Predictors of the Aggregate of COVID-19 Cases and Its Case-Fatality: A Global Investigation Involving 120 Countries

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