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Social networks and infectious disease: The Colorado Springs study

Author

Listed:
  • Klovdahl, A.S.
  • Potterat, J.J.
  • Woodhouse, D.E.
  • Muth, J.B.
  • Muth, S.Q.
  • Darrow, W.W.

Abstract

The social network paradigm provides a set of concepts and methods useful for studying the structure of a population through which infectious agents transmitted during close personal contact spread, and an opportunity to develop improved disease control programs. The research discussed was a first attempt to use a social network approach to better understand factors affecting the transmission of a variety of pathogens, including hepatitis B virus (HBV) and human immunodeficiency viruses (HIV), in population of prostitutes, injecting drug users (IDU) and their personal associates in a moderate-sized city (Colorado Springs, CO). Some of the challenges of studying large social networks in epidemiological research are described, some initial results reported and a new view of interconnections in an at risk population provided. Overall, for the first time in epidemiologic research a large number of individuals (over 600) were found connected to each other, directly or indirectly, using a network design. The average distance (along observed social relationships) between persons infected with HIV and susceptible persons was about three steps (3.1) in the core network region. All susceptibles in the core were within seven steps of HIV infection.

Suggested Citation

  • Klovdahl, A.S. & Potterat, J.J. & Woodhouse, D.E. & Muth, J.B. & Muth, S.Q. & Darrow, W.W., 1994. "Social networks and infectious disease: The Colorado Springs study," Social Science & Medicine, Elsevier, vol. 38(1), pages 79-88, January.
    • Handle: RePEc:eee:socmed:v:38:y:1994:i:1:p:79-88
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    Citations

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    Cited by:

    1. Matt J Keeling & Thomas House & Alison J Cooper & Lorenzo Pellis, 2016. "Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-18, December.
    2. Stephane Helleringer & Hans-Peter Kohler & Agnes Chimbiri & Praise Chatonda & James Mkandawire, 2009. "The Likoma Network Study: Context, data collection and initial results," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 21(15), pages 427-468.
    3. Lichoti, Jacqueline K. & Davies, Jocelyn & Okoth, Edward & Maru, Yiheyis & Bishop, Richard, 2013. "Insights from social network analysis are helping to build understanding of African Swine Fever epidemiology," 2013 Fourth International Conference, September 22-25, 2013, Hammamet, Tunisia 159704, African Association of Agricultural Economists (AAAE).
    4. Papachristos, Andrew V. & Wildeman, Christopher & Roberto, Elizabeth, 2015. "Tragic, but not random: The social contagion of nonfatal gunshot injuries," Social Science & Medicine, Elsevier, vol. 125(C), pages 139-150.
    5. Samuel F Rosenblatt & Jeffrey A Smith & G Robin Gauthier & Laurent Hébert-Dufresne, 2020. "Immunization strategies in networks with missing data," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-21, July.
    6. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    7. Berry, George & Cameron, Christopher John, 2017. "A new method to reduce overestimation of thresholds with observational network data," SocArXiv ctjd6, Center for Open Science.
    8. Jonas, Adam B. & Young, April M. & Oser, Carrie B. & Leukefeld, Carl G. & Havens, Jennifer R., 2012. "OxyContin® as currency: OxyContin® use and increased social capital among rural Appalachian drug users," Social Science & Medicine, Elsevier, vol. 74(10), pages 1602-1609.
    9. Hollm-Delgado, Maria-Graciela, 2009. "Molecular epidemiology of tuberculosis transmission: Contextualizing the evidence through social network theory," Social Science & Medicine, Elsevier, vol. 69(5), pages 747-753, September.
    10. Benjamin Armbruster & Margaret Brandeau, 2007. "Contact tracing to control infectious disease: when enough is enough," Health Care Management Science, Springer, vol. 10(4), pages 341-355, December.
    11. Kyle Vincent & Steve Thompson, 2017. "Estimating Population Size With Link-Tracing Sampling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1286-1295, July.
    12. John Roberts & Devon Brewer, 2001. "Measures and tests of heaping in discrete quantitative distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(7), pages 887-896.
    13. Tyler H. McCormick & Tian Zheng, 2015. "Latent Surface Models for Networks Using Aggregated Relational Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1684-1695, December.
    14. Petter Holme & Nelly Litvak, 2017. "Cost-efficient vaccination protocols for network epidemiology," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-18, September.

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