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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/33067
Title: Online respondent-driven sampling for studying contact patterns relevant for the spread of close-contact pathogens: A pilot study in Thailand
Authors: Mart L. Stein
Jim E. Van Steenbergen
Charnchudhi Chanyasanha
Mathuros Tipayamongkholgul
Vincent Buskens
Peter G.M. Van Der Heijden
Wasamon Sabaiwan
Linus Bengtsson
Xin Lu
Anna E. Thorson
Mirjam E.E. Kretzschmar
University Medical Center Utrecht
National Institute of Public Health and the Environment
Leiden University Medical Center - LUMC
Mahidol University
Utrecht University
University of Southampton
Chulalongkorn University
Karolinska Institutet
National University of Defense Technology
Keywords: Agricultural and Biological Sciences;Biochemistry, Genetics and Molecular Biology
Issue Date: 8-Jan-2014
Citation: PLoS ONE. Vol.9, No.1 (2014)
Abstract: Background: Information on social interactions is needed to understand the spread of airborne infections through a population. Previous studies mostly collected egocentric information of independent respondents with self-reported information about contacts. Respondent-driven sampling (RDS) is a sampling technique allowing respondents to recruit contacts from their social network. We explored the feasibility of webRDS for studying contact patterns relevant for the spread of respiratory pathogens. Materials and Methods: We developed a webRDS system for facilitating and tracking recruitment by Facebook and email. One-day diary surveys were conducted by applying webRDS among a convenience sample of Thai students. Students were asked to record numbers of contacts at different settings and self-reported influenza-like-illness symptoms, and to recruit four contacts whom they had met in the previous week. Contacts were asked to do the same to create a network tree of socially connected individuals. Correlations between linked individuals were analysed to investigate assortativity within networks. Results: We reached up to 6 waves of contacts of initial respondents, using only non-material incentives. Forty-four (23.0%) of the initially approached students recruited one or more contacts. In total 257 persons participated, of which 168 (65.4%) were recruited by others. Facebook was the most popular recruitment option (45.1%). Strong assortative mixing was seen by age, gender and education, indicating a tendency of respondents to connect to contacts with similar characteristics. Random mixing was seen by reported number of daily contacts. Conclusions: Despite methodological challenges (e.g. clustering among respondents and their contacts), applying RDS provides new insights in mixing patterns relevant for close-contact infections in real-world networks. Such information increases our knowledge of the transmission of respiratory infections within populations and can be used to improve existing modelling approaches. It is worthwhile to further develop and explore webRDS for the detection of clusters of respiratory symptoms in social networks. © 2014 Stein et al.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84896954261&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/33067
ISSN: 19326203
Appears in Collections:Scopus 2011-2015

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