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Social network-based cohorting to reduce the spread of SARS-CoV-2 in secondary schools: A simulation study in classrooms of four European countries

Mannheimer Zentrum für Europäische Sozialforschung (MZES); Universität Mannheim; Columbia University

Sociologists from the University of Mannheim and Columbia University recommend dividing classes according to the friendship networks of the students. They have published the first independently peer-reviewed study on the subject of group splitting, switching classes and Covid-19 in European schools.

  • Disziplin: Sozial, Bildung
  • Forschungsmethode: Quantitativ
  • Forschungsdesign: Sekundäranalyse
  • Erhebungsstatus: Erhebung abgeschlossen, Ergebnisse veröffentlicht, Daten zugänglich

Ziele der Studie

To safely operate schools in pandemic conditions, strategies that lower the risk of in-school infection are needed to reduce the spread of SARS-CoV-2. Specifically, decomposing the student population into smaller isolated units may reduce the risk of large infection clusters. While research on social distancing measures in schools is still scant, emerging evidence from modelling studies for schools in the US suggests that reducing group size can indeed help reduce infections.
The researchers of this study examined the effectiveness of different strategies to divide classrooms in curbing the spread of SARS-CoV-2 in European Schools.


They compare four cohorting strategies to a baseline scenario with undivided classrooms with considering random cohorting first, which randomly divides classrooms into two equally-sized cohorts. Unlike the remaining strategies, random cohorting does not account for students’ out-of-school contacts, so contacts that span cohorts can still serve as transmission channels. By contrast, the first network-based strategy splits cohorts by gender, exploiting strong gender segregation in adolescents’ networks, so that many resulting out-of-school contacts are within rather than between cohorts. This gender-split cohorting strategy is easy to implement, but cross-gender friendships or romantic relationships may undermine its efficiency. The second network-based strategy, optimized cohorting, explicitly uses information on students’ self-reported out-of-school contacts to form cohorts that minimize the number of cross-cohort contacts. By definition, this strategy produces the cleanest separation of cohorts and should thus be most effective in preventing cross-cohort infection. However, it requires teachers to know students’ out-of-school contact networks and optimize cohorts accordingly, and is thus hard to implement in practice. As a third network-based strategy, they therefore propose a network chain cohorting approach that uses an easy-to-implement in-class nomination procedure to approximate the optimization strategy. In this strategy, an initial student who is well-connected—such as a class representative—names all of her in-class out-of-school contacts, and the resulting set of students forms the basis for the first cohort. Subsequently, the listed out-of-school contacts name their out-of-school contacts, who also become members of the first cohort. The process continues until half of the classroom is allocated to the first cohort, and the remaining students form the second cohort.


Data can be requested from https://doi.org/10.4232/cils4eu.5656.3.3.0

For data access to be granted, a data access agreement has to be signed and a short research proposal has to be submitted and approved. Data are available for academic research and teaching only.