Epidemic Curves and COVID-19: How to Reduce The Confusion
Bibliographic record
Abstract
Abstract Introduction: Epidemic curves have played a central role in comparing COVID-19 burden and progression across cities, states, and countries. Methods: We created a series of epidemic curves for Québec and Ontario, comparing and contrasting different COVID-19 outcomes. Results: The different epidemic curves of COVID-19 revealed that crude incidence rates displayed larger differences between the two provinces compared to absolute counts. More notable differences between Ontario and Québec were demonstrated when comparing crude rates of hospitalizations to crude rates of confirmed cases in each province. Crude daily hospitalizations revealed twice the magnitude of hospitalizations for Québec from April to May when compared to Ontario. Conclusions: We recommend using crude rates of hospitalizations, intensive care unit admissions, and mortality for COVID-19 epidemic curve comparison as they reveal important patterns in disease trends, and are more easily comparable between health jurisdictions. A harmonized approach to data presentation is important to not only accurately compare the progression of the pandemic, but also for interpretation of the media and the general public.
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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 | 0.099 | 0.461 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.021 | 0.054 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".