Carbon dioxide (CO2) rate within the atmosphere has been rising for decades due to human activities especially due to usage of fuel types such as coal, cement, flaring, gas, oil, etc. Especially in 2020, COVID-19 pandemic caused major economic, production, and energy crises all around the world. As a result of this situation, there was a sharp decrease in the global CO2 emissions depending on the fuel types used during this pandemic. The aim of this study was to explore the effects of “CO2 emissions due to the fuel types” on “percentage of deaths in total cases” attributed to the COVID-19 pandemic using generalized linear model and generalized linear mixed model (GLMM) approaches with inverse Gaussian and gamma distributions, and also to obtain global statistical inferences about 169 World Health Organization member countries that will disclose the impact of the CO2 emissions due to the fuel types during this pandemic. The response variable is taken as “percentage of deaths in total cases attributed to the COVID-19 pandemic” calculated as “(total deaths/total confirmed cases attributed to the COVID-19 pandemic until December 31, 2020)*100.” The explanatory variables are taken as “production-based emissions of CO2 from different fuel types,” measured in tonnes per person, which are “coal, cement, flaring, gas, and oil.” As a result of this study, according to the goodness-of-fit test statistics, “GLMM approach with gamma distribution” called “gamma mixed regression model” is determined as the most appropriate statistical model for investigating the impact of CO2 emissions on the COVID-19 pandemic. As the main findings of this study, 1 t CO2 emissions belonging to the fuel types “cement, coal, flaring, gas, and oil” per person cause increase in deaths in total cases attributed to the COVID-19 pandemic by 2.8919, 2.6151, 2.5116, 2.5774, and 2.5640%, respectively.
In this study, the effects of disability-adjusted life years (DALYs) from neoplasms and concomitant non-communicable diseases (NCDs) on total deaths from the COVID-19 pandemic until 21 July 2021 are examined globally for 179 countries. For this purpose, the explanatory variables are taken as DALYs as a measure of total burden of diseases in life lost years and lived with a disability years from neoplasm and NCDs. In this study, the total number of deaths caused by the COVID-19 pandemic has been made categorical with the help of the indicator variable and then taken as the response variable. Thus, in this study, the effects of neoplasms and concomitant NCDs on the COVID-19 pandemic are investigated by using binary logit and binary probit regression models in the family of generalized linear models (GLMs) as statistical methods. Specific to this study, the superiority of the probit model which is based on the assumption that the errors have a normal distribution in the statistical sense over the logit model which is based on the assumption that the errors have a logistic distribution is emphasized. As principle results and major conclusion from this study, neoplasms, cirrhosis and other chronic liver diseases, cardiovascular diseases, skin and subcutaneous diseases and other non-communicable diseases have been found to have statistically significant effects on deaths due to the COVID-19 pandemic.
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