Alessandro Vespignani

Northeastern University

Yamir Moreno

ISI, University of Zaragoza

Student preparation

Students should:
• Be familiar with basic network terminology
• Basic mathematical background (linear algebra, ODE) .

This course will involve some conceptual/mathematical material.

About the instructors

Alessandro Vespignani

Alessandro Vespignani received his undergraduate degree and Ph.D., both in physics and both from the University of Rome “La Sapienza,” in 1990 and 1994 respectively. He completed his postdoctoral research at Yale University and Leiden University. Prof. Vespignani worked at the International Center for Theoretical Physics (UNESCO) in Trieste and at the University of Paris-Sud in France as a member of the National Council for Scientific Research (CNRS) before moving to Indiana University in 2004. Before joining Northeastern University Vespignani was J.H.Rudy Professor of Informatics and Computing at Indiana University and serving as the Director of the Center for Complex Networks and Systems Research and the Associate Director of the Pervasive Technology Institute. Vespignani is elected fellow of the American Physical Society, member of the Academy of Europe, and fellow of the Institute for Quantitative Social Sciences at Harvard University. He has received the Honorary Doctorate from the Technical University of Delft, the Netherlands, and the 2016 Aspen institute Italia award. He is serving in the board/leadership of a variety of professional association and journals and the Institute for Scientific Interchange Foundation.

Vespignani has worked in a number of areas of non-equilibrium particle systems, statistical physics and computational sciences, including characterization of non-equilibrium phase transitions, fractal growth and self-organized criticality. Recently Vespignani’s research activity focuses on the interdisciplinary application of statistical and numerical simulation methods in the analysis of epidemic and spreading phenomena and the study of biological, social and technological networks. For several years he has been working on the characterization and modeling of the Internet, the WWW and large-scale information networks. He is now focusing his research activity in modeling the spatial spread of epidemics, including the realistic and data-driven computational modeling of emerging infectious diseases, the resilience of complex networks and the behavior of techno-social systems.

Yamir Moreno

Yamir Moreno got his PhD in Physics (Summa Cum Laude, 2000) from the University of Zaragoza. He is the Director of the Institute for Biocomputation and Physics of Complex Systems (BIFI), the head of the Complex Systems and Networks Lab (COSNET) and Professor of Physics at the Department of Theoretical Physics of the Faculty of Sciences, University of Zaragoza. Prof. Moreno is also an External Professor of the Complexity Science Hub Vienna, Austria.

Prof. Moreno is the elected President of the Network Science Society and was the President of the Complex Systems Society from 2015 to 2018. His field of research is in the theoretical foundations of complex systems, which he investigates using tools from mathematics, physics and network science. Pro. Moreno is a world expert on disease dynamics, diffusion processes, mathematical biology, nonlinear dynamical processes and the structure and dynamics of complex systems. He has published more than 210 scientific papers with a total of 17800+ citations and h-index=54 (ISI WoK) or 29300+ and 64 (Google Scholar), including the most cited Physics Reports (Complex Networks and their applications, Phys. Rep. 424, 175-304 (2006), 9000+ citations). At present, Prof. Moreno is a Divisional Associate Editor of Physical Review Letters, Editor of the New Journal of Physics; Chaos, Solitons and Fractals; and Journal of Complex Networks; an Academic Editor of PLoS ONE, and a member of the Editorial Boards of Scientific Reports, Applied Network Science, and Frontiers in Physics.

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