期刊
BMC MEDICINE
卷 19, 期 1, 页码 -出版社
BMC
DOI: 10.1186/s12916-021-02133-y
关键词
Social contact behaviour; Mixing patterns; Contact data; Mathematical modelling; SARS-CoV-2; COVID-19; Europe
资金
- European Union [101003688]
- Belgium: Belgian Health Institute - Sciensano and Janssen Belgium
- Germany: Institute of Epidemiology and Social Medicine at the University of Munster
- Institute for Medical Epidemiology Biometry and Informatics at Martin Luther University Halle-Wittenberg
- Robert-Koch-Institute Berlin
- Helmholtz-Gemeinschaft Deutscher Forschungszentren e.V. via the HZEpiAdHoc The Helmholtz Epidemiologic Response against the COVID-19 Pandemic project
- German Free State of Saxony via the SaxoCOV project
- Norway: University of Bergen
- National Institute of Public Health Norway
- the Netherlands: Ministry of Health, Welfare and Sport - the Netherlands
- UK: Medical Research Council MRC [MC_PC_19065]
- National Institute of Health Research NIHR [CV220-088]
This study collected social contact data in various phases of the COVID-19 pandemic in over 20 European countries, providing essential support for mathematical models on epidemic spread. These data can help policymakers balance non-pharmaceutical interventions, economic activity, mental health, and wellbeing.
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.
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