Chao Xu

Mass gathering events (MGEs) play a critical role for infectious disease dynamics on a population level as they provide opportunities for superspreading; however, underlying mechanisms remain insufficiently understood. We analyzed nationwide GPS-based, individual-level location data from mobile phone users in Germany between April and August 2024 with 16 m spatial precision. Potentially infectious contacts were inferred from close co-location and linked to contact settings using OpenStreetMap data. Various MGEs, including EURO 2024 matches, major concerts, festivals, and fairs were compared using a common contact metric. Non-football events generated substantially more contacts than football events. While overall national contact numbers remained stable, MGEs produced so-called “small-world” contacts which gather people from distant locations into close proximity and could strongly enhance infectious disease dynamics. Crucially, most high-risk contacts occurred within two hours before the event, not at the event itself, and concentrated in public transport, leisure, and event-adjacent areas. Our work provides the first systematic and comparative evaluation of contact exposure across various types of MGEs and contact settings. Event-type-specific dynamics, particularly indirect and mobility-driven contacts, critically shape infection risk. These insights can inform accurate transmission modeling, targeted intervention and event-management strategies.

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.