COVID-19 Vaccinations: Efficacy and Financial Benefits (The Case of the Pharmaceutical Companies)
Bibliographic record
Abstract
This paper empirically investigates two sides of the COVID-19 vaccinations. The first part is a study of the vaccination efficacy and its impact on the number of new cases and new deaths, using the daily doses data of Eight different COVID-19 vaccination for a sample of 30 countries around the world. The second part is a study of the financial benefits for the same eight vaccines producers’ companies. The event study method is adopted in this research to explore the abnormal returns and its accumulations, the event date is January 30, 2020. The Vaccine’s efficacy results show that (Moderna, Oxford/Astrazeneca, and Novavax V) proved efficacy in reducing new cases and new deaths. Meanwhile, Cansino’s vaccine was effective in reducing the number of new cases only. On the other hand, vaccines like (Pfizer/ Biontech, Sinovac, Johnson & Johnson, and Sinopharm/ Beijing) didn’t prove efficacy in reducing the number of new cases and new deaths. The Financial benefits results show that the vaccine manufacturers who achieved the benefits of abnormal returns in the presence of vaccine efficacy are (Oxford/AstraZeneca and Novavax). While other manufacturers did not achieve the benefits of abnormal returns. The results also show that the pharmaceutical companies that achieved benefits on the cumulative abnormal returns in the presence of the vaccine efficacy are (Oxford/AstraZeneca, Novavax, Moderna, and CanSino). Meanwhile, the rest of manufacturers achieved cumulative abnormal returns but they are not effective in reducing the numbers of neither new cases nor new deaths.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".