Patterns and descriptors of COVID-19 testing and lab-confirmed COVID-19 incidence in Manitoba, Canada, March 2020-May 2021: A population-based study
Bibliographic record
Abstract
BACKGROUND: We studied lab-confirmed COVID-19 infection (LCCI) testing, incidence, and severity. METHODS: We included all Manitoba residents and limited our severity analysis to LCCI patients. We calculated testing, incidence and vaccination rates between March 8, 2020 and June 1, 2021. We estimated the association between patient characteristics and testing (rate ratio [RR]; Poisson regression), including the reason for testing (screening, symptomatic, contact/outbreak asymptomatic), incidence (hazard ratio [HR]; Cox regression), and severity (prevalence ratio [PR], Cox regression). FINDINGS: The overall testing rate during the second/third wave was 570/1,000 person-years, with an LCCI rate of 50/1,000 person-years. The secondary attack rate during the second/third wave was 16%. Across regions, young children (<10) had the lowest positivity for symptomatic testing, the highest positivity for asymptomatic testing, and the highest risk of LCCI as asymptomatic contact. People in the lowest income quintile had the highest risk of LCCI, 1.3-6x the hazard of those in the highest income quintile. Long-term care (LTC) residents were particularly affected in the second wave with HRs>10 for asymptomatic residents. INTERPRETATION: Although the severity of LCCI in children was low, they have a high risk of asymptomatic positivity. The groups most vulnerable to LCCI, who should remain a focus of public health, were residents of Manitoba's North, LTC facilities, and low-income neighbourhoods. FUNDING: Canada Research Chair Program.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".