Bibliographic record
Abstract
Since the outbreak of the COVID-19 in 2019, it has been a great challenge for the whole world.When the epidemic is serious and the vaccine will play a role, the statistic is an effective tool.It can help the government collect various data and conduct modelling analysis, so that it can face the actual situation and issue appropriate policies.This paper aims to analyse the factors that could affect the death rates among all COVID-19 confirmed cases in Ontario.Specifically, Seasonal ARIMA is used to fit past one-year data to predict short-term trend of confirmed case.An overall upward slope is predicted by selected time series model.Logistic regression is then used to determine how age group and vaccination could affect the mortality risk quantitatively.According to the information as of November 6, 2021, the forecast trend in the short term is expected to show an upward trend.In addition, age group and vaccination status significantly affect the probability of death of confirmed cases.The mortality increased with age.It has also been proved that the mortality of fully vaccinated patients is lower than that of partially vaccinated patients, followed by unvaccinated patients.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".