Public Health Applications of Genomic Epidemiology during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has brought the power of genomic epidemiology to the forefront. Harnessing the information stored in genomic sequences supplemented with traditional epidemiological data important questions about the drivers of transmission can be elucidated. In this thesis, we used genomic epidemiological methods for surveillance, understanding transmission in outbreaks, and to create tools to help monitor the spread of SARS-CoV-2 VOCs. We identified potential introductions through returning travellers and characterized the genetic diversity and circulating lineages within the first three months of the pandemic in Ontario. Genomic epidemiology allowed us to discern whether farm outbreaks were seeded by multiple introductions and provided support to suggest that infections in farm outbreaks were acquired locally. Finally, understanding the genomic landscape of the variants of concern (VOC) lineages allowed us to develop an assay that allows for fast identification of VOCs, which is a useful tool in monitoring their spread. The results from each of these investigations can lead to policy changes or new measures to help prevent the spread of SARS-CoV-2.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".