Intraindividual variability in non-household contacts: a German longitudinal study, April 2020-December 2021
Related Experiment Video
Updated: Feb 23, 2026

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
Published on: February 3, 2023
Chao Xu1, Aleksandr Bryzgalov1, Johannes Horn1
1Institute for Medical Epidemiology, Biometrics, and Informatics, Martin Luther University Halle-Wittenberg, Halle, Germany.
Abstract:
BACKGROUND: Day-to-day variability in social contacts can shape transmission dynamics yet is rarely quantified. We aimed to quantify intraindividual variability (IIV) in non-household contacts during the COVID-19 pandemic in Germany and to assess its associations with sociodemographic characteristics, vaccination, and policy stringency. METHODS: We analyzed longitudinal contact survey data with 33 waves between April 2020 and December 2021, including 7,845 participants and 59,462 observations. Pearson residuals from a mixed-effects negative binomial model were used to derive the within-person standard deviation (riSD) for participants with at least two observations, as a proxy of IIV. Gamma regression models with log link were fitted to estimate mean ratios (MR). RESULTS: Children and adolescents aged 0–17 years showed higher riSD than other age groups (MR = 1.13, 95% CI 1.09–1.16). Participants living in households with three or more members had higher riSD than those living alone (1.05, 95% CI 1.02–1.07). Retired individuals, homemakers, the unemployed, and students exhibited lower riSD than employed participants. Regarding COVID-19 vaccination, compared with the pre-vaccination window (− 100 to 0 days), riSD was higher in the post-vaccination window (1 to 100 days after the first COVID-19 vaccination dose) (1.13, 95% CI 1.06–1.20). Weaker policy stringency was strongly associated with higher riSD (1.36, 95% CI 1.32–1.39). CONCLUSIONS: IIV in non-household contacts was shaped by age, household composition, employment status, vaccination status, and policy context. Analyses relying solely on average contact numbers may misrepresent transmission risk when contact behavior is highly variable. Incorporating IIV alongside mean contact levels may improve infectious disease models and inform public health policies.
More Related Videos
11:21Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
05:03Author Spotlight: Unveiling Mechanisms of Stress Resilience - Significant Findings, Advancements, and Future Research
Published on: December 15, 2023
Related Concept Videos
Longitudinal Studies
Longitudinal Research
Variability: Analysis
The range is a simple measure of variability, indicating the difference between the highest and...
Cross-Sectional Research
Data Collection by Observations
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Statistical Methods for Analyzing Epidemiological Data