Analyses of Factors Affecting Deaths Associated with COVID-19 in Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it