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

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

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

Martin Grunnill et al. PLoS Comput Biol. .

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.

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Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: LC is a Sanofi employee and may hold stock options within Sanofi. EWT is a Sanofi employee and may hold stock options within Sanofi. AA is a Sanofi employee and may hold stock options within Sanofi.

Figures

Fig 1
Fig 1. Flow diagram of Model Classes (A) and Vaccination Groups (B).
A: all but the states with a * notation move between the vaccination groups depicted B at rates νv=unvaccinated, νv=effective or νv=waned.
Fig 2
Fig 2. Effect of different Test Regimes on infections and hospitalisations as measured by Partial Rank Correlation Coefficient (PRCC).
In calculating PRCCs Latin Hypercube (LH) sampling draws on the parameter space outlined in Tables 2, 3 and 5, using uniform distributions. Simulations are made with the resulting LH sample with each of the testing regimes outlined in Table 7. Every set of simulation made under a testing regime is given a dummy parameter value of 1, except “No Testing” which is given a value of 0. Each testing regime’s effect on an output (Total Infections or Hospitalisation) is measured through calculating PRCCs using the dummy parameter comparing the 1 for the particular testing regime and 0 for its absence.
Fig 3
Fig 3. Effect of different Test Regimes on infections and hospitalisations as measured by % Relative Difference to simulations with no testing regime.
A: Boxplots Total Infections and Hospitalisation in simulations made with no testing regime. B and C: Boxplots of a Testing Regimes % Relative Differences in Total infections and Hospitalisation. For every parameter set produced under LHS the % relative difference in outputs simulated under a testing regime, Fig 3B and 3C, was calculated against the corresponding output from the “No Testing” regime simulations, depicted in Fig 3A, as a control (see Eq 4). The white dots are the means. The array of samples used in simulation was generated from Latin Hypercube sampling drawing upon the distributions outlined in Tables 2, 3 and 5. Details of testing regimes can be found in Table 7.
Fig 4
Fig 4. Partial Rank Correlation Coefficients (PRCCs) between parameters and starting conditions relating to COVID-19 control measures and Total Infections and Hospitalisations.
Where, κ is the isolation transmission modifier (0–1), θ is the asymptomatic transmission modifier (0.342–1), and vA and vB are the proportion recently vaccinated visitors in group clusters A and B, respectively, (0–1). The array of samples used in simulation was generated from Latin Hypercube sampling drawing upon the distributions outlined above and in Tables 2, 3 and 5, using uniform distributions. Details of testing regimes can be found in Table 7.
Fig 5
Fig 5. Comparison of a policy ensuring all visitors must be effectively vaccinated but not having testing “effective visitor vaccination”) against other policies.
A: Boxplots of Total Infections and Hospitalisation under “effective visitor vaccination” (vA = vB = 1). B Boxplots of % relative differences in Total Infections and Hospitalisation seen under various testing regimes at differing levels of effective vaccination for visitors compared to “effective visitor vaccination” as a control. In B % relative differences are calculated between simulations made with the same Latin Hypercbe (LH) sample, see Eq 4. Testing regimes used in comparisons are “No Testing”, “Pre-Travel RT-PCR”, “Pre-Match RT-PCR”, “Pre-Match RA” or “RT-PCR then RA” testing regimes (see Table 7). Levels of effective vaccination for visitors in the comparisons are vA = vB = 0, vA = vB = 0.25, vA = vB = 0.5 and vA = vB = 0.75. The white dots on the boxplots represent mean values. All parameters other than those relating to effective vaccination for visitors (vA and vB) are drawn using LH sampling from distributions outlined in Tables 2, 3 and 5.
Fig 6
Fig 6. Comparison of a policy ensuring all visitors must be effectively vaccinated but not having testing “effective visitor vaccination”) against other policies.
A: Boxplots of Total Infections and Hospitalisation under “effective visitor vaccination” (vA = vB = 1). B Boxplots of % relative differences in Total Infections and Hospitalisation seen under various testing regimes at differing levels of effective vaccination for visitors compared to “effective visitor vaccination” as a control. Note that Figs 5B and 6B plot the same data, Fig 6B simply has a decreased range on the x-axis to aid comparison between boxplots. In B % relative differences are calculated between simulations made with the same Latin Hypercbe (LH) sample, see Eq 4. Testing regimes used in comparisons are “No Testing”, “Pre-Travel RT-PCR”, “Pre-Match RT-PCR”, “Pre-Match RA” or “RT-PCR then RA” testing regimes (see Table 7). Levels of effective vaccination for visitors in the comparisons are vA = vB = 0, vA = vB = 0.25, vA = vB = 0.5 and vA = vB = 0.75. The white dots on the boxplots represent mean values. All parameters other than those relating to effective vaccination for visitors (vA and vB) are drawn using LH sampling from distributions outlined in Tables 2, 3 and 5.
Fig 7
Fig 7. Qatari COVID-19 New Cases Smoothed [48] and Acute Cases under Hospital Treatment [64] around the time of the World Cup.
The area between the yellow dotted lines is the time between the first world cup match and the final match. The area between the red dotted lines is the time between the last group stage match and the beginning of the quarter finals.

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