Exploring contact patterns among students via social network analysis: A cohort study in İstanbul, Türkiye

Abstract

This study explores the dynamics of social interactions among middle school students using Social Network Analysis (SNA) and ultra-wide band (UWB) technology embedded in sensor cards. Conducted in a Turkish middle school, this research aimed to capture and analyze the social mixing patterns of students across three grades on separate days in March 2023. The results have significant implications for understanding disease transmission and developing effective public health interventions in school settings. SNA, which integrates sociological theories and mathematical principles from graph theory is employed in this context to understand how interactions between individuals influence social phenomena, such as cognitive processes, feelings, and behaviors. It also seeks to uncover the patterns of information, influence, and infection flow. In our study, the goal is to understand how infection spreads among students of a middle-school given the significant role children play in transmission of airborne diseases when they commute between home and school and interact with a number of peers. The data collection utilized lightweight, credit-card-sized wireless sensor devices to record close proximity interactions among students and giving us details of those contacts including who interacts with whom and for how long. These UWB-based devices are known for their high accuracy in contact tracing and were configured to detect face-to-face contacts within a 1.5-meter range. The study was set in a co-educational public middle school in Istanbul. Each student, along with their teachers and staff, was equipped with a sensor card that recorded face-to-face contacts throughout the school day. The data was collected over three separate days, each dedicated to a specific grade: 5th, 6th, and 7th grades. On each day, students were given sensor cards at the beginning of the school day, and these were collected before the last class ended. The collected data captured interactions during class times and break times, providing a comprehensive picture of the students' social networks. The context of the study is particularly significant given the recent global COVID-19 pandemic in addition to previous outbreaks that claimed a lot of lives and left detrimental impacts on public health systems and economies worldwide, thus highlighting the critical need for effective contact tracing and understanding social interactions to predict how pathogens spread and eventually prevent disease spread. Schools, being high-density environments where close contact is frequent, are prime locations for such studies. By focusing on these settings, the research aims to provide insights that can inform public health policies and interventions, such as school closings, social distancing, masking, and vaccination strategies. The contact data was analyzed to construct a network of interactions among students. Various metrics such as degree (number of contacts per student), density (ratio of actual contacts to possible contacts), clustering coefficient (degree of interconnectedness among a student's contacts), and shortest path (minimum number of intermediary nodes connecting two individuals) were calculated to understand the cohesiveness and connectedness of the network. Other data analysis methods namely calculating the distribution of contact durations and degree values were also applied to gain insight on the likelihood of transmission events, and informing disease models with the related parameters. The results obtained from this study are listed as follows: • It was found that older students tended to form more interconnected groups with stronger ties across different classroom communities. • It was determined that contact durations were short, with most interactions lasting less than a minute. • The patterns suggest that while younger students have more frequent contacts, these interactions are generally brief. The study also identified the potential for different contact durations to influence infection spread, emphasizing the need for further research on proximity distances and their effects on network dynamics. By focusing on environments with high population density and connectivity, such as schools, valuable insights can be gained about the potential effects and dynamics of virus transmission. Lastly, this thesis provides valuable insights into the social interactions of middle school students, with implications for designing effective public health interventions and improving our understanding of disease transmission in school settings. The results underscore the need for targeted strategies to manage infectious diseases, particularly in educational environments where close contact is frequent. The findings also highlight the importance of using advanced technologies like UWB sensors to gather accurate data on social interactions, which can inform more precise and effective public health measures.