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

There are some additional limitations to the dataset presented here. First, children under the age of 13 were excluded for ethical reasons, which meant there was a gap in the matrices for participants in this group. Given the role of school-age children in transmission of many respiratory infections, we are missing important information on mixing in school-aged children. For a flu-like pathogen, this core group will be responsible for driving transmission in the wider population [2] which can be seen from indirect effects observed in other age-groups by targeting the vaccination of children [13] . In our previous work [17] we filled the missing square sub-matrix (the dimensions of it will depend on the size of the age-groups chosen in the model) after making contacts reciprocal with appropriate values from POLYMOD, and here we take an additional step of rescaling the missing sub-matrix so that the overall dominant eigenvalue of the matrix doesn't change. The missing data could also be interpolated from surrounding regions and assuming the log-binomial distribution of contacts.