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Online Respondent-Driven Sampling for Studying Contact Patterns Relevant for the Spread of Close-Contact Pathogens: A Pilot Study in Thailand

Author

Listed:
  • 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

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.

Suggested Citation

  • 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, 2014. "Online Respondent-Driven Sampling for Studying Contact Patterns Relevant for the Spread of Close-Contact Pathogens: A Pilot Study in Thailand," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    • Handle: RePEc:plo:pone00:0085256
    DOI: 10.1371/journal.pone.0085256
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    References listed on IDEAS

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    1. Linus Bengtsson & Xin Lu & Quoc Cuong Nguyen & Martin Camitz & Nguyen Le Hoang & Tuan Anh Nguyen & Fredrik Liljeros & Anna Thorson, 2012. "Implementation of Web-Based Respondent-Driven Sampling among Men Who Have Sex with Men in Vietnam," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-8, November.
    2. van der Weijden, Charlie P. & Stein, Mart L. & Jacobi, André J. & Kretzschmar, Mirjam E.E. & Reintjes, Ralf & van Steenbergen, Jim E. & Timen, Aura, 2013. "Choosing pandemic parameters for pandemic preparedness planning: A comparison of pandemic scenarios prior to and following the influenza A(H1N1) 2009 pandemic," Health Policy, Elsevier, vol. 109(1), pages 52-62.
    3. Erik M Volz & Joel C Miller & Alison Galvani & Lauren Ancel Meyers, 2011. "Effects of Heterogeneous and Clustered Contact Patterns on Infectious Disease Dynamics," PLOS Computational Biology, Public Library of Science, vol. 7(6), pages 1-13, June.
    4. Kirsty J Bolton & James M McCaw & Kristian Forbes & Paula Nathan & Garry Robins & Philippa Pattison & Terry Nolan & Jodie McVernon, 2012. "Influence of Contact Definitions in Assessment of the Relative Importance of Social Settings in Disease Transmission Risk," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-8, February.
    5. Marcel Salathé & James H Jones, 2010. "Dynamics and Control of Diseases in Networks with Community Structure," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-11, April.
    6. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    7. Ken T D Eames & Natasha L Tilston & Ellen Brooks-Pollock & W John Edmunds, 2012. "Measured Dynamic Social Contact Patterns Explain the Spread of H1N1v Influenza," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-8, March.
    8. Joël Mossong & Niel Hens & Mark Jit & Philippe Beutels & Kari Auranen & Rafael Mikolajczyk & Marco Massari & Stefania Salmaso & Gianpaolo Scalia Tomba & Jacco Wallinga & Janneke Heijne & Malgorzata Sa, 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 5(3), pages 1-1, March.
    9. Neil M. Ferguson & Derek A.T. Cummings & Simon Cauchemez & Christophe Fraser & Steven Riley & Aronrag Meeyai & Sopon Iamsirithaworn & Donald S. Burke, 2005. "Strategies for containing an emerging influenza pandemic in Southeast Asia," Nature, Nature, vol. 437(7056), pages 209-214, September.
    10. Mirjam Kretzschmar & Rafael T Mikolajczyk, 2009. "Contact Profiles in Eight European Countries and Implications for Modelling the Spread of Airborne Infectious Diseases," PLOS ONE, Public Library of Science, vol. 4(6), pages 1-8, June.
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    1. Praveen Chansrichavala & Nittayasee Wongsuwan & Suthee Suddee & Mayura Malasit & Maliwan Hongsuwan & Prapass Wannapinij & Rungreung Kitphati & Nicholas P J Day & Susan Michie & Sharon J Peacock & Dire, 2015. "Public Awareness of Melioidosis in Thailand and Potential Use of Video Clips as Educational Tools," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-13, March.

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