Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: May 24, 2021
Open Peer Review Period: May 24, 2021 - Jun 7, 2021
Date Accepted: Sep 18, 2021
Date Submitted to PubMed: Dec 2, 2021
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
A Predictive Modelling Framework for COVID-19 Transmission to Inform the Management of Mass Events
ABSTRACT
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 3rd 2020, January 18th 2021, and March 8th 2021) would likely lead to transmission events only slightly above background rates (0.5 vs 0.2, 6.7 vs 3.5, and 5.4 vs 2.5, respectively. However, the 97.5 percentile of the prediction interval for the infections would likely be substantially higher than the background rate (6.8 vs 2, 89 vs 8, and 71 vs 7), 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.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.