Genomic Epidemiology of a Severe Acute Respiratory Syndrome Coronavirus 2 Outbreak in a US Major League Soccer Club: Was It Travel Related?
Bibliographic record
Abstract
Background: Professional soccer athletes are at risk of acquiring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). United States Major League Soccer (MLS) uses protocol-based SARS-CoV-2 testing for identification of individuals with coronavirus disease 2019. Methods: Per MLS protocol, fully vaccinated players underwent SARS-CoV-2 real-time polymerase chain reaction testing weekly; unvaccinated players were tested every other day. Demographic and epidemiologic data were collected from individuals who tested positive, and contact tracing was performed. Whole genome sequencing (WGS) was performed on positive specimens, and phylogenetic analyses were used to identify potential transmission patterns. Results: In the fall of 2021, all 30 players from 1 MLS team underwent SARS-CoV-2 testing per protocol; 27 (90%) were vaccinated. One player who had recently traveled to Africa tested positive for SARS-CoV-2; within the following 2 weeks, 10 additional players and 1 staff member tested positive. WGS yielded full genome sequences for 10 samples, including 1 from the traveler. The traveler's sample was Delta sublineage AY.36 and was closely related to a sequence from Africa. Nine samples yielded other Delta sublineages including AY.4 (n = 7), AY.39 (n = 1), and B.1.617.2 (n = 1). The 7 AY.4 sequences clustered together; suggesting a common source of infection. Transmission from a family member visiting from England to an MLS player was identified as the potential index case. The other 2 AY.4 sequences differed from this group by 1-3 nucleotides, as did a partial genome sequence from an additional team member. Conclusions: WGS is a useful tool for understanding SARS-CoV-2 transmission dynamics in professional sports teams.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".