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Epidemic potential by sexual activity distributions

Published online by Cambridge University Press:  24 April 2017

JAMES MOODY ,
JIMI ADAMS  and
MARTINA MORRIS
JAMES MOODY
Affiliation:
Department of Sociology, Duke University, Durham, NC, USA Department of Sociology, King Abdulaziz University, Jedda, Saudi Arabia (e-mail: [email protected])
JIMI ADAMS
Affiliation:
Department of Health and Behavioral Sciences, University of Colorado Denver, USA (e-mail: [email protected])
MARTINA MORRIS
Affiliation:
Departments of Statistics and Sociology, University of Washington, Seattle, WA, USA (e-mail: [email protected])
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Abstract

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 the degree 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.

Keywords

sexually transmitted infectionsdegree distributionssimulationcohesionconnectivitydynamic network diffusion
Type
Research Article
Information
Network Science , Volume 5 , Issue 4 , December 2017 , pp. 461 - 475
Copyright
Copyright © Cambridge University Press 2017 

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