Synopsis by Ells Campbell
In 1927, the study of infectious disease dynamics was revolutionized by the advent of a powerful quantitative tool called the susceptible - infected - recovered (SIR) model. Much of our scientific understanding of how infectious diseases spread through a population is firmly rooted in this paradigm. While the SIR model is still commonly used today, it focuses on individuals who mix randomly, assuming connections between individuals in a population have vanishingly short duration. However, individuals’ interactions are seldom random, tending to be structured into a social network. Further, the network connections are not static, but form and break or are maintained for durations that can range from vanishingly short to life-long.
Though modern simulations can and have been used to explore the effects of social structure on infectious disease, they are computationally expensive. In a recent publication, Joel C. Miller and colleagues shifted the perspective of the traditional SIR model from the individual to the connection. In their model, the type and duration of connections are considered. This conceptually simple change of perspective may revolutionize epidemiology, not because it introduces more complexity, but because it maintains analytical simplicity. Rather than performing large-scale computer simulations, researchers can evaluate the robustness of their predictions mathematically. Furthermore, the team’s approach lays a foundation for investigating infectious disease dynamics when social networks change in response to a spreading epidemic.
Written By: Miller JC, Slim AC, & Volz EM
Paper Url: http://18.104.22.168/content/9/70/890.short
Journal: 9: 890-906
Journal Reference: 9: 890-906
Paper Id: 10.1098/rsif.2011.0403