Evaluating Factors Influencing COVID-19 Outcomes across Countries Using Decision Trees (Student Abstract)
Aniruddha Pokhrel, Nikesh Subedi, Saurav Keshari Aryal
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.
BibTeX
@article{Pokhrel_Subedi_Aryal_2024, title={Evaluating Factors Influencing COVID-19 Outcomes across Countries Using Decision Trees (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/27011}, DOI={10.1609/aaai.v37i13.27011}, abstractNote={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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Pokhrel, Aniruddha and Subedi, Nikesh and Aryal, Saurav Keshari}, year={2024}, month={Jul.}, pages={16302-16303} }