Online respondent-driven sampling for studying contact patterns relevant for the spread of close-contact pathogens: a pilot study in Thailand

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

Number of recorded contacts by different characteristics.

<p>^a Most seeds provided a postal code from a district far away from Bangkok, however we assume that most of these students stayed in a student dorm in Bangkok during the study week.</p

Correlations between directly linked individuals in one component.

<p>^a One case who reported >500 household contacts was removed.</p><p>^b Based on total number of self-reported symptoms by each respondent.</p

RDS recruitment.

<p>(<b>A</b>) RDS network trees showing age, gender and educational level of respondents. Only trees with two or more participants were displayed (in total 44 trees), symbols with black borders indicate seeds. (<b>B</b>) Cumulative number of participants and seeds (those who filled in the survey) over length of time the survey was active (in days), since the survey launch. The cumulative number of respondents is indicated by the red solid line, and participating seeds with the purple dotted line. Initially 44 students were approached, after 25 days more students were approached with an invitation email.</p

Number of successful recruitments by recruitment option.

<p>Successful recruitment (of 1 to max. 4 contacts) counts when the invited contact also completed the survey; 0 (‘no’) indicates that recruiter invited his/her contacts but these contacts did not complete the survey. 75 respondents did not invite anyone after filling the survey.</p

Correlations between any two individuals with different link distance.

<p>With graph <b>A</b> showing the correlations for age, gender, and education. Graph <b>B</b> displays the correlations in degree, number of contacts while having food, and household size (all after log transformation). Graph <b>C</b> shows the correlations for total number of self-reported symptoms (after log transformation), and for two or more self-reported symptoms (yes/no). Distances of five or more links were lumped together. The dotted lines show the confidence intervals.</p

Correlations between recruiter and recruited contact.

<p>Graphs display correlations for (<b>A</b>) age, (<b>B</b>) degree (untransformed). Overlapping points were made visible with a colour scale.</p

Recorded contacts.

<p>(<b>A</b>) Distribution of reported degree, the line indicates the fitted negative binomial distribution; (<b>B</b>) degree by day of the week (outliers >200 are not shown); (<b>C</b>) contacts while travelling with mass transport (sky train, subway and/or airplane), bus/minibus/shuttle boot, car/taxi and/or motorbike/tuk-tuk (outliers >40 are not shown); (<b>D</b>) numbers of contacts at different locations (outliers >80 are not shown). School was defined as ‘school/university’, ‘restaurant’ includes contacts at coffee shop, and ‘other’ is the sum of contacts encountered at sport/leisure, concert and ‘other places’. Above each plot in B, C and D the mean and SD (in blue) are displayed and within each plot the median (in red); B also contains the number of observations. Plots in C and D are based on an equal number of observations (n = 221).</p