Simple Accurate Regression-Based Forecasting of Intensive Care Unit Admissions due to COVID-19 in Ontario, Canada
Bibliographic record
Abstract
Abstract The pandemic caused by SARS-CoV-2 has proven challenging clinically, and at the population level, due to heterogeneity in both transmissibility and severity. Recent case incidence in Ontario, Canada (autumn 2020) has outstripped incidence in seen during the first (spring) pandemic wave; but has been associated with a lower incidence of intensive care unit (ICU) admissions and deaths. We hypothesized that differential ICU burden might be explained by increased testing volumes, as well as the shift in mean case age from older to younger. We constructed a negative binomial regression model using only three covariates, at a 2-week lag: log 10 (weekly cases); log 10 (weekly deaths); and mean weekly case age. This model reproduced observed ICU admission volumes, and demonstrated good preliminary predictive validity. Furthermore, when admissions were used in combination with ICU length of stay, our modeled estimates demonstrated excellent convergent validity with ICU occupancy data reported by the Canadian Institute for Health Information. Our approach needs external validation in other settings and at larger and smaller geographic scales, but appears to be a useful short-term forecasting tool for ICU resource demand; we also demonstrate that the virulence of SARS-CoV-2 infection has not meaningfully changed in Ontario between the first and second waves, but the demographics of those infected, and the fraction of cases identified, have.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".