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BMC Infect Dis. 2015 Nov 14;15:522. doi: 10.1186/s12879-015-1250-z.

Tracking social contact networks with online respondent-driven detection: who recruits whom?

Author information

1
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands. M.L.Stein-2@umcutrecht.nl.
2
Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands. M.L.Stein-2@umcutrecht.nl.
3
Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, University Utrecht, Utrecht, The Netherlands. P.G.M.vanderHeijden@uu.nl.
4
Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, UK. P.G.M.vanderHeijden@uu.nl.
5
Department of Sociology, Faculty of Social and Behavioural Sciences, University Utrecht, Utrecht, The Netherlands. V.Buskens@uu.nl.
6
Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands. Jim.van.Steenbergen@rivm.nl.
7
Centre of Infectious Diseases, Leiden University Medical Centre, Leiden, The Netherlands. Jim.van.Steenbergen@rivm.nl.
8
Department of Public Health Sciences-Global Health, Karolinska Institutet, Stockholm, Sweden. Linus.Bengtsson@ki.se.
9
Flowminder Foundation, Stockholm, Sweden. Linus.Bengtsson@ki.se.
10
Science in Action BV, Amsterdam, The Netherlands. Carl@science-in-action.nl.
11
Department of Public Health Sciences-Global Health, Karolinska Institutet, Stockholm, Sweden. Anna.Thorson@ki.se.
12
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands. M.E.E.Kretzschmar@umcutrecht.nl.
13
Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands. M.E.E.Kretzschmar@umcutrecht.nl.

Abstract

BACKGROUND:

Transmission of respiratory pathogens in a population depends on the contact network patterns of individuals. To accurately understand and explain epidemic behaviour information on contact networks is required, but only limited empirical data is available. Online respondent-driven detection can provide relevant epidemiological data on numbers of contact persons and dynamics of contacts between pairs of individuals. We aimed to analyse contact networks with respect to sociodemographic and geographical characteristics, vaccine-induced immunity and self-reported symptoms.

METHODS:

In 2014, volunteers from two large participatory surveillance panels in the Netherlands and Belgium were invited for a survey. Participants were asked to record numbers of contacts at different locations and self-reported influenza-like-illness symptoms, and to invite 4 individuals they had met face to face in the preceding 2 weeks. We calculated correlations between linked individuals to investigate mixing patterns.

RESULTS:

In total 1560 individuals completed the survey who reported in total 30591 contact persons; 488 recruiter-recruit pairs were analysed. Recruitment was assortative by age, education, household size, influenza vaccination status and sentiments, indicating that participants tended to recruit contact persons similar to themselves. We also found assortative recruitment by symptoms, reaffirming our objective of sampling contact persons whom a participant may infect or by whom a participant may get infected in case of an outbreak. Recruitment was random by sex and numbers of contact persons. Relationships between pairs were influenced by the spatial distribution of peer recruitment.

CONCLUSIONS:

Although complex mechanisms influence online peer recruitment, the observed statistical relationships reflected the observed contact network patterns in the general population relevant for the transmission of respiratory pathogens. This provides useful and innovative input for predictive epidemic models relying on network information.

PMID:
26573658
PMCID:
PMC4647802
DOI:
10.1186/s12879-015-1250-z
[Indexed for MEDLINE]
Free PMC Article

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