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 language | English |
---|---|
Article number | 09622802211037078 |
Pages (from-to) | 1704-1715 |
Number of pages | 12 |
Journal | Statistical Methods in Medical Research |
Volume | 31 |
Issue number | 9 |
Early online date | 1 Sept 2021 |
DOIs | |
Publication status | E-pub ahead of print - 1 Sept 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