Underscoring the effect of swab type, workflow, and positive sample order on swab pooling for COVID-19 surveillance testing
Bibliographic record
Abstract
Sample pooling is a promising strategy to facilitate COVID-19 surveillance testing for a larger population in comparison to individual single testing due to resource and time constraints. Increased surveillance testing capacity will reduce the likelihood of outbreaks as the general population is returning to work, school, and other gatherings. We have analyzed the impact of three variables on the effectiveness of pooling test samples: swab type, workflow, and positive sample order. We investigated the performance of several commercially available swabs (Steripack polyester flocked, Puritan nylon flocked, Puritan foam) in comparison to a new injected molded design (Yukon). The bench-top performance of collection swab was conducted with a previously developed anterior nasal cavity tissue model, based on a silk-glycerol sponge to mimic soft tissue mechanics and saturated with a physiologically relevant synthetic nasal fluid spiked with heat-inactivated SARS-CoV-2. Overall, we demonstrated statistically significant differences in performance across the different swab types. A characterization of individual swab uptake (gravimetric analysis) and FITC microparticle release suggests that differences in absorbance and retention drive the observed differences in Ct of the pooled samples. We also proposed two distinct pooling workflows to encompass different community collection modes and analyzed the difference in resulting positive pools as an effect of workflow, swab type, and positive sample order. Overall, swab types with lower volume retention resulted in reduced false negative occurrence, also observed for collection workflows with limited incubation times. Concurrently, positive sample order did have a significant impact on pooling test outcome, particularly in the case of swab type with great volume retention. We demonstrated that the variables investigated here affect the results of pooled COVID-19 testing, and therefore should be considered while designing pooled surveillance testing.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".