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.
journal_name
Am J Epidemioljournal_title
American journal of epidemiologyauthors
Christley RM,Pinchbeck GL,Bowers RG,Clancy D,French NP,Bennett R,Turner Jdoi
10.1093/aje/kwi308subject
Has Abstractpub_date
2005-11-15 00:00:00pages
1024-31issue
10eissn
0002-9262issn
1476-6256pii
kwi308journal_volume
162pub_type
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