Reduced COVID-19-Related Critical Illness and Death, and High Risk of Epidemic Resurgence, After Physical Distancing in Ontario, Canada
Bibliographic record
Abstract
We explored the impact of physical distancing measures on COVID-19 transmission in the population of Ontario, Canada using a previously described age- and health-status stratified transmission model. The model was fit to confirmed cases occupying intensive care unit (ICU) beds and mortality among hospitalized COVID-19 cases for the time period 19 March to 26 April 2020. We projected that mortality would have been 4.6-fold what was observed had physical distancing measures not been implemented in the province. Relaxation of physical distancing measures without compensatory increases in case detection, isolation, and/or contact tracing was projected to result in resurgence of disease activity. Return to normal or near-normal levels of contact would rapidly result in cases exceeding ICU capacity. Maintaining physical distancing for a longer period of time, allowing for the initial wave of infections to subside, delayed this resurgence, but the level of contacts post-restrictive distancing was the major factor determining how quickly ICU capacity was expected to be overwhelmed. Using a model, we demonstrate the marked impact strong public health measures had in reducing ICU admissions and mortality in Ontario. We also show that this hard-earned success is tenuous: relaxation of physical distancing measures in the near-term is projected to result in a rapid resurgence of disease activity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".