Contacts in context: large-scale setting-specific social mixing matrices from the BBC Pandemic project

Petra Klepac; Adam J Kucharski; Andrew JK Conlan; Stephen Kissler; Maria Tang; Hannah Fry; Julia R Gog

For directly transmitted respiratory pathogens such as influenza, measles and coronaviruses, social mixing patterns shape the risk of individual-level infection [6] and population-level transmission dynamics [29, 18] , as well as the effectiveness of control measures targeted at specific age groups [2] . Typically two main approaches have been used to measure social mixing patterns relevant for the spread of disease: inference of contacts based on wearable devices such as proximity sensors [26, 20] , or self-recording of contacts [9] . As well as being able to capture age-specific patterns of infection [18] , self-recording also allow for details of the type and setting of social contacts, and demographic information about the contacts themselves.