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Abstract
Large-scale events like the UEFA Euro 2020 football (soccer) championship offer a unique opportunity to quantify the impact of gatherings on the spread of COVID-19, as the number and dates of matches played by participating countries resembles a randomized study. Using Bayesian modeling and the gender imbalance in COVID-19 data, we attribute 840,000 (95% CI: [0.39M, 1.26M]) COVID-19 cases across 12 countries to the championship. The impact depends non-linearly on the initial incidence, the reproduction number R, and the number of matches played. The strongest effects are seen in Scotland and England, where as much as 10,000 primary cases per million inhabitants occur from championship-related gatherings. The average match-induced increase in R was 0.46 [0.18, 0.75] on match days, but important matches caused an increase as large as +3. Altogether, our results provide quantitative insights that help judge and mitigate the impact of large-scale events on pandemic spread.
In this Bayesian inference study, the authors aim to quantify the impact of the men’s 2020 UEFA Euro Football Championship on COVID-19 spread in twelve participating countries. They estimate that 0.84 million cases and 1,700 deaths were attributable to the championship, with most impacts in England and Scotland.
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1 Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany (GRID:grid.419514.c) (ISNI:0000 0004 0491 5187)
2 Max Planck Institute for Physics, München, Germany (GRID:grid.435824.c) (ISNI:0000 0001 2375 0603)
3 Universität Bonn, Physikalisches Institut, Bonn, Germany (GRID:grid.10388.32) (ISNI:0000 0001 2240 3300)
4 Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany (GRID:grid.419514.c) (ISNI:0000 0004 0491 5187); University of Göttingen, Institute for the Dynamics of Complex Systems, Göttingen, Germany (GRID:grid.7450.6) (ISNI:0000 0001 2364 4210); University of Göttingen, Institute of Computer Science and Campus Institute Data Science, Göttingen, Germany (GRID:grid.7450.6) (ISNI:0000 0001 2364 4210)