The True Infection Mortality Rate of COVID-19 During the Spring 2020 Wave
Bibliographic record
Abstract
Abstract Estimations of the infection mortality rate of COVID-19, the disease caused by the SARS-CoV-2 virus, are prone to biases due to underdiagnosis, false positives, false negatives, and time lag between diagnosis and death. With a systematic analysis that combines epidemiological modeling of COVID-19 in Spain and in the state of New York, and results of random immunological testing in the spring of 2020 in both locations, most of the bias is eliminated and any remaining bias is evaluated and reported as an uncertainty estimate. A true infection mortality rate of 1.45 ± 0.45 % is obtained, representing an average for the two locations, and obtained with a different technique to minimize the effect of potential biases. In the absence of specific local data, this number can be used for the first wave of COVID-19 in OECD countries. This mortality rate estimate of the new coronavirus is sufficiently accurate to be used as a basis for policy decisions. When differences in age distribution between Spain and the state of New York are accounted for, tentative infection mortality rates of 1.18 ± 0.26 % and 1.94 ± 0.43 % are put forward for New York and Spain, respectively.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".