BCG Immunization Appears to Explain 26% of Variance in Cases of Covid19 per Capita
Bibliographic record
Abstract
Background: Biological scientists Gursel and Gursel have compiled very valuable tabular data showing that countries with national BCG immunization program have, per capita, significantly less cases and lower death toll from covid19.These authors also highlighted the importance of selecting the best BCG vaccines from among its various strains.With respect to statistical analysis, Gursel and Gursel's study reported only a significant p value: it is very important to recalculate their data to determine the actual correlational size, proportions of explained variance, and the effect sizes.This statistical information is crucial for determining if, or to what extent, the BCG vaccines could contribute to containing the covid19 pandemic.Method: We recalculated Gursel's tabular data to measure the proportion of variance explained by the underlying correlational relationships of BCG immunization to the per capita cases of covid19 and to the per capita death from the covid19.We also calculated the correlations of these two per capita rates to population density.Results and Discussion: Countries with current national BCG immunization programs had 6 times less per capita cases of covid19: the correlation coefficient is statistically significant, and of moderate strength (Pearson r=.51, p<.001, 1-tailed), and accounts for approximately 26.0% of variance.This indicates that BCG vaccination is an important strategy of reducing cases of covid19, especially if carried out with the best strains of the vaccine.The BCG vaccination is an important potential statistical predictor of covid19 cases.The death toll from covid19 was also 6 times lower in countries with national BCG vaccination: the correlation coefficient is also statistically significant, but its size is weak (Pearson r=.27, p=.049, 1-tailed) thus probably accounting for only 7.3% of variance.This suggests that the BCG vaccination is a less powerful (but not a negligible) predictor of lethal outcomes.Population density was unrelated to covid19 cases (Pearson r=.07, p=.377) and to the death toll (Pearson r=.05, p=.326).The BCG immunization programs are currently more common in low income countries than elsewhere.Death rate per capita divided by the number of cases per capita can serve as a predictor of lethal outcome of existing cases: this index was unrelated to presence or absence of the BCG programs (Pearson r=.03, p=.432).Conclusions: The number of covid19 cases and the related death toll are 6 times smaller in countries which have the national BCG coverage.The BCG vaccination is an important statistical predictor of proportions of covid19 cases per capita.This effect is very large (Cohen's d=1.19).The BCG vaccination is also associated with lower per capita death rate from covid19, but that statistical effect is of medium size (Cohen's d=0.56).The choice of the best vaccine strain is presumably crucial, as suggested by Gursel and Gursel: this needs further statistical research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".