Modelling disease mitigation at mass gatherings: A case study of COVID-19 at the 2022 FIFA World Cup

PLoS Comput Biol. 2024 Jan 18;20(1):e1011018. doi: 10.1371/journal.pcbi.1011018. eCollection 2024 Jan.

Abstract

The 2022 FIFA World Cup was the first major multi-continental sporting Mass Gathering Event (MGE) of the post COVID-19 era to allow foreign spectators. Such large-scale MGEs can potentially lead to outbreaks of infectious disease and contribute to the global dissemination of such pathogens. Here we adapt previous work and create a generalisable model framework for assessing the use of disease control strategies at such events, in terms of reducing infections and hospitalisations. This framework utilises a combination of meta-populations based on clusters of people and their vaccination status, Ordinary Differential Equation integration between fixed time events, and Latin Hypercube sampling. We use the FIFA 2022 World Cup as a case study for this framework (modelling each match as independent 7 day MGEs). Pre-travel screenings of visitors were found to have little effect in reducing COVID-19 infections and hospitalisations. With pre-match screenings of spectators and match staff being more effective. Rapid Antigen (RA) screenings 0.5 days before match day performed similarly to RT-PCR screenings 1.5 days before match day. Combinations of pre-travel and pre-match testing led to improvements. However, a policy of ensuring that all visitors had a COVID-19 vaccination (second or booster dose) within a few months before departure proved to be much more efficacious. The State of Qatar abandoned all COVID-19 related travel testing and vaccination requirements over the period of the World Cup. Our work suggests that the State of Qatar may have been correct in abandoning the pre-travel testing of visitors. However, there was a spike in COVID-19 cases and hospitalisations within Qatar over the World Cup. Given our findings and the spike in cases, we suggest a policy requiring visitors to have had a recent COVID-19 vaccination should have been in place to reduce cases and hospitalisations.

MeSH terms

  • COVID-19 Vaccines
  • COVID-19* / epidemiology
  • COVID-19* / prevention & control
  • Humans
  • Mass Gatherings
  • Soccer*
  • Sports*

Substances

  • COVID-19 Vaccines

Grants and funding

MG position was funded through the Fields Institute’s Mathematics for Public Health Next Generation program (http://www.fields.utoronto.ca/activities/public-health), grant number 72062654. JA is funded through the Discovery Grant program from the Natural Science and Engineering Research Council of Canada (NSERC, https://www.nserc-crsng.gc.ca/index_eng.asp), grant number RGPIN-2017-05466. LB work is supported, in part, by the US National Science Foundation (NSF, https://www.nsf.gov/). AA is funded through the Advanced Disaster, Emergency and Rapid Response Simulation Initiative (ADERSIM), Ontario Research Fund (https://www.ontario.ca/page/ontario-research-fund) 33270. JW work is also supported by the ADERSIM (Ontario Research Fund 33270), along with the Canada Research Chairs program (https://www.chairs-chaires.gc.ca/home-accueil-eng.aspx, 230720), and the Discovery Grant program from NSERC (105588). This work was supported by the NSERC-Sanofi Industrial Research Chair program in Vaccine Mathematics, Modelling, and Manufacturing (517504). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.