World Tuberculosis Day 2023 theme “Yes! We Can End TB!”
Bibliographic record
Abstract
INTRO: Viruses, including SARS-CoV-2, which causes COVID-19, are constantly changing. These genetic changes (aka mutations) occur over time and can lead to the emergence of new variants that may have different characteristics. After the first SARS-CoV-2 genome was published in early 2020, scientists all over the world soon realized the immediate need to obtain as much genetic information from as many strains as possible. However, understanding the functional significance of the mutations harbored by a variant is important to assess its impact on transmissibility, disease severity, immune escape, and the effectiveness of vaccines and therapeutics. METHODS: Here in Canada, we have developed an interactive framework for visualizing and reporting mutations in SARS-CoV-2 variants. This framework is composed of three stand-alone yet connected components; an interactive visualization (COVID-MVP), a manually curated functional annotation database (pokay), and a genomic analysis workflow (nf-ncov-voc). Findings: COVID-MVP provides (i) an interactive heatmap to visualize and compare mutations in SARS-CoV-2 lineages classified across different VOCs, VOIs, and VUMs; (ii) mutation profiles including the type, impact, and contextual information; (iii) annotation of biological impacts for mutations where functional data is available in the literature; (iv) summarized information for each variant and/or lineage in the form of a surveillance report; and (v) the ability to upload raw genomic sequence(s) for rapid processing and annotating for real-time classification. DISCUSSION: This comprehensive comparison allows microbiologists and public health practitioners to better predict how the mutations in emerging variants will impact factors such as infection severity, vaccine resistance, hospitalization rates, etc. CONCLUSION: This framework is cloud-compatible & standalone, which makes it easier to integrate into other genomic surveillance tools as well. COVID-MVP is integrated into the Canadian VirusSeq data portal (https://virusseqdataportal.ca) - a national data hub for SARS-COV-2 genomic data. COVID-MVP is also used by the CanCOGeN and CoVaRR networks in national COVID-19 genomic surveillance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.239 | 0.178 |
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".