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Background <p>The collection of updated post-COVID-19 data on social contacts is critical for future epidemiological assessment and evaluation of non-pharmaceutical interventions.</p> Methods <p>We conducted two waves of an online survey in Italy (March 2022 and March 2023), collecting representative data on direct (verbal/physical) and indirect (indoor co-location) contacts. Using a generalised linear mixed model, we analysed social contact determinants and the impact of work-from-home and distance learning on reducing a pathogen’s reproduction number (R). Additionally, we calibrated an age-structured model to the 2023–2024 influenza A epidemic in Italy to explore the impact of alternative in-person attendance scenarios on infection attack rates.</p> Results <p>We find that in-person attendance significantly increases contacts: adults attending in person have 1.69 times (95%CI: 1.55-1.83) more contacts than those staying home, while children/adolescents 2.36 (95%CI: 1.96-2.84). Limiting in-person work alone marginally affects R, whereas combining work-from-home with distance learning (from primary school onwards) reduces R by up to 23.2% (95%CI: 13.7-30.1%), with minimal additional benefit from suspending early childcare. In the influenza A case study, seasonal infection attack rates range from 14.7% (95%PI: 12.8–16.5%) under full in-person attendance to &lt;0.2% under the most restrictive scenario. Moderate interventions (suspension of tertiary education and work-from-home) reduce attack rates by up to one fourth among adults (15-64 years) and one sixth among older individuals.</p> Conclusions <p>This study provides post-pandemic contact matrices for Italy, essential for modelling transmission of respiratory pathogens, and quantitative evidence on the epidemiological impact of targeted physical distancing measures, thereby supporting future policy design.</p>

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Implications for distancing measures on in-person school and work attendance from Italian post-pandemic social contact data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月4日

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  • Lorenzo Lucchini,
  • Valentina Marziano,
  • Filippo Trentini,
  • Chiara Chiavenna,
  • Elena D’Agnese,
  • Vittoria Offeddu,
  • Mattia Manica,
  • Piero Poletti,
  • Duilio Balsamo,
  • Giorgio Guzzetta,
  • Marco Ajelli,
  • Alessia Melegaro,
  • Stefano Merler

摘要

Background

The collection of updated post-COVID-19 data on social contacts is critical for future epidemiological assessment and evaluation of non-pharmaceutical interventions.

Methods

We conducted two waves of an online survey in Italy (March 2022 and March 2023), collecting representative data on direct (verbal/physical) and indirect (indoor co-location) contacts. Using a generalised linear mixed model, we analysed social contact determinants and the impact of work-from-home and distance learning on reducing a pathogen’s reproduction number (R). Additionally, we calibrated an age-structured model to the 2023–2024 influenza A epidemic in Italy to explore the impact of alternative in-person attendance scenarios on infection attack rates.

Results

We find that in-person attendance significantly increases contacts: adults attending in person have 1.69 times (95%CI: 1.55-1.83) more contacts than those staying home, while children/adolescents 2.36 (95%CI: 1.96-2.84). Limiting in-person work alone marginally affects R, whereas combining work-from-home with distance learning (from primary school onwards) reduces R by up to 23.2% (95%CI: 13.7-30.1%), with minimal additional benefit from suspending early childcare. In the influenza A case study, seasonal infection attack rates range from 14.7% (95%PI: 12.8–16.5%) under full in-person attendance to <0.2% under the most restrictive scenario. Moderate interventions (suspension of tertiary education and work-from-home) reduce attack rates by up to one fourth among adults (15-64 years) and one sixth among older individuals.

Conclusions

This study provides post-pandemic contact matrices for Italy, essential for modelling transmission of respiratory pathogens, and quantitative evidence on the epidemiological impact of targeted physical distancing measures, thereby supporting future policy design.