Abstract
BackgroundModelling COVID-19 transmission at live events and public gatherings is essential to controlling the probability of subsequent outbreaks and communicating to participants their personalized risk. Yet, despite the fast-growing body of literature on COVID-19 transmission dynamics, current risk models either neglect contextual information including vaccination rates or disease prevalence or do not attempt to quantitatively model transmission.ObjectiveThis paper attempted to bridge this gap by providing informative risk metrics for live public events, along with a measure of their uncertainty.MethodsBuilding upon existing models, our approach ties together 3 main components: (1) reliable modelling of the number of infectious cases at the time of the event, (2) evaluation of the efficiency of pre-event screening, and (3) modelling of the event’s transmission dynamics and their uncertainty using Monte Carlo simulations.ResultsWe illustrated the application of our pipeline for a concert at the Royal Albert Hall and highlighted the risk’s dependency on factors such as prevalence, mask wearing, and event duration. We demonstrate how this event held on 3 different dates (August 20, 2020; January 20, 2021; and March 20, 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 widened in the upper tails of the distribution of the number of infections (as denoted by their respective 99th quantiles: 1 vs 1, 19 vs 8, and 6 vs 3, respectively, for our 3 dates), further demonstrating that sole reliance on vaccination and antigen testing to gain entry would likely significantly underestimate the tail risk of the event.ConclusionsDespite 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 RShiny interface. Finally, we discussed our model’s limitations as well as avenues for model evaluation and improvement.
Highlights
BackgroundEvaluating the Safety of Live EventsMore than a year after a global and unprecedented cancellation of live events in March 2020, the future of live events and the entertainment industry remains uncertain despite increasing vaccination rates and low community prevalence levels
Despite the re-opening of live events in the United Kingdom on July 19, 2021, the threat of existing and emergent COVID-19 variants coupled with dwindling immunity from vaccination over time suggests that policy makers and event organizers will likely continue to struggle with the following 2 questions: (1) Is the COVID-19 transmission risk posed by these events tolerable? and (2) What additional safety measures can be feasibly deployed to reduce this risk?
JMIR Public Health Surveill 2021 | vol 7 | iss. 12 | e30648 | p. 5 had COVID-19 in the previous year, or has the participant been vaccinated?) While previous history could be imputed through additional questions, for the sake of simplicity, we only considered the vaccination status of the participants— leaving out the proportion of the population that had COVID-19 but was not yet vaccinated
Summary
BackgroundEvaluating the Safety of Live EventsMore than a year after a global and unprecedented cancellation of live events in March 2020, the future of live events and the entertainment industry remains uncertain despite increasing vaccination rates and low community prevalence levels (at the time of writing). Evaluating the safety (or lack thereof) of large public gatherings can be reframed as quantifying the significance and magnitude of their effect on the distribution of the number of primary and secondary COVID-19 cases. Modelling COVID-19 transmission at live events and public gatherings is essential to controlling the probability of subsequent outbreaks and communicating to participants their personalized risk. Results: We illustrated the application of our pipeline for a concert at the Royal Albert Hall and highlighted the risk’s dependency on factors such as prevalence, mask wearing, and event duration We demonstrate how this event held on 3 different dates (August 20, 2020; January 20, 2021; and March 20, 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). We discussed our model’s limitations as well as avenues for model evaluation and improvement
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