Contacts in context: large-scale setting-specific social mixing matrices from the BBC Pandemic project
Social mixing patterns are crucial in driving transmission of infectious diseases and informing public health interventions to contain their spread. Age-specific social mixing is often inferred from surveys of self-recorded contacts which by design often have a very limited number of participants. In addition, such surveys are rare, so public health interventions are often evaluated by considering only one such study. Here we report detailed population contact patterns for United Kingdom based self-reported contact data from over 36,000 volunteers that participated in the massive citizen science project BBC Pandemic. The amount of data collected allows us generate fine-scale age-specific population contact matrices by context (home, work, school, other) and type (conversational or physical) of contact that took place. These matrices are highly relevant for informing prevention and control of new outbreaks, and evaluating strategies that reduce the amount of mixing in the population (such as school closures, social distancing, or working from home). In addition, they finally provide the possibility to use multiple sources of social mixing data to evaluate the uncertainty that stems from social mixing when designing public health interventions.
Keywords
Social mixing patterns, Epidemiology, Citizen scienceItem Type | Dataset |
---|---|
Capture method | Aggregation |
Collection Period |
From To September 2017 December 2018 |
Date | 19 February 2020 |
Geographical area covered (offline during plugin upgrade) |
North Latitude East Longitude South Latitude West Longitude 51.1001 -0.686954 51.0774 -0.756134 59.0288 1.73312 49.9365 -10.3079 |
Language(s) of written materials | English |
Creator(s) |
Klepac, P |
LSHTM Faculty/Department | Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology (-2023) |
Participating Institutions | London School of Hygiene & Tropical Medicine, London, United Kingdom, University of Cambridge, Cambridge, United Kingdom, University College London, London, United Kingdom |
Funders |
Project Funder Grant Number Funder URI |
Date Deposited | 03 Mar 2020 16:28 |
Last Modified | 08 Jul 2021 12:50 |
Publisher | Medrxiv |
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- medrxiv (Online Data Resource)
- medrxiv - Dataset (Data)