Evidence map›Paper›PMID 38236838›Full record

ArticlePLoS computational biology2024

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

Martin Grunnill, Julien Arino, Zachary McCarthy, Nicola Luigi Bragazzi, Laurent Coudeville, Edward W Thommes, Amine Amiche, Abbas Ghasemi, Lydia Bourouiba, Mohammadali Tofighi and 3 more

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.6field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 5 institutions in 3 countries.

Martin GrunnillLaboratory of Industrial and Applied Mathematics (LIAM), York University, Toronto, Ontario, Canada.ORCID 0000-0002-4835-8007
Julien ArinoDepartment of Mathematics, University of Manitoba, Winnipeg, Manitoba, Canada.ORCID 0000-0001-6409-5027
Zachary McCarthyLaboratory of Industrial and Applied Mathematics (LIAM), York University, Toronto, Ontario, Canada.ORCID 0000-0002-3760-3740
Nicola Luigi BragazziLaboratory of Industrial and Applied Mathematics (LIAM), York University, Toronto, Ontario, Canada.
Laurent CoudevilleModeling, Epidemiology and Data Science (MEDS), Sanofi, Lyon, France.
Edward W ThommesModeling, Epidemiology and Data Science (MEDS), Sanofi, Lyon, France.
Amine AmicheSanofi, Dubai, United Arab Emirates.
Abbas GhasemiThe Fluid Dynamics of Disease Transmission Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.ORCID 0000-0003-2442-5962
Lydia BourouibaThe Fluid Dynamics of Disease Transmission Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Mohammadali TofighiDahdaleh Institute for Global Health Research, York University, Toronto, Canada.ORCID 0000-0001-9574-7614
Ali AsgaryDisaster & Emergency Management, York University, Toronto, Canada.
Mortaza Baky-HaskueeDisaster & Emergency Management, York University, Toronto, Canada.
Jianhong WuLaboratory of Industrial and Applied Mathematics (LIAM), York University, Toronto, Ontario, Canada.
York University · CASanofi (France) · FRMassachusetts Institute of Technology · USToronto Metropolitan University · CAUniversity of Manitoba · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19SoccerSportsCOVID-19 VaccinesHumansMass GatheringsCOVID-19 Vaccines

Identifiers

PMID38236838
PMCPMC10796029
OpenAlexW4390971110

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.