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.

Highlights

  • Age-specific social mixing patterns are important in shaping the spread of infectious disease, from pandemic influenza [1, 2] to varicella and parvovirus [3]

  • As well as measuring contacts using proximity sensors such as radio-frequency identification (RFID) tags [4,5,6], self reported social contacts can be collected via routine questionnaires in different settings [7, 8]

  • As in previous studies [23, 31], the research was embedded within a public engagement project, with students and teachers in participating schools contributing to the study design and data collection

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Summary

Introduction

Age-specific social mixing patterns are important in shaping the spread of infectious disease, from pandemic influenza [1, 2] to varicella and parvovirus [3]. As well as measuring contacts using proximity sensors such as radio-frequency identification (RFID) tags [4,5,6], self reported social contacts can be collected via routine questionnaires in different settings [7, 8]. Such data is commonly used to parameterise mathematical models of infectious diseases [9]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

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