The correlation between Australian Excess Deaths by State and Booster Vaccinations
Main Article Content
Abstract
The study explores the relationship by Australia State between COVID Booster Vaccinations and excess deaths. There is evidence of a very strong correlation in ordinary least squares regression analysis. Cross-validation tests support the strength of the regression relationship. The results suggest that it would be worthwhile to explore these associations in greater depth as it is an important public health issue.
Article Details
How to Cite
ALLEN, David Edmund.
The correlation between Australian Excess Deaths by State and Booster Vaccinations.
Medical Research Archives, [S.l.], v. 12, n. 7, july 2024.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/5485>. Date accessed: 21 dec. 2024.
doi: https://doi.org/10.18103/mra.v12i7.5485.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
1. Allen, D.E. Excess Deaths and Excess Covid Booster Vaccine Doses – are they related?, Medical Research Archives, 2023. Vol 11, Issue 12, https://doi.org/10.18103/mra.v11i12.4841.
2. Gao, F., V. Mallajosyula, P.S. Arunachalam, K. van der Ploeg, M. Manohar, K. Ro¤ ltgen, F. Yang, O. Wirz, R. Hoh, E. Haraguchi, J-Y.‘ Lee, R. Willis, V. Ramachandiran, J. Li, K. R. Kathuria, C. Li, A. S. Lee, M.M.. Shah, S.B. Sindher, J. Gonzalez, J.D. Altman, T.T. Wang, S.D. Boyd, B. Pulendran, P. Jagannathan, K.C. Nadeau, and M..M. Davis. Spheromers reveal robust T cell responses to the Pfizer/BioNTech vaccine and attenuated peripheral CD8+ T cell responses post SARS-CoV-2 infection, Immunity 56, 2023. 864–878, https://doi.org/10.1016/j.immuni.2023.03.005.
3. Krug A, Stevenson J, Høeg T.B. BNT162b2 Vaccine-Associated Myo/Pericarditis in Adolescents: A Stratified Risk-Benefit Analysis, Eur J Clin Invest. 2022;52:e13759. https://doi.org/10.1111/eci.13759
4. Chennupati, K., Bendi S., Gantasala P., Kickloo A., Navigating the Gastrointestinal Implications of Coronavirus 19: Strategies and Lessons Learned, Medical Research Archives, 2024. [online] 12(5). https://doi.org/10.18103/mra.v12i5.5342
5. Pillay J, Gaudet L. Wingert A., Bialy L, Mackie A S, Paterson I.D, Hartling L, Incidence, risk factors, natural history, and hypothesised mechanisms of myocarditis and pericarditis following covid-19 vaccination: living evidence syntheses and review, BMJ 2022; 378: e069445 | doi: 10.1136/bmj-2021-069445
6. Mostert S., Hoogland M., Huibers M., Kaspers G., Excess mortality across countries in the Western World since the COVID-19 pandemic: ‘Our World in Data’ estimates of January 2020 to December 2022. BMJ Public Health 2024;2:e000282. doi:10.1136/ bmjph-2023-000282
7. Yamamoto, K. Adverse effects of COVID-19 vaccines and measures to prevent them, Virology Journal, 2022. 19:100, https://doi.org/10.1186/s12985022-01831-0.
8. Bates, S. Hastie T. and Tibshirani, R. Cross-Validation: What Does It Estimate and How Well Does It Do It? Journal of the American Statistical Association, 2023. 1-12. https://doi.org/10.1080/01621459.2023.2197686
9. Zhang, P. Model Selection Via Multifold Cross Validation, The Annals of Statistics, 1993. 21, 299–313.
10. Zhang, P. Assessing Prediction Error in Non-parametric Regression, Scandinavian Journal of Statistics, 1995. 22, 83–94. [2,4]
11. Hastie, T., Tibshirani, R., and Friedman, J. The Elements of Statistical Learning (2nd ed.), New York: Springer. 2009. [2,4]
12. Yousef, W. A. A Leisurely Look at Versions and Variants of the Cross Validation Estimator, 2020. arXiv preprint. arXiv:1907.13413. [2,4]
13. Rosset, S., and Tibshirani, R. J. From Fixed-x to Random-x Regression: Bias-Variance Decompositions, Covariance Penalties, and Prediction Error Estimation, Journal of the American Statistical Association, 2020.115, 138–151. [2,6]
14. Wager, S. Cross-Validation, Risk Estimation, and Model Selection: Comment on a Paper by Rosset and Tibshirani, Journal of the American Statistical Association, 2020. 115, 157–160. [2,3,5,6]
15. Fox J, Monette G. _cv: Cross-Validation of Regression Models_. R package version 1.1.0, 2024..
2. Gao, F., V. Mallajosyula, P.S. Arunachalam, K. van der Ploeg, M. Manohar, K. Ro¤ ltgen, F. Yang, O. Wirz, R. Hoh, E. Haraguchi, J-Y.‘ Lee, R. Willis, V. Ramachandiran, J. Li, K. R. Kathuria, C. Li, A. S. Lee, M.M.. Shah, S.B. Sindher, J. Gonzalez, J.D. Altman, T.T. Wang, S.D. Boyd, B. Pulendran, P. Jagannathan, K.C. Nadeau, and M..M. Davis. Spheromers reveal robust T cell responses to the Pfizer/BioNTech vaccine and attenuated peripheral CD8+ T cell responses post SARS-CoV-2 infection, Immunity 56, 2023. 864–878, https://doi.org/10.1016/j.immuni.2023.03.005.
3. Krug A, Stevenson J, Høeg T.B. BNT162b2 Vaccine-Associated Myo/Pericarditis in Adolescents: A Stratified Risk-Benefit Analysis, Eur J Clin Invest. 2022;52:e13759. https://doi.org/10.1111/eci.13759
4. Chennupati, K., Bendi S., Gantasala P., Kickloo A., Navigating the Gastrointestinal Implications of Coronavirus 19: Strategies and Lessons Learned, Medical Research Archives, 2024. [online] 12(5). https://doi.org/10.18103/mra.v12i5.5342
5. Pillay J, Gaudet L. Wingert A., Bialy L, Mackie A S, Paterson I.D, Hartling L, Incidence, risk factors, natural history, and hypothesised mechanisms of myocarditis and pericarditis following covid-19 vaccination: living evidence syntheses and review, BMJ 2022; 378: e069445 | doi: 10.1136/bmj-2021-069445
6. Mostert S., Hoogland M., Huibers M., Kaspers G., Excess mortality across countries in the Western World since the COVID-19 pandemic: ‘Our World in Data’ estimates of January 2020 to December 2022. BMJ Public Health 2024;2:e000282. doi:10.1136/ bmjph-2023-000282
7. Yamamoto, K. Adverse effects of COVID-19 vaccines and measures to prevent them, Virology Journal, 2022. 19:100, https://doi.org/10.1186/s12985022-01831-0.
8. Bates, S. Hastie T. and Tibshirani, R. Cross-Validation: What Does It Estimate and How Well Does It Do It? Journal of the American Statistical Association, 2023. 1-12. https://doi.org/10.1080/01621459.2023.2197686
9. Zhang, P. Model Selection Via Multifold Cross Validation, The Annals of Statistics, 1993. 21, 299–313.
10. Zhang, P. Assessing Prediction Error in Non-parametric Regression, Scandinavian Journal of Statistics, 1995. 22, 83–94. [2,4]
11. Hastie, T., Tibshirani, R., and Friedman, J. The Elements of Statistical Learning (2nd ed.), New York: Springer. 2009. [2,4]
12. Yousef, W. A. A Leisurely Look at Versions and Variants of the Cross Validation Estimator, 2020. arXiv preprint. arXiv:1907.13413. [2,4]
13. Rosset, S., and Tibshirani, R. J. From Fixed-x to Random-x Regression: Bias-Variance Decompositions, Covariance Penalties, and Prediction Error Estimation, Journal of the American Statistical Association, 2020.115, 138–151. [2,6]
14. Wager, S. Cross-Validation, Risk Estimation, and Model Selection: Comment on a Paper by Rosset and Tibshirani, Journal of the American Statistical Association, 2020. 115, 157–160. [2,3,5,6]
15. Fox J, Monette G. _cv: Cross-Validation of Regression Models_. R package version 1.1.0, 2024.