Augmenting contact matrices with time-use data for fine-grained intervention modelling of disease dynamics: A modelling analysis

PHE Joint modelling group, Edwin van Leeuwen*, Frank Sandmann, Andre Charlett, Anne Presanis, Brian Ferguson, Daniela DeAngelis, Declan Bays, Emilia Vynnycky, Emily Agnew, Emma Bennett, Emma Gillingham, James Lewis, Jonathan Carruthers, Joseph Shingleton, Nick Gent, Paul Birrell, Peter White, Stephanie Shadwell, Steven DykeThomas Finnie

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Social distancing is an important public health intervention to reduce or interrupt the sustained community transmission of emerging infectious pathogens, such as severe acute respiratory syndrome-coronavirus-2 during the coronavirus disease 2019 pandemic. Contact matrices are typically used when evaluating such public health interventions to account for the heterogeneity in social mixing of individuals, but the surveys used to obtain the number of contacts often lack detailed information on the time individuals spend on daily activities. The present work addresses this problem by combining the large-scale empirical data of a social contact survey and a time-use survey to estimate contact matrices by age group (0--15, 16--24, 25–44, 45–64, 65+ years) and daily activity (work, schooling, transportation, and four leisure activities: social visits, bar/cafe/restaurant visits, park visits, and non-essential shopping). This augmentation allows exploring the impact of fewer contacts when individuals reduce the time they spend on selected daily activities as well as when lifting such restrictions again. For illustration, the derived matrices were then applied to an age-structured dynamic-transmission model of coronavirus disease 2019. Findings show how contact matrices can be successfully augmented with time-use data to inform the relative reductions in contacts by activity, which allows for more fine-grained mixing patterns and infectious disease modelling.

Original languageEnglish
Article number09622802211037078
Number of pages12
JournalStatistical Methods in Medical Research
Early online date1 Sep 2021
DOIs
Publication statusE-pub ahead of print - 1 Sep 2021

Bibliographical note

Funding Information: The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

The authors received no financial support for the research, authorship and/or publication of this article.

Open Access: No Open Access licence.

Publisher Copyright: © The Author(s) 2021.

Citation: van Leeuwen E, Sandmann F. Augmenting contact matrices with time-use data for fine-grained intervention modelling of disease dynamics: A modelling analysis. Statistical Methods in Medical Research. September 2021.

DOI: doi:10.1177/09622802211037078

Keywords

  • NONPHARMACEUTICAL INTERVENTIONS
  • SPREAD
  • INFLUENZA
  • SARS
  • TRANSMISSION
  • OUTBREAKS
  • DESIGN

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