Public health genomics capacity assessment: readiness for large-scale pathogen genomic surveillance in Canada’s public health laboratories
Bibliographic record
Abstract
BACKGROUND: Along with rapid diagnostic testing, contact tracing, and public health measures, an effective pandemic response incorporates genomics-based surveillance. Large-scale SARS-CoV-2 genome sequencing is a crucial component of the global response to COVID-19. Characterizing the state of genomics readiness among Canada's public health laboratories was necessary to inform strategic planning and deployment of capacity-building resources in the early stages of the pandemic. METHODS: We used a qualitative study design and focus group discussions, encompassing both technical and leadership perspectives, to perform an in-depth evaluation of the state of pathogen genomics readiness in Canada. RESULTS: We found substantial diversity in the state of readiness for SARS-CoV-2 genomic surveillance across Canada. Despite this variability, we identified common barriers and needs in the areas of specimen access, data flow and sharing, computing infrastructure, and access to highly qualified bioinformatics personnel. CONCLUSIONS: These findings enable the strategic prioritization and deployment of resources to increase Canada's ability to perform effective public health genomic surveillance for COVID-19 and prepare for future emerging infectious diseases. They also provide a unique qualitative research model for use in capacity building.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".