Emergence of SARS-CoV-2 Delta Variant and Effect of Nonpharmaceutical Interventions, British Columbia, Canada
Bibliographic record
Abstract
T hroughout the COVID-19 pandemic, SARS-CoV-2 variants have emerged through viral mutation.Variants demonstrating an increase in transmissibility or virulence; changes in clinical manifestations; or a decrease in the effectiveness of public health measures, diagnostics, vaccines, or therapeutics are designated variants of concern (VOCs) by the World Health Organization (1).By June 2021, a total of 4 SARS-CoV-2 variants had been designated VOCs (1).Designated a VOC in May 2021, Delta largely replaced the earlier Alpha, Beta, and Gamma VOCs because of its comparatively higher transmissibility (2).In India, where it was first detected, Delta outcompeted Alpha and drove an increase of COVID-19 cases beginning in March 2021 (3).By mid-August 2021, Delta represented >90% of genetically sequenced SARS-CoV-2 samples submitted to GISAID (https://www.gisaid.org), dominating on a global scale until its decline in favor of Omicron beginning in December 2021 (4,5).By March 1, 2021, British Columbia (2021 population 5,214,805), Canada, had reported >80,000 COVID-19 cases and detected Alpha, Beta, and Gamma VOC cases among residents (6).The Delta VOC was first detected in British Columbia during the week of March 21, 2021, but did not grow to dominance (>50% prevalence) until 17 weeks later, during the week of July 18, 2021, after major relaxations in public health measures, or nonpharmaceutical interventions (NPIs).Differences in factors such as NPIs, vaccination rates, competing variants in circulation, and population density and behavior may result in interjurisdictional differences in the transmission and growth rates of variants (7-10).The initial growth and time to dominance of Delta in British Columbia was slower than in jurisdictions such as England, Scotland, and several US states (7-10).In England, Delta grew to Emergence of SARS-CoV-2 Delta Variant and Effect of Nonpharmaceutical Interventions, British Columbia, Canada
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".