/1 Network Epidemiology: From simple to data-driven models
In this tutorial we will deal with a topic that has advanced enormously in the recent decades thanks to contribution of network science: the modeling of epidemics. We will begin by reviewing the building blocks of the broad field of theoretical epidemiology: the compartmental models. From this point, we will progressively add ingredients aimed at capturing the real patterns of connectivity (networks) and mobility (metapopulations) observed in real societies. Finally, after analyzing the behavior of these models from the theoretical point of view, we will address the current challenges of epidemics prediction and the design of containment strategies.
University of Zaragoza, Spain
Jesus Gomez-Gardeñes is Associate Professor and head of the Group of Theoretical and Applied Modeling (GOTHAM) at the Institute of Biocomputation and Physics of Complex Systems (BIFI) of the University of Zaragoza (Spain). His main fields of research are statistical physics, nonlinear dynamics and the theory of complex networks. Within these disciplines he has mainly focused in the study of the emergence of collective phenomena out of nonlinearity and the structure of interactions in complex systems. Along these lines he has studied some paradigmatic problems such as energy localization, synchronization, random walks, traffic congestion, disease propagation and evolutionary dynamics. He has authored more than 100 scientific articles in international journals, including Nature Physics, PNAS, Physical Review Letters, Physics Reports, Science Advances, Nature Human Behavior among others. In the recent years he has focused on the study of multilayer networks and network epidemiology.