Untangling the contribution of survey design and demographic change to observed differences in age-stratified contact patterns.

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 45 contact matrices generated from surveys conducted before and during the COVID-19 pandemic, and analyse key structural features that distinguish contact matrices generated from POLYMOD and CoMix, two of the largest contact studies. Our analysis suggests that, while contextual features such as lockdowns could account for some of these distinguishing features, others can be explained by differences in the design of the two studies 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 survey design choices or long-term changes to the short-term effects of interventions.