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. (C) 2022 The Authors. Published by IASE.