SARS-CoV-2 Transmission in Alberta, British Columbia, and Ontario, Canada, January 1-July 6, 2020
Bibliographic record
Abstract
Abstract Objective To investigate COVID-19 epidemiology in Alberta, British Columbia and Ontario, Canada. Methods We calculated the incidence rate ratio (January 1—July 6, 2020) between the 3 provinces, and estimated time-varying reproduction number, R t , starting from March 1, using EpiEstim package in R. Results Using British Columbia as a reference, the incidence rate ratios in Alberta and Ontario are 3.1 and 4.3 among females, and 3.4 and 4.0 among males. In Ontario, R t fluctuated ~1 in March, reached values >1 in early and mid-April, then dropped <1 in late April and early May. R t rose to ~1 in mid-May and then remained <1 from late May through early July. In British Columbia, R t dropped <1 in early April, but it increased towards the end of April. R t <1 in May while it fluctuated around 1.0 in June and early July. In Alberta, R t > 1 in March; R t dropped in early April and rose again in late April. In much of May, R t <1, but R t increases in early June and fluctuates ~1 since mid-June. Conclusions R t wavering around 1.0 indicated that three provinces of Canada have managed to achieve limited onward transmission of SARS-CoV-2 as of early July 2020.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".