Evaluating Factors Influencing COVID-19 Outcomes across Countries Using Decision Trees (Student Abstract)

Abstract While humanity prepares for a post-pandemic world and a return to normality through worldwide vaccination campaigns, each country experienced different levels of impact based on natural, political, regulatory, and socio-economic factors. To prepare for a possible future with COVID-19 and similar outbreaks, it is imperative to understand how each of these factors impacted spread and mortality. We train and tune two decision tree regression models to predict COVID-related cases and deaths using a multitude of features. Our findings suggest that, at the country-level, GDP per capita and comorbidity mortality rate are best predictors for both outcomes. Furthermore, latitude and smoking prevalence are also significantly related to COVID-related spread and mortality.