Epidemic potential by sexual activity distributions.
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For sexually transmitted infections like HIV to propagate through a population, there must be a path linking susceptible cases to currently infectious cases. The existence of such paths depends in part on thedegree distribution.Here, we use simulation methods to examine how two features of the degree distribution affect network connectivity: Mean degree captures a volume dimension, while the skewness of the upper tail captures a shape dimension. We find a clear interaction between shape and volume: When mean degree is low, connectivity is greater for long-tailed distributions, but at higher mean degree, connectivity is greater in short-tailed distributions. The phase transition to a giant component and giant bicomponent emerges as a positive function of volume, but it rises more sharply and ultimately reaches more people in short-tail distributions than in long-tail distributions. These findings suggest that any interventions should be attuned to how practices affect both the volume and shape of the degree distribution, noting potential unanticipated effects. For example, policies that primarily affect high-volume nodes may not be effective if they simply redistribute volume among lower degree actors, which appears to exacerbate underlying network connectivity.
dynamic network diffusion
sexually transmitted infections
Published Version (Please cite this version)10.1017/nws.2017.3
Publication InfoAdams, J; Moody, James; & Morris, M (2017). Epidemic potential by sexual activity distributions. Netw Sci (Camb Univ Press), 5(4). pp. 461-475. 10.1017/nws.2017.3. Retrieved from http://hdl.handle.net/10161/16104.
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Professor in the Department of Sociology
James Moody is the Robert O. Keohane professor of sociology at Duke University. He has published extensively in the field of social networks, methods, and social theory. His work has focused theoretically on the network foundations of social cohesion and diffusion, with a particular emphasis on building tools and methods for understanding dynamic social networks. He has used network models to help understand school racial segregation, adolescent health, disease spread, economic development, a