TY - JOUR
T1 - Augmenting contact matrices with time-use data for fine-grained intervention modelling of disease dynamics
T2 - A modelling analysis
AU - PHE Joint modelling group
AU - van Leeuwen, Edwin
AU - Sandmann, Frank
AU - Jaccard, Abbygail
AU - Charlett, Andre
AU - Rance, Anna
AU - Presanis, Anne
AU - Purohit, Archana
AU - Ferguson, Brian
AU - Walker, Brodie
AU - DeAngelis, Daniela
AU - Mustard, David
AU - Bays, Declan
AU - Addei, Dianne
AU - Vynnycky, Emilia
AU - Agnew, Emily
AU - Bennett, Emma
AU - Gillingham, Emma
AU - Williams, Hannah
AU - Hall, Ian
AU - Lewis, James
AU - Carruthers, Jonathan
AU - Shingleton, Joseph
AU - Blake, Joshua
AU - Field, Judith
AU - Grunnill, Martin
AU - Edmunds, Matt
AU - Hennessey, Matt
AU - Gent, Nick
AU - Birrell, Paul
AU - White, Peter
AU - Baracaia, Simona
AU - Shadwell, Stephanie
AU - Dyke, Steven
AU - Finnie, Thomas
AU - Cox, Virginia
AU - Zhang, Xu Sheng
N1 - 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
PY - 2021/9/1
Y1 - 2021/9/1
N2 - 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.
AB - 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.
KW - NONPHARMACEUTICAL INTERVENTIONS
KW - SPREAD
KW - INFLUENZA
KW - SARS
KW - TRANSMISSION
KW - OUTBREAKS
KW - DESIGN
UR - http://www.scopus.com/inward/record.url?scp=85114649670&partnerID=8YFLogxK
U2 - 10.1177/09622802211037078
DO - 10.1177/09622802211037078
M3 - Article
AN - SCOPUS:85114649670
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
SN - 0962-2802
M1 - 09622802211037078
ER -