Social network diversity and COVID-19 infection and severity risk: a longitudinal population study

Background: Clinical evidence on how social network diversity (SND) influences the risk of infection and disease severity during the coronavirus disease 2019 (COVID-19) pandemic remains limited. We aim to investigate the associations between SND and the risk of COVID-19 infection and disease severity using a large-scale longitudinal cohort study. Methods: We analyzed data from participants in a longitudinal study, the Japan COVID-19 and Society Internet Survey (JACSIS) between 2020 and 2023. The SND score was calculated as the sum of seven distinct types of social networks. COVID-19 infection was assessed as ever infection, and severity was defined as oxygen-requiring admission, using a self-reported questionnaire. Poisson regression with robust standard errors estimated risk ratios (RRs) and 95% confidence intervals (CIs), adjusting for sociodemographic and clinical characteristics. Results: Of 13,713 participants (mean age 53.2 ± 15.7 years, 46.4% women), 3,251 (23.7%) developed COVID-19, and among infected individuals, 277 (8.5%) required oxygen therapy. Higher SND scores were associated with COVID-19 infection with linear trend (SND score 7 vs. 0: adjusted RR 2.49; 95% CI 2.11-2.95). In contrast, the association between SND score and disease severity followed a U-shaped pattern, with 4-5 SND showing the lowest risk of oxygen-requiring admission (adjusted RR 0.15; 95% CI 0.11-0.30) compared to those with 0 SND. Conclusion: While higher SND was associated with increased COVID-19 infection risk, moderate social network diversity appeared protective against severe disease outcomes. These findings suggest a complex trade-off between exposure risk and potential health benefits of social networks during infectious disease outbreaks.