The Impact of Age Demographics on Interpreting and Applying Population-Wide Infection Fatality Rates for COVID-19
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
The ongoing coronavirus disease 2019 (COVID-19) pandemic affects the Canadian Armed Forces (CAF) and its members in multiple ways. As the CAF manages its own healthcare system for its members, it must consider the impact of COVID-19 not only on the operational effectiveness of its workforce but also on its healthcare operations. Furthermore, given that the CAF has deployed task forces in support of other government departments, including into long-term care facilities that are experiencing outbreaks, it is important for the CAF to maintain situational awareness of the outbreak in the Canadian population generally. In providing analytical support to the CAF on these questions, we focused on establishing the applicability of estimates of COVID-19 infection fatality rates (IFRs) from the literature to the CAF and to the Canadian public. This paper explores how the age-dependent effects of COVID-19 must be taken into account when comparing estimates based on countries with very different age profiles, such as China and Italy. Furthermore, it explores how varying age structures within a country (e.g., within a subnational jurisdiction, or within a given working population) should affect how analysts apply estimates of IFR to scenarios involving those specific populations.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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