medRxiv preprint doi: https://doi.org/10.1101/2021.05.13.21256857; this version posted May 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . A Predictive Modelling Framework for COVID-19 Transmission to Inform the Management of Mass Events Claire Donnat Freddy Bunbury Department of Statistics Department of Plant Biology University of Chicago, Chicago, USA Carnegie Institution for Science, Stanford, USA
[email protected] [email protected] Jack Kreindler Filippos T. Filippidis School of Public Health School of Public Health Imperial College, London, UK Imperial College, London, UK
[email protected] [email protected] Austen El-Osta Tõnu Esko Matthew Harris School of Public Health Institute of Genomics School of Public Health Imperial College, London, UK University of Tartu, Tartu, Estonia Imperial College, London, UK
[email protected] [email protected] [email protected] Abstract Modelling COVID-19 transmission at live events and public gatherings is essential to evaluate and control the probability of subsequent outbreaks. Model estimates can be used to inform event organizers about the possibility of super-spreading and the predicted efficacy of safety protocols, as well as to communicate to participants their personalised risk so that they may choose whether to attend. 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, thus limiting their potential to provide insightful estimates.