Epistorm-Mix: Mapping Social Contact Patterns for Respiratory Pathogen Spread in the Post-Pandemic United States

Human contact patterns are a fundamental determinant of respiratory pathogen transmission, yet nationally representative post-pandemic data for the United States are limited. We present Epistorm-Mix, a 2024 probability-based online survey designed to be nationally representative by age, sex, race/ethnicity, household income, census region, and language. Respondents reported all person-to-person contacts from the preceding day, including the contact's age and the setting (household, school, workplace, or community). We quantified contact numbers across demographic and social characteristics and used generalized additive models to test adjusted differences. We constructed age-stratified contact matrices and their setting-specific counterparts, benchmarking them against widely used synthetic matrices to simulate the spread of an epidemic of a respiratory pathogen. We found an average of 7.4 contacts per day, with significant heterogeneity across the population. Contact rates were highest among teenagers (15-19 years) and lowest among older adults (60+ years). In-person attendance at school or work was a major driver, resulting in 2-3 times more contacts than remote participation. We also identified key socioeconomic and demographic group heterogeneities: the number of contacts generally increased with household income, and Non-Hispanic (NH) Black and NH Asian individuals reported statistically significant fewer total contacts than NH White individuals. We found strong assortative mixing by age and demographic group with markedly distinct contact patterns across different social settings (households, schools, workplaces, and the community). While the study's age-mixing patterns are broadly comparable to international findings, the identified demographic heterogeneities reflect social structures unique to the US, underscoring the need for country-specific data. Epistorm-Mix provides a nationally representative portrait of post-pandemic US contact patterns and serves as an open-access resource for modeling and public health planning.