Background:

Modelling COVID-19 transmission at live events and public gatherings is essential to control the probability of subsequent outbreaks and communicate to participants their personalised risk. Yet, despite the fast-growing body of literature on COVID transmission dynamics, current risk models either neglect contextual information on vaccination rates or disease prevalence or do not attempt to quantitatively model transmission.

Objective:

This paper attempts to bridge this gap by providing informative risk metrics for live public events, along with a measure of their uncertainty.

Methods:

Building upon existing models, our approach ties together three main components: (a) reliable modelling of the number of infectious cases at the time of the event, (b) evaluation of the efficiency of pre-event screening, and (c) modelling of the event’s transmission dynamics and their uncertainty along using Monte Carlo simulations.

Results:

We illustrate the application of our pipeline for a concert at the Royal Albert Hall and highlight the risk’s dependency on factors such as prevalence, mask wearing, or event duration. We demonstrate how this event held on three different dates (August 20th 2020, January 20th 2021, and March 20th 2021) would likely lead to transmission events that are similar to community transmission rates (0.06 vs 0.07, 2.38 vs 2.39, and 0.67 vs 0.60, respectively). However, differences between event and background transmissions substantially widen in the upper tails of the distribution of number of infections (as denoted by their respective 99th quantiles: 1 vs 1, 19 vs 8, and 6 vs 3 for our three dates), further demonstrating that sole reliance on vaccination and antigen testing to gain entry would likely significantly underestimate the tail risk of the event.

Conclusions:

Despite the unknowns surrounding COVID-19 transmission, our estimation pipeline opens the discussion on contextualized risk assessment by combining the best tools at hand to assess the order of magnitude of the risk. Our model can be applied to any future event, and is presented in a user-friendly R Shiny interface.