Objective: Since the identification of COVID-19 in December 2019 as a pandemic,
over 4500 research papers were published with the term “COVID-19” contained in
its title. Many of these reports on the COVID-19 pandemic suggested that the
coronavirus was associated with more serious chronic diseases and mortality
particularly in patients with chronic diseases regardless of country and age.
Therefore, there is a need to understand how common comorbidities and other
factors are associated with the risk of death due to COVID-19 infection. Our
investigation aims at exploring this relationship. Specifically, our analysis
aimed to explore the relationship between the total number of COVID-19 cases
and mortality associated with COVID-19 infection accounting for other risk
factors. Methods: Due to the presence of over dispersion, the Negative
Binomial Regression is used to model the aggregate number of COVID-19 cases.
Case-fatality associated with this infection is modeled as an outcome variable
using machine learning predictive multivariable regression. The data we used
are the COVID-19 cases and associated deaths from the start of the pandemic up
to December 02-2020, the day Pfizer was granted approval for their new COVID-19
vaccine. Results: Our analysis found significant regional variation in
case fatality. Moreover, the aggregate number of cases had several risk factors
including chronic kidney disease, population density and the percentage of
gross domestic product spent on healthcare. The Conclusions: There are
important regional variations in COVID-19 case fatality. We identified three
factors to be significantly correlated with case fatality.
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