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Structure and consistency of self-reported social contact networks in British secondary schools
Adam J Kucharski1, Clare Wenham1,2, Polly Brownlee3
1Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Insights
Understanding social mixing in schools is key for infectious disease modeling. This study shows longitudinal contact network data from UK secondary school children is feasible and consistent over time.
Area of Science:
- Epidemiology
- Network Science
- Mathematical Biology
Background:
- Self-reported social mixing patterns are crucial for infectious disease mathematical modeling.
- Quantifying social interactions in school-aged children is vital due to their significant role in disease transmission.
- Longitudinal changes in school-aged children's social interaction structures require further investigation.
Purpose of the Study:
- To examine self-reported social contact networks in UK secondary school children over time.
- To assess the feasibility and consistency of collecting longitudinal social contact data in schools.
- To analyze the structural properties of social networks among adolescents.
Main Methods:
- Integrated data collection into a public engagement program across four UK secondary schools.
- Collected data from 460 unique participants across four rounds between January and June 2015.
- Analyzed 7,315 reported contacts and network properties like clustering and community structure.
Main Results:
- Achieved over 90% out-of-sample accuracy in predicting social contacts between individuals across data collection rounds.
- Network properties (clustering, communities) were consistent within schools but varied significantly between schools.
- Social networks exhibited assortativity by gender and school class, with higher clustering among males in co-educational settings.
Conclusions:
- Collecting longitudinal self-reported social contact data from school children is feasible.
- Key properties of these social contact networks demonstrate consistency between data collection rounds.
- Findings support the use of such data for improving infectious disease models in school settings.
Abstract:
Self-reported social mixing patterns are commonly used in mathematical models of infectious diseases. It is particularly important to quantify patterns for school-age children given their disproportionate role in transmission, but it remains unclear how the structure of such social interactions changes over time. By integrating data collection into a public engagement programme, we examined self-reported contact networks in year 7 groups in four UK secondary schools. We collected data from 460 unique participants across four rounds of data collection conducted between January and June 2015, with 7,315 identifiable contacts reported in total. Although individual-level contacts varied over the study period, we were able to obtain out-of-sample accuracies of more than 90% and F-scores of 0.49-0.84 when predicting the presence or absence of social contacts between specific individuals across rounds of data collection. Network properties such as clustering and number of communities were broadly consistent within schools between survey rounds, but varied significantly between schools. Networks were assortative according to gender, and to a lesser extent school class, with the estimated clustering coefficient larger among males in all surveyed co-educational schools. Our results demonstrate that it is feasible to collect longitudinal self-reported social contact data from school children and that key properties of these data are consistent between rounds of data collection.
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