  Social contact patterns are key drivers of infectious disease transmission.
During the COVID-19 pandemic, differences between pre-COVID and COVID-era
contact rates were widely attributed to non-pharmaceutical interventions such
as lockdowns. However, the factors that drive changes in the distribution of
contacts between different subpopulations remain poorly understood. Here, we
present a clustering analysis of 33 contact matrices generated from surveys
conducted before and during the COVID-19 pandemic, and analyse key features
distinguishing their topological structures. While we expected to identify
aspects of pandemic scenarios responsible for these features, our analysis
demonstrates that they can be explained by differences in study design and
long-term demographic trends. Our results caution against using survey data
from different studies in counterfactual analysis of epidemic mitigation
strategies. Doing so risks attributing differences stemming from methodological
choices or long-term changes to the short-term effects of interventions.
