Analysis of Potential Risk Factors of COVID-19 Based on Variants: Omicron, Delta, and Alpha
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has changed which affects the risk of COVID-19 infection for specific subgroups. We focused on the subgroups based on the factors (sex, age, and vaccination) and COVID-19 strains (Alpha, Delta, and Omicron). Past studies focused on analysing these factors based on one geographic region or one COVID-19 strain. Therefore, there is a need to understand these factors’ association with risk of COVID-19 infection through analysing data from various geographic regions and strains. The association between COVID-19 strains and the factors was assessed through chi-square test and odds ratio tests. Sex, vaccination, age had a significant association with testing positive for the COVID-19 strains of interest in most geographies. The biggest difference was unvaccinated individuals have 3.14 higher odds of getting Alpha than vaccinated individuals in Canada. These findings provide insights into the groups that are more susceptible to contracting specific strains of COVID-19.The coronavirus disease 2019 (COVID-19) pandemic has changed which affects the risk of COVID-19 infection for specific subgroups. We focused on the subgroups based on the factors (sex, age, and vaccination) and COVID-19 strains (Alpha, Delta, and Omicron). Past studies focused on analysing these factors based on one geographic region or one COVID-19 strain. Therefore, there is a need to understand these factors’ association with risk of COVID-19 infection through analysing data from various geographic regions and strains. The association between COVID-19 strains and the factors was assessed through chi-square test and odds ratio tests. Sex, vaccination, age had a significant association with testing positive for the COVID-19 strains of interest in most geographies. The biggest difference was unvaccinated individuals have 3.14 higher odds of getting Alpha than vaccinated individuals in Canada. These findings provide insights into the groups that are more susceptible to contracting specific strains of COVID-19.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".