The spread of infectious diseases and the flow of ideas and information through populations fundamentally depend on the complex structure of the underlying network of interactions between individuals. Disease ecologists and sociologists have historically studied the dynamics of contagion using models that assume very simple population structures. Recently, however, network modeling has revolutionized both fields by enabling the rigorous exploration of the relationship between complex individual-level behavior and the higher-level emergence of outbreaks. The field draws on advanced statistical tools for inferring network structure from often limited data, data-driven algorithms for generating realistic network structures, and mathematical approximations for predicting transmission dynamics that draw from the methods of percolation theory and other fields within statistical physics.
While network models are more complex than their mass-action predecessors, they are remarkably tractable, often reducing to low-dimensional descriptions and allowing straightforward calculations of the dynamics of contagion. The fields of infectious disease epidemiology and sociology are simultaneously experiencing an explosion of computationally-intensive agent-based simulation models, that allow much higher-resolution representations of populations but often preclude comprehensive analysis. Selecting among the diversity of modeling approaches is non-trivial, and may be highly dependent on the system and the questions.
This workshop will focus on network models for biological and social contagion, and how they compare to alternative approaches. It will address the challenges of inferring network structure from sociological and/or epidemiological data, understanding the emergence of such network structure from simple individual-level behavior, and predicting the dynamics of contagion from simple characterizations of the underlying network.