Epidemic processes in complex networks

TL;DR

This paper reviews the complex dynamics of epidemic spreading in heterogeneous networks, highlighting theoretical frameworks, key results, and recent advances in modeling coevolving and time-varying networks.

Contribution

It provides a comprehensive review of epidemic processes in complex networks, including new theoretical approaches and insights into dynamic and coevolving network structures.

Findings

Successful analytical frameworks for epidemic spreading in heterogeneous networks

Limitations and assumptions of current models are clarified

Recent advances include studies on coevolving and time-varying networks

Abstract

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.