Age-Specific SARS-CoV-2 Infection Fatality and Case Identification Fraction in Ontario, Canada
Bibliographic record
Abstract
Abstract Background SARS-CoV-2 is a novel pandemic pathogen that displays great variability in virulence across cases. Due to limitations in diagnostic testing only a subset of infections are identified. Underestimation of true infections makes calculation of infection fatality ratios (IFR) challenging. Seroepidemiology allows estimation of true cumulative incidence of infection in populations, for estimation of IFR. Methods Seroprevalence estimates were derived using retention samples stored by Canadian Blood Services in May 2020. These were compared to non-long-term care-linked case and fatality data from the same period. Estimates were combined to generate IFR and case identification fraction estimates. Results Overall IFR was estimated to be 0.80% (0.75 to 0.85%), consistent with estimates from other jurisdictions. IFR increased exponentially with age from 0.01% (0.002 to 0.04%) in those aged 20-29 years, to 12.71% (4.43 to 36.50%) in those aged 70 and over. We estimated that 5.88 infections (3.70 to 9.21) occurred for every case identified, with a higher fraction of cases identified in those aged 70 and older (42.0%) than those aged 20-29 (9.4%). IFR estimates in those aged 60 and older were identical to pooled estimates from other countries. Conclusions To our knowledge these are the first Canadian estimates SARS-CoV-2 IFR and case identification fraction. Notwithstanding biases associated with donor sera they are similar to estimates from other countries, and approximately 80-fold higher than estimates for influenza A (H1N1) during the 2009 epidemic. Ontario’s first COVID-19 pandemic wave is likely to have been accurately characterized due to a high case identification fraction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".