Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pcbi.1009098
Title: Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era
Authors: Prem, Kiesha 
Zandvoort, Kevin van
Klepac, Petra
Eggo, Rosalind M.
Davies, Nicholas G.
Cook, Alex R. 
Jit, Mark
Issue Date: 26-Jul-2021
Publisher: Public Library of Science
Citation: Prem, Kiesha, Zandvoort, Kevin van, Klepac, Petra, Eggo, Rosalind M., Davies, Nicholas G., Cook, Alex R., Jit, Mark (2021-07-26). Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era. PLoS Computational Biology 17 (7). ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pcbi.1009098
Rights: Attribution 4.0 International
Abstract: Mathematical models have played a key role in understanding the spread of directly-transmissible infectious diseases such as Coronavirus Disease 2019 (COVID-19), as well as the effectiveness of public health responses. As the risk of contracting directly-transmitted infections depends on who interacts with whom, mathematical models often use contact matrices to characterise the spread of infectious pathogens. These contact matrices are usually generated from diary-based contact surveys. However, the majority of places in the world do not have representative empirical contact studies, so synthetic contact matrices have been constructed using more widely available setting-specific survey data on household, school, classroom, and workplace composition combined with empirical data on contact patterns in Europe. In 2017, the largest set of synthetic contact matrices to date were published for 152 geographical locations. In this study, we update these matrices with the most recent data and extend our analysis to 177 geographical locations. Due to the observed geographic differences within countries, we also quantify contact patterns in rural and urban settings where data is available. Further, we compare both the 2017 and 2020 synthetic matrices to out-of-sample empirically-constructed contact matrices, and explore the effects of using both the empirical and synthetic contact matrices when modelling physical distancing interventions for the COVID-19 pandemic. We found that the synthetic contact matrices show qualitative similarities to the contact patterns in the empirically-constructed contact matrices. Models parameterised with the empirical and synthetic matrices generated similar findings with few differences observed in age groups where the empirical matrices have missing or aggregated age groups. This finding means that synthetic contact matrices may be used in modelling outbreaks in settings for which empirical studies have yet to be conducted. © 2021 Prem et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Source Title: PLoS Computational Biology
URI: https://scholarbank.nus.edu.sg/handle/10635/233686
ISSN: 1553-734X
DOI: 10.1371/journal.pcbi.1009098
Rights: Attribution 4.0 International
Appears in Collections:Elements
Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1371_journal_pcbi_1009098.pdf3.35 MBAdobe PDF

OPEN

NoneView/Download

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons