Sequencing and variant calling of SARS-CoV-2 from floor swabs: a potential tool for identifying emergent lineages
Bibliographic record
Abstract
Ongoing viral evolution is a key driver of global pandemics, such as COVID-19, contributing to the repeated emergence and spread of new variants of concern. Identifying emerging viral variants is crucial for controlling the spread of infection; however, patient testing is not always feasible, and clinical samples are not routinely sequenced. As a result, new approaches, such as environmental-based surveillance, are needed for monitoring genetic diversity. Floor swabs provide greater spatial resolution than other environmental sampling approaches, but pose challenges for genomic analyses due to microbial RNA/DNA yields. We investigate the potential of obtaining whole-genome diversity data from floor swab samples to detect circulating lineages of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Floor swabs (n=23) were collected and sequenced from public locations in Ottawa, Canada, during December 2022, and were compared with contemporaneous human samples. Low biomass recovery remained a challenge, as approximately half of the swabs did not yield sufficient genetic material for analysis. The most commonly identified lineages from the floor swabs were XBB, while B (12.5%) and BA (12.5%) lineages appeared less frequently. In contrast, swab results from humans most often identified BQ (49.3%), BA (23.8%) and BF (17.8%), with XBB detected at a lower prevalence (2.7%). XBB became the dominant lineage in the region in the month following floor swab collection, suggesting that floor swabs may offer early signals of emerging outbreaks in comparison with hospital-based clinical sampling. This may suggest a role for floor swabs in outbreak prediction; however, larger studies are needed to validate this approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".