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American Journal of Epidemiology Advance Access published online on September 21, 2005
American Journal of Epidemiology, doi:10.1093/aje/kwi308
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American Journal of Epidemiology Copyright © 2005 by the Johns Hopkins Bloomberg School of Public Health All rights reserved; printed in U.S.A.
Received December 21, 2004
Accepted June 7, 2005

PRACTICE OF EPIDEMIOLOGY

Infection in Social Networks: Using Network Analysis to Identify High Risk Individuals

R. M. Christley 1*, G. L. Pinchbeck 1, R. G. Bowers 2, D. Clancy 2, N. P. French 1, R. Bennett 2, and J. Turner 1

1 Epidemiology Group, Faculty of Veterinary Science, University of Liverpool, Liverpool, United Kingdom
2 Department of Mathematics, Faculty of Science, University of Liverpool, Liverpool, United Kingdom

* To whom correspondence should be addressed.
R. M. Christley, E-mail: robc{at}liverpool.ac.uk


  Abstract

Simulation studies using susceptible-infectious-recovered models were conducted to estimate individuals' risk of infection and time to infection in small-world and randomly mixing networks. Infection transmitted more rapidly but ultimately resulted in fewer infected individuals in the small-world, compared with the random, network. The ability of measures of network centrality to identify high-risk individuals was also assessed. "Centrality" describes an individual's position in a population; numerous parameters are available to assess this attribute. Here, the authors use the centrality measures degree (number of contacts), random-walk betweenness (a measure of the proportion of times an individual lies on the path between other individuals), shortest-path betweenness (the proportion of times an individual lies on the shortest path between other individuals), and farness (the sum of the number of steps between an individual and all other individuals). Each was associated with time to infection and risk of infection in the simulated outbreaks. In the networks examined, degree (which is the most readily measured) was at least as good as other network parameters in predicting risk of infection. Identification of more central individuals in populations may be used to inform surveillance and infection control strategies.

Keywords: disease outbreaks; disease transmission; infection; population surveillance.
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