Apparent structural changes in contact patterns during COVID-19 were driven by survey design and long-term demographic trends

TL;DR

This study shows that observed changes in contact patterns during COVID-19 are mainly due to survey design and demographic trends, not solely pandemic-related interventions, impacting how we interpret contact data.

Contribution

The paper reveals that differences in contact patterns during COVID-19 are driven by survey methodology and demographic shifts, not just pandemic measures, highlighting the importance of study design in contact research.

Findings

Contact pattern differences are explained by survey design.

Long-term demographic trends influence contact matrices.

Caution advised in using disparate survey data for epidemic modeling.

Abstract

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.