Abstract
Identification of individuals or subpopulations that contribute the most to disease transmission is key to target surveillance and control efforts. In a recent study in BMC Medicine, Smieszek and Salathé introduced a novel method based on readily available information about spatial proximity in high schools, to help identify individuals at higher risk of infection and those more likely to be infected early in the outbreak. By combining simulation models for influenza transmission with high-resolution data on school contact patterns, the authors showed that their proximity method compares favorably to more sophisticated methods using detailed contact tracing information. The proximity method is simple and promising, but further research is warranted to confront this method against real influenza outbreak data, and to assess the generalizability of the approach to other important transmission units, such as work, households, and transportation systems.See related research article here http://www.biomedcentral.com/1741-7015/11/35.
Original language | English (US) |
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Article number | 36 |
Journal | BMC Medicine |
Volume | 11 |
Issue number | 1 |
DOIs |
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State | Published - Feb 12 2013 |
Keywords
- class schedule
- collocation ranking
- contact network
- contact pattern
- disease transmission.
- dynamic network
- high school
- hotspot
- influenza
- wireless sensing devices
ASJC Scopus subject areas
- General Medicine