SOCRATES-CoMix: a platform for timely and open-source contact mixing data during and in between COVID-19 surges and interventions in over 20 European countries.

COVID-19 Contact data Europe Mathematical modelling Mixing patterns SARS-CoV-2 Social contact behaviour

Journal

BMC medicine
ISSN: 1741-7015
Titre abrégé: BMC Med
Pays: England
ID NLM: 101190723

Informations de publication

Date de publication:
29 09 2021
Historique:
received: 16 05 2021
accepted: 16 09 2021
entrez: 29 9 2021
pubmed: 30 9 2021
medline: 16 10 2021
Statut: epublish

Résumé

SARS-CoV-2 dynamics are driven by human behaviour. Social contact data are of utmost importance in the context of transmission models of close-contact infections. Using online representative panels of adults reporting on their own behaviour as well as parents reporting on the behaviour of one of their children, we collect contact mixing (CoMix) behaviour in various phases of the COVID-19 pandemic in over 20 European countries. We provide these timely, repeated observations using an online platform: SOCRATES-CoMix. In addition to providing cleaned datasets to researchers, the platform allows users to extract contact matrices that can be stratified by age, type of day, intensity of the contact and gender. These observations provide insights on the relative impact of recommended or imposed social distance measures on contacts and can inform mathematical models on epidemic spread. These data provide essential information for policymakers to balance non-pharmaceutical interventions, economic activity, mental health and wellbeing, during vaccine rollout.

Sections du résumé

BACKGROUND
SARS-CoV-2 dynamics are driven by human behaviour. Social contact data are of utmost importance in the context of transmission models of close-contact infections.
METHODS
Using online representative panels of adults reporting on their own behaviour as well as parents reporting on the behaviour of one of their children, we collect contact mixing (CoMix) behaviour in various phases of the COVID-19 pandemic in over 20 European countries. We provide these timely, repeated observations using an online platform: SOCRATES-CoMix. In addition to providing cleaned datasets to researchers, the platform allows users to extract contact matrices that can be stratified by age, type of day, intensity of the contact and gender. These observations provide insights on the relative impact of recommended or imposed social distance measures on contacts and can inform mathematical models on epidemic spread.
CONCLUSION
These data provide essential information for policymakers to balance non-pharmaceutical interventions, economic activity, mental health and wellbeing, during vaccine rollout.

Identifiants

pubmed: 34583683
doi: 10.1186/s12916-021-02133-y
pii: 10.1186/s12916-021-02133-y
pmc: PMC8478607
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

254

Subventions

Organisme : Horizon 2020 Framework Programme
ID : 101003688

Informations de copyright

© 2021. The Author(s).

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Auteurs

Frederik Verelst (F)

Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.

Lisa Hermans (L)

Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium. lisa.hermans@uhasselt.be.

Sarah Vercruysse (S)

Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium.

Amy Gimma (A)

London School of Hygiene and Tropical Medicine, London, UK.

Pietro Coletti (P)

Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium.

Jantien A Backer (JA)

Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.

Kerry L M Wong (KLM)

London School of Hygiene and Tropical Medicine, London, UK.

James Wambua (J)

Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium.

Kevin van Zandvoort (K)

London School of Hygiene and Tropical Medicine, London, UK.

Lander Willem (L)

Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.

Laurens Bogaardt (L)

Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.

Christel Faes (C)

Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium.

Christopher I Jarvis (CI)

London School of Hygiene and Tropical Medicine, London, UK.

Jacco Wallinga (J)

Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
Dept Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.

W John Edmunds (WJ)

London School of Hygiene and Tropical Medicine, London, UK.

Philippe Beutels (P)

Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.
School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia.

Niel Hens (N)

Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.
Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium.

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Classifications MeSH