International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
line decor
  
line decor

 Volume 9, Issue 5 (May 2022), Pages: 18-31

----------------------------------------------

 Original Research Paper

 Title: COVID-19 vaccine dosages and government factors role on the global variation in COVID-19 mortality: A statistical and regression analysis

 Author(s): Asif Hassan Syed 1, *, Tabrej Khan 2, Nashwan A. Alromema 1, Lalbihari Barik 2, Ahmad Abdul Qadir AlRababah 1, Murad M. Aljiffry 3

 Affiliation(s):

 1Department of Computer Science, Faculty of Computing and Information Technology Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi Arabia
 2Department of Information Sciences, Faculty of Computing and Information Technology Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi Arabia
 3Department of Surgery, Faculty of Medicine King Abdulaziz University, Jeddah, Saudia Arabia

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-7288-3098

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.05.003

 Abstract:

The objective of our study was to explore the influence of the current vaccination program and other relevant government factors to explain the variation in COVID-19 mortality in the world. The study involves a cross-sectional survey of COVID-19 related and government factors from 161 countries. We retrieved and processed publically available coronavirus pandemic data (July 17, 2021) from several online databases, excluding countries' data violating correlation and regression analysis assumptions. In addition, partial correlations studies and multivariate analysis were performed to explore the influence current vaccination program and other relevant government factors on the relationship between the explanatory variable and the total deaths due to COVID-19. The partial-correlation studies revealed that controlling for a complete dosage of COVID-19 vaccine per 100 people in the population had a significant (P<0.001)  impact on the strength of the relationship between some explanatory variables and the response variable (total COVID-19 mortality). Furthermore, the Stepwise Linear Regression (SLR) model shows that the covariates, namely total_cases, hospital patients per million, hospital beds per thousand, male smokers, and people fully vaccinated per hundred, added significantly (P<0.001) to the prediction of the response variable. Our SLR model validation study revealed that the observed total COVID-19 mortality was highly correlated with the predicted total COVID-19 mortality in various countries (r = 0.977, P<0.001). Our Stepwise Linear Regression model performs significantly better with an R-squared value of 0.958 and adjusted R-squared value of 0.956 than other related regression models built to predict COVID-19 mortality. Based on our current findings, we conclude that governments with better hospital infrastructure and people with complete dosages of the COVID-19 vaccine will have minimal COVID-19 fatalities. 

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: COVID-19, COVID-19 mortality variation, Cross-sectional data, Partial-correlation, Controlling factor, COVID-19 vaccine dosages, Stepwise regression model

 Article History: Received 22 October 2021, Received in revised form 1 February 2022, Accepted 1 March 2022

 Acknowledgment 

This work was supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under Grant G:-186-132-1442. The authors, therefore, gratefully acknowledge DSR’s technical and financial support.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Syed AH, Khan T, and Alromema NA et al. (2022). COVID-19 vaccine dosages and government factors role on the global variation in COVID-19 mortality: A statistical and regression analysis. International Journal of Advanced and Applied Sciences, 9(5): 18-31

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 

----------------------------------------------    

 References (51)

  1. Alwafi H, Naser AY, Qanash S, Brinji AS, Ghazawi MA, Alotaibi B, and Shabrawishi M (2021). Predictors of length of hospital stay, mortality, and outcomes among hospitalised COVID-19 patients in Saudi Arabia: A cross-sectional study. Journal of Multidisciplinary Healthcare, 14: 839-852. https://doi.org/10.2147/JMDH.S304788   [Google Scholar] PMid:33883900 PMCid:PMC8055273
  2. Beasley TM, Erickson S, and Allison DB (2009). Rank-based inverse normal transformations are increasingly used, but are they merited? Behavior Genetics, 39(5): 580-595. https://doi.org/10.1007/s10519-009-9281-0   [Google Scholar] PMid:19526352 PMCid:PMC2921808
  3. Breusch TS and Pagan AR (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica: Journal of the Econometric Society, 47: 1287-1294. https://doi.org/10.2307/1911963   [Google Scholar]
  4. Buuren VS (2018). Flexible imputation of missing data. CRC Press, Boca Raton, USA.   [Google Scholar]
  5. Buuren VS and Groothuis-Oudshoorn K (2011). Mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3): 1-67. https://doi.org/10.18637/jss.v045.i03   [Google Scholar]
  6. Casson RJ and Farmer LD (2014). Understanding and checking the assumptions of linear regression: A primer for medical researchers. Clinical and Experimental Ophthalmology, 42(6): 590-596. https://doi.org/10.1111/ceo.12358   [Google Scholar] PMid:24801277
  7. Challen R, Brooks-Pollock E, Read JM, Dyson L, Tsaneva-Atanasova K, and Danon L (2021). Risk of mortality in patients infected with SARS-CoV-2 variant of concern 202012/1: Matched cohort study. British Medical Association: Peer-Reviewed Journal, 372: n579. https://doi.org/10.1136/bmj.n579   [Google Scholar] PMid:33687922 PMCid:PMC7941603
  8. Chang C, Deng Y, Jiang X, and Long Q (2020). Multiple imputation for analysis of incomplete data in distributed health data networks. Nature Communications, 11(1): 1-11. https://doi.org/10.1038/s41467-020-19270-2   [Google Scholar] PMid:33122624 PMCid:PMC7596726
  9. Chen T, Wu DI, Chen H, Yan W, Yang D, Chen G, and Ning Q (2020). Clinical characteristics of 113 deceased patients with coronavirus disease 2019: Retrospective study. British Medical Association: Peer-Reviewed Journal, 2020: 368. https://doi.org/10.1136/bmj.m1091   [Google Scholar] PMid:32217556 PMCid:PMC7190011
  10. Chodick G, Tene L, Patalon T, Gazit S, Tov AB, Cohen D, and Muhsen K (2021). Assessment of effectiveness of 1 dose of BNT162b2 vaccine for SARS-CoV-2 infection 13 to 24 days after immunization. JAMA Network Open, 4(6): e2115985-e2115985. https://doi.org/10.1001/jamanetworkopen.2021.15985   [Google Scholar] PMid:34097044 PMCid:PMC8185600
  11. Dagan N, Barda N, Kepten E, Miron O, Perchik S, Katz MA, and Balicer RD (2021). BNT162b2 mRNA Covid-19 vaccine in a nationwide mass vaccination setting. New England Journal of Medicine, 384(15): 1412-1423. https://doi.org/10.1056/NEJMoa2101765   [Google Scholar] PMid:33626250 PMCid:PMC7944975
  12. Davies NG, Abbott S, Barnard RC, Jarvis CI, Kucharski AJ, Munday JD, and Edmunds WJ (2021b). Estimated transmissibility and impact of SARS-CoV-2 lineage B. 1.1. 7 in England. Science, 372(6538). https://doi.org/10.1126/science.abg3055   [Google Scholar] PMid:33658326 PMCid:PMC8128288
  13. Davies NG, Jarvis CI, Edmunds WJ, Jewell NP, Diaz-Ordaz K, and Keogh RH (2021a). Increased mortality in community-tested cases of SARS-CoV-2 lineage B. 1.1. 7. Nature, 593(7858): 270-274. https://doi.org/10.1038/s41586-021-03426-1   [Google Scholar] PMid:33723411
  14. Davis JW (2008). Medical statistics: A textbook for the health sciences. The American Statistician, 62(4): 362. https://doi.org/10.1198/tas.2008.s274   [Google Scholar]
  15. Dorp, C. H. V., Dorp CH, Goldberg EE, Hengartner N, Ke R, and Romero-Severson EO (2021). Estimating the strength of selection for new SARS-CoV-2 variants. Nature Communications, 12(1): 1-13. https://doi.org/10.1038/s41467-021-27369-3   [Google Scholar] PMid:34907182 PMCid:PMC8671537
  16. Ghasemi A and Zahediasl S (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2): 486-489. https://doi.org/10.5812/ijem.3505   [Google Scholar] PMid:23843808 PMCid:PMC3693611
  17. Grentzelos C, Caroni C, and Barranco‐Chamorro I (2021). A comparative study of methods to handle outliers in multivariate data analysis. Computational and Mathematical Methods, 3(3): e1129. https://doi.org/10.1002/cmm4.1129   [Google Scholar]
  18. Grubaugh ND, Hodcroft EB, Fauver JR, Phelan AL, and Cevik M (2021). Public health actions to control new SARS-CoV-2 variants. Cell, 184(5): 1127-1132. https://doi.org/10.1016/j.cell.2021.01.044   [Google Scholar] PMid:33581746 PMCid:PMC7846239
  19. Haas EJ, Angulo FJ, McLaughlin JM, Anis E, Singer SR, Khan F, and Alroy-Preis S (2021). Impact and effectiveness of mRNA BNT162b2 vaccine against SARS-CoV-2 infections and COVID-19 cases, hospitalisations, and deaths following a nationwide vaccination campaign in Israel: An observational study using national surveillance data. The Lancet, 397(10287): 1819-1829. https://doi.org/10.1016/S0140-6736(21)00947-8   [Google Scholar]
  20. Hocking RR (1976). A Biometrics invited paper: The analysis and selection of variables in linear regression. Biometrics, 32: 1-49. https://doi.org/10.2307/2529336   [Google Scholar]
  21. Hopkinson NS, Rossi N, El-Sayed_Moustafa J, Laverty AA, Quint JK, Freidin M, and Falchi M (2021). Current smoking and COVID-19 risk: Results from a population symptom app in over 2.4 million people. Thorax, 76(7): 714-722. https://doi.org/10.1136/thoraxjnl-2020-216422   [Google Scholar] PMid:33402392 PMCid:PMC7789201
  22. Hou C, Chen J, Zhou Y, Hua L, Yuan J, He S, and Jia E (2020). The effectiveness of quarantine of Wuhan city against the Corona Virus Disease 2019 (COVID‐19): A well‐mixed SEIR model analysis. Journal of Medical Virology, 92(7): 841-848. https://doi.org/10.1002/jmv.25827   [Google Scholar] PMid:32243599
  23. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, and Cao B (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395(10223): 497-506. https://doi.org/10.1016/S0140-6736(20)30183-5   [Google Scholar]
  24. Iacobucci G (2020). COVID-19: UK lockdown is “crucial” to saving lives, say doctors and scientists. British Medical Association: Peer-Reviewed Journal, 368: m1091. https://doi.org/10.1136/bmj.m1204   [Google Scholar] PMid:32209548
  25. James G, Witten D, Hastie T, and Tibshirani R (2013). An introduction to statistical learning. Volume 112, Springer, New York, USA. https://doi.org/10.1007/978-1-4614-7138-7   [Google Scholar]
  26. Ji Y, Ma Z, Peppelenbosch MP, and Pan Q (2020). Potential association between COVID-19 mortality and health-care resource availability. The Lancet Global Health, 8(4): e480. https://doi.org/10.1016/S2214-109X(20)30068-1   [Google Scholar]
  27. Kandel N, Chungong S, Omaar A, and Xing J (2020). Health security capacities in the context of COVID-19 outbreak: An analysis of International Health Regulations annual report data from 182 countries. The Lancet, 395(10229): 1047-1053. https://doi.org/10.1016/S0140-6736(20)30553-5   [Google Scholar]
  28. Kim HY (2013). Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry and Endodontics, 38(1): 52-54. https://doi.org/10.5395/rde.2013.38.1.52   [Google Scholar] PMid:23495371 PMCid:PMC3591587
  29. Kim HY (2017). Statistical notes for clinical researchers: Chi-squared test and Fisher's exact test. Restorative Dentistry and Endodontics, 42(2): 152-155. https://doi.org/10.5395/rde.2017.42.2.152   [Google Scholar] PMid:28503482 PMCid:PMC5426219
  30. Liang LL, Tseng CH, Ho HJ, and Wu CY (2020). COVID-19 mortality is negatively associated with test number and government effectiveness. Scientific Reports, 10(1): 1-7. https://doi.org/10.1038/s41598-020-68862-x   [Google Scholar] PMid:32709854 PMCid:PMC7381657
  31. Lipsitch M, Swerdlow DL, and Finelli L (2020). Defining the epidemiology of COVID-19-Studies needed. New England Journal of Medicine, 382(13): 1194-1196. https://doi.org/10.1056/NEJMp2002125   [Google Scholar] PMid:32074416
  32. Maesschalck DR, Jouan-Rimbaud D, and Massart DL (2000). The mahalanobis distance. Chemometrics and Intelligent Laboratory Systems, 50(1): 1-18. https://doi.org/10.1016/S0169-7439(99)00047-7   [Google Scholar]
  33. Mishra P, Pandey CM, Singh U, Gupta A, Sahu C, and Keshri A (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1): 67-72. https://doi.org/10.4103/aca.ACA_157_18   [Google Scholar] PMid:30648682 PMCid:PMC6350423
  34. Moghadas SM, Shoukat A, Fitzpatrick MC, Wells CR, Sah P, Pandey A, and Galvani AP (2020). Projecting hospital utilization during the COVID-19 outbreaks in the United States. Proceedings of the National Academy of Sciences, 117(16): 9122-9126. https://doi.org/10.1073/pnas.2004064117   [Google Scholar] PMid:32245814 PMCid:PMC7183199
  35. O'Toole Á, Hill V, Pybus OG, Watts A, Bogoch II, Khan K, and COVID T (2021). Tracking the international spread of SARS-CoV-2 lineages B. 1.1. 7 and B. 1.351/501Y-V2. Wellcome Open Research, 6: 121.   [Google Scholar]
  36. Peto J (2020). COVID-19 mass testing facilities could end the epidemic rapidly. British Medical Association: Peer-Reviewed Journal, 368: m1163. https://doi.org/10.1136/bmj.m1163   [Google Scholar] PMid:32201376
  37. Planas D, Veyer D, Baidaliuk A, Staropoli I, Guivel-Benhassine F, Rajah MM, and Schwartz O (2021). Reduced sensitivity of SARS-CoV-2 variant Delta to antibody neutralization. Nature, 596(7871): 276-280. https://doi.org/10.1038/s41586-021-03777-9   [Google Scholar] PMid:34237773
  38. Priesemann V, Balling R, Brinkmann MM, Ciesek S, Czypionka T, Eckerle I, and Szczurek E (2021). An action plan for pan-European defence against new SARS-CoV-2 variants. The Lancet, 397(10273): 469-470. https://doi.org/10.1016/S0140-6736(21)00150-1   [Google Scholar]
  39. Rossman H, Meir T, Somer J, Shilo S, Gutman R, Arie AB, and Gorfine M (2021). Hospital load and increased COVID-19 related mortality in Israel. Nature Communications, 12: 1904. https://doi.org/10.1038/s41467-021-22214-z   [Google Scholar] PMid:33771988 PMCid:PMC7997985
  40. Rubino S, Kelvin N, Bermejo-Martin JF, and Kelvin D (2020). As COVID-19 cases, deaths and fatality rates surge in Italy, underlying causes require investigation. The Journal of Infection in Developing Countries, 14(03): 265-267. https://doi.org/10.3855/jidc.12734   [Google Scholar] PMid:32235086
  41. Sarkodie SA and Owusu PA (2020). Investigating the cases of novel coronavirus disease (COVID-19) in China using dynamic statistical techniques. Heliyon, 6(4): e03747. https://doi.org/10.1016/j.heliyon.2020.e03747   [Google Scholar] PMid:32289090 PMCid:PMC7128585
  42. Sen-Crowe B, Sutherland M, McKenney M, and Elkbuli A (2021). A closer look into global hospital beds capacity and resource shortages during the COVID-19 pandemic. Journal of Surgical Research, 260: 56-63. https://doi.org/10.1016/j.jss.2020.11.062   [Google Scholar] PMid:33321393 PMCid:PMC7685049
  43. Shapiro SS and Wilk MB (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4): 591-611. https://doi.org/10.1093/biomet/52.3-4.591   [Google Scholar]
  44. Singh J, Rahman SA, Ehtesham NZ, Hira S, and Hasnain SE (2021). SARS-CoV-2 variants of concern are emerging in India. Nature Medicine, 27: 131–1133. https://doi.org/10.1038/s41591-021-01397-4   [Google Scholar] PMid:34045737
  45. Stephens MA (1974). EDF statistics for goodness of fit and some comparisons. Journal of the American Statistical Association, 69(347): 730-737. https://doi.org/10.1080/01621459.1974.10480196   [Google Scholar]
  46. Vatcheva KP, Lee M, McCormick JB, and Rahbar MH (2016). Multicollinearity in regression analyses conducted in epidemiologic studies. Epidemiology (Sunnyvale, Calif.), 6(2): 227. https://doi.org/10.4172/2161-1165.1000227   [Google Scholar] PMid:27274911 PMCid:PMC4888898
  47. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, and Peng Z (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. Journal of the American Medical Association: Peer-Reviewed Journal, 323(11): 1061-1069. https://doi.org/10.1001/jama.2020.1585   [Google Scholar] PMid:32031570 PMCid:PMC7042881
  48. Ward H, Atchison C, Whitaker M, Ainslie KE, Elliott J, Okell L, and Elliott P (2021). SARS-CoV-2 antibody prevalence in England following the first peak of the pandemic. Nature Communications, 12: 905. https://doi.org/10.1038/s41467-021-21237-w   [Google Scholar] PMid:33568663 PMCid:PMC7876103
  49. Westen-Lagerweij VNA, Meijer E, Meeuwsen EG, Chavannes NH, Willemsen MC, and Croes EA (2021). Are smokers protected against SARS-CoV-2 infection (COVID-19)? The origins of the myth. NPJ Primary Care Respiratory Medicine, 31: 10. https://doi.org/10.1038/s41533-021-00223-1   [Google Scholar] PMid:33637750 PMCid:PMC7910565
  50. Winblad B, Palmer K, Kivipelto M, Jelic V, Fratiglioni L, Wahlund LO, and Petersen RC (2004). Mild cognitive impairment–Beyond controversies, towards a consensus: Report of the international working group on mild cognitive impairment. Journal of Internal Medicine, 256(3): 240-246. https://doi.org/10.1111/j.1365-2796.2004.01380.x   [Google Scholar] PMid:15324367
  51. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, and Cao B (2020). Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. The Lancet, 395(10229): 1054-1062. https://doi.org/10.1016/S0140-6736(20)30566-3   [Google Scholar]