Duke RDS^2: Respondent-driven sampling for respiratory disease surveillance, the snowball sampling study (social mixing and referrals dataset)

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  • Community mixing patterns by sociodemographic traits can inform the risk of epidemic spread among groups, and the balance of in- and out-group mixing affects epidemic potential. Understanding mixing patterns can provide insight about potential transmission pathways throughout a community. We used a snowball sampling design to enroll people recently diagnosed with SARS-CoV-2 in an ethnically and racially diverse county and asked them to describe their close contacts and recruit some contacts to enroll in the study. We constructed egocentric networks of the participants and their contacts and assessed age-mixing, ethnic/racial homophily, and gender homophily. The total size of the egocentric networks was 2,544 people (n=384 index cases + n=2,160 recruited peers or other contacts). We observed high rates of in-group mixing among ethnic/racial groups compared to the ethnic/racial proportions of the background population. Black or African-American respondents interacted with a wider range of ages than other ethnic/racial groups, largely due to familial relationships. The egocentric networks of non-binary contacts had little age diversity. Black or African-American respondents in particular reported mixing with older or younger family members, which could increase the risk of transmission to vulnerable age groups. Understanding community mixing patterns can inform infectious disease risk, support analyses to predict epidemic size, or be used to design campaigns such as vaccination strategies so that community members who have vulnerable contacts are prioritized.

    The project described was supported by Grant/Cooperative Agreement Number 75D30120C09551 made to Duke University from the Centers for Disease Control and Prevention (CDC), US Department of Health and Human Services (HHS), awarded to D.K.P., J.M., and K. B.-E. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC or US HHS. D.K.P. and J.M. were also supported by the National Institutes of Health (NIH) Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (Grant Awards R25HD079352 (awarded to J.M.), R21HD104431 (awarded to J.M. and D.K.P.), and R21HD101268 (awarded to J.M. and D.K.P)) and National Science Foundation (Grant Award SES-2029790 (awarded to J.M. and D.K.P)). The contents are solely the responsibility of the authors and do not necessarily represent the official views of NIH or NSF. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.     ... [Read More]

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2 files (190 KB)
Data Citation
  • Pasquale, D. K., Moody, J., Bentley-Edwards, K. Welsh, W., & Olson, A. (2024) Duke RDS^2: Respondent-driven sampling for respiratory disease surveillance, the snowball sampling study (social mixing and referrals dataset). Duke Research Data Repository. https://doi.org/10.7924/r43f4zj2q
DOI
  • 10.7924/r43f4zj2q
Publication Date
ARK
  • ark:/87924/r43f4zj2q
Collection Dates
  • December 2020 to July 2022
Location
  • Durham, North Carolina, United States
Language
Type
Format
Related Materials
  • Manuscript under review
Funding Agency
  • National Institutes of Health
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development
  • Centers for Disease Control and Prevention
  • National Science Foundation
Grant Number
  • 75D30120C09551
Title
  • Duke RDS^2: Respondent-driven sampling for respiratory disease surveillance, the snowball sampling study (social mixing and referrals dataset)
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file details Snowball_socialMixing_openData.pdf 2024-03-25 Public Download
file details Snowball_socialMixing_openData.csv 2024-03-25 Public Download

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