# Homophily and social mixing in a small community: Implications for infectious disease transmission

## TL;DR

This study examines how people in a diverse community mix with others based on traits like race and age, and how this affects the spread of infectious diseases like SARS-CoV-2.

## Contribution

The study provides novel insights into ethnic/racial and age-based mixing patterns in a real-world setting using egocentric networks of SARS-CoV-2 cases.

## Key findings

- High in-group mixing was observed among ethnic/racial groups compared to the general population.
- Black or African-American individuals interacted with a wider age range, mainly due to family ties.
- Non-binary individuals had less age diversity in their networks, potentially affecting transmission risk.

## Abstract

Community mixing patterns by sociodemographic traits can inform the risk of epidemic spread among groups, and the balance of in- and out-group mixing affects epidemic potential. Understanding mixing patterns can provide insight about potential transmission pathways throughout a community. We used a snowball sampling design to enroll people recently diagnosed with SARS-CoV-2 in an ethnically and racially diverse county and asked them to describe their close contacts and recruit some contacts to enroll in the study. We constructed egocentric networks of the participants and their contacts and assessed age-mixing, ethnic/racial homophily, and gender homophily. The total size of the egocentric networks was 2,544 people (n = 384 index cases + n = 2,160 recruited peers or other contacts). We observed high rates of in-group mixing among ethnic/racial groups compared to the ethnic/racial proportions of the background population. Black or African-American respondents interacted with a wider range of ages than other ethnic/racial groups, largely due to familial relationships. The egocentric networks of non-binary contacts had little age diversity. Black or African-American respondents in particular reported mixing with older or younger family members, which could increase the risk of transmission to vulnerable age groups. Understanding community mixing patterns can inform infectious disease risk, support analyses to predict epidemic size, or be used to design campaigns such as vaccination strategies so that community members who have vulnerable contacts are prioritized.