Estimating excess mortality in Canada during the COVID-19 pandemic: Statistical methods adapted for rapid response in an evolving crisis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents the approach adopted at Statistics Canada to produce timely and accurate estimates of excess mortality during the ongoing COVID-19 pandemic. It focuses primarily on the two models involved in the estimation of excess mortality: the model used to estimate the expected number of deaths in the absence of the pandemic (baseline mortality), and the model used to adjust provisional death counts for undercoverage. We describe both, including how the models were adapted to fit our specific criteria as well as the various limitations they both possess. We conclude by presenting selected results from Statistics Canada’s official release of excess mortality estimates from February 8 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="{}^{\text{th}}" display="inline" overflow="scroll"> <mml:msup> <mml:mi/> <mml:mtext>th</mml:mtext> </mml:msup> </mml:math> , 2021.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.250 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it