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Evolving infectious disease dynamics shape school-based intervention effectiveness

Author

Listed:
  • Javier Perez-Saez

    (Geneva University Hospitals
    Geneva University Hospitals and University of Geneva
    Johns Hopkins Bloomberg School of Public Health)

  • Mathilde Bellon

    (Geneva University Hospitals and University of Geneva
    University of Geneva)

  • Justin Lessler

    (Johns Hopkins Bloomberg School of Public Health
    University of North Carolina at Chapel Hill
    University of North Carolina at Chapel Hill)

  • Julie Berthelot

    (Geneva University Hospitals)

  • Emma B. Hodcroft

    (University of Bern
    Swiss Institute of Bioinformatics
    University of Bern)

  • Grégoire Michielin

    (Ecole Polytechnique Fédérale de Lausanne)

  • Francesco Pennacchio

    (Geneva University Hospitals)

  • Julien Lamour

    (Geneva University Hospitals)

  • Florian Laubscher

    (Geneva University Hospitals)

  • Arnaud G. L’Huillier

    (Geneva University Hospitals
    Geneva University Hospitals
    Faculty of Medicine, University of Geneva)

  • Klara M. Posfay-Barbe

    (Faculty of Medicine, University of Geneva)

  • Sebastian J. Maerkl

    (Ecole Polytechnique Fédérale de Lausanne)

  • Idris Guessous

    (Geneva University Hospitals
    University of Geneva)

  • Andrew S. Azman

    (Geneva University Hospitals and University of Geneva
    Johns Hopkins Bloomberg School of Public Health
    Geneva University Hospitals)

  • Isabella Eckerle

    (Geneva University Hospitals and University of Geneva
    University of Geneva)

  • Silvia Stringhini

    (Geneva University Hospitals
    University of Geneva
    University f British Columbia)

  • Elsa Lorthe

    (Geneva University Hospitals
    Université Paris Cité, Inserm, INRAE, Centre for Research in Epidemiology and Statistics Paris (CRESS)
    HES-SO University of Applied Sciences and Arts Western Switzerland)

Abstract

School-based interventions during epidemics are often controversial, as experienced during the COVID-19 pandemic, where reducing transmission had to be weighed against the adverse effects on young children. However, it remains unclear how the broader epidemiologic context influences the effectiveness of these interventions and when they should be implemented. Through integrated modeling of epidemiological and genetic data from a longitudinal school-based surveillance study of SARS-CoV-2 in 2021–2022 (N children = 336, N adults = 51) and scenario simulations, we show how transmission dynamics in schools changed markedly due to strong increases in community-acquired infections in successive periods of viral variants, ultimately undermining the potential impact of school-based interventions in reducing infection rates in the school-aged population. With pandemic preparedness in mind, this study advocates for a dynamic perspective on the role and importance of schools in infectious disease control, one that adapts to the evolving epidemiological landscape shaped by pathogen characteristics and evolution, shifting public health policies, and changes in human behavior.

Suggested Citation

  • Javier Perez-Saez & Mathilde Bellon & Justin Lessler & Julie Berthelot & Emma B. Hodcroft & Grégoire Michielin & Francesco Pennacchio & Julien Lamour & Florian Laubscher & Arnaud G. L’Huillier & Klara, 2025. "Evolving infectious disease dynamics shape school-based intervention effectiveness," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-61925-5
    DOI: 10.1038/s41467-025-61925-5
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    References listed on IDEAS

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    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Emily Howerton & Lucie Contamin & Luke C. Mullany & Michelle Qin & Nicholas G. Reich & Samantha Bents & Rebecca K. Borchering & Sung-mok Jung & Sara L. Loo & Claire P. Smith & John Levander & Jessica , 2023. "Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    3. Ganna Rozhnova & Christiaan H. Dorp & Patricia Bruijning-Verhagen & Martin C. J. Bootsma & Janneke H. H. M. Wijgert & Marc J. M. Bonten & Mirjam E. Kretzschmar, 2021. "Model-based evaluation of school- and non-school-related measures to control the COVID-19 pandemic," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    4. Clodomir Santana & Federico Botta & Hugo Barbosa & Filippo Privitera & Ronaldo Menezes & Riccardo Di Clemente, 2023. "COVID-19 is linked to changes in the time–space dimension of human mobility," Nature Human Behaviour, Nature, vol. 7(10), pages 1729-1739, October.
    5. Jeffrey D Sachs & Salim S Abdool Karim & Lara Aknin & Joseph Allen & Kirsten Brosbol & Francesca Colombo & Gabriela Cuevas Barron & Maria Fernanda Espinosa & Vitor Gaspar & Alejandro Gaviria & Andy Ha, 2022. "The Lancet Commission on lessons for the future from the COVID-19 pandemic," DEOS Working Papers 2226, Athens University of Economics and Business.
    6. Simon Cauchemez & Alain-Jacques Valleron & Pierre-Yves Boëlle & Antoine Flahault & Neil M. Ferguson, 2008. "Estimating the impact of school closure on influenza transmission from Sentinel data," Nature, Nature, vol. 452(7188), pages 750-754, April.
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