A Joint Temporal Model for Hospitalizations and ICU Admissions Due to COVID‐19 in Quebec
Bibliographic record
Abstract
ABSTRACT Infectious respiratory diseases have been of interest in recent years for the great burden they place on health systems, for instance, the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) that caused the global COVID‐19 pandemic. As many of these diseases might require hospitalization and even intensive care unit (ICU) admission, understanding the joint dynamics of hospitalizations and ICU admissions across time and different groups of the population remains of great importance. We aim to understand the joint evolution of hospital and ICU admissions given COVID‐19 test‐positive cases in the province of Quebec, Canada. We obtain the daily counts, by age group, on the number of confirmed COVID‐19 cases, the number of hospitalizations and the number of ICU admissions due to COVID‐19, from March 2020 through October 2021 in Quebec. We propose a joint Bayesian generalized dynamic linear model for the number of hospitalizations and ICU admissions to study their temporal trends and possible associations with sex and age group. Additionally, we use transfer functions to investigate if there is a memory effect of the number of cases on hospitalizations across the different age groups. The results suggest that there is a clear distinction in the patterns of hospitalizations and ICU admissions across age groups and that the number of cases has a persistent effect on the rate of hospitalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".