Predicting COVID-19 Induced Mortality Utilizing Vaccination Status Data
Bibliographic record
Abstract
COVID-19 is a prevalent pandemic that has caused of millions of mortalities. While medical and statistical research have proven the effectiveness of COVID vaccines to reduce mortality rate in specific areas. This study aims to explore the quantitative effect of vaccine in preventing in the global level. Mortality and vaccine data was acquired from Kaggle, which summarized related data from various sources. Multiple linear regression, quasi-Poisson regression, LASSO regression, and random forest are applied to the model to analyze and predict the effect of vaccine on mortality. The adjusted R square value of these four models is 0.6670, 0.6863, 0.4901, and 0.7152 respectively. The residual boxplots of all four models show that LASSO model sometimes make extremely inaccurate predictions, and the random forest model is the most accurate model, which matches the comparison result of the adjusted R square values. Therefore, the random forest model can be potentially used to predict future COVID-related deaths based on vaccine-related data.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".