Alcov: Estimating Variant of Concern Abundance from SARS-CoV-2 Wastewater Sequencing Data
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Detection of SARS-CoV-2 in wastewater is an important strategy for community level surveillance. Variants of concern (VOCs) can be detected in the wastewater samples using next generation sequencing, however it can be challenging to determine the relative abundance of different VOCs since the reads cannot be assembled into complete genomes. Here, we present Alcov (abundance learning of SARS-CoV-2 variants), a tool that uses mutation frequencies in SARS-CoV-2 sequencing data to predict the distribution of VOC lineages in the sample. We used Alcov to predict the distributions of lineages from three wastewater samples which agreed well with clinical data. By predicting not just which VOCs are present, but their relative abundances in the population, Alcov extracts a more complete snapshot of the variants which are circulating in a community.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it