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.
Cite
Text
Pokhrel et al. "Evaluating Factors Influencing COVID-19 Outcomes Across Countries Using Decision Trees (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.27011Markdown
[Pokhrel et al. "Evaluating Factors Influencing COVID-19 Outcomes Across Countries Using Decision Trees (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/pokhrel2023aaai-evaluating/) doi:10.1609/AAAI.V37I13.27011BibTeX
@inproceedings{pokhrel2023aaai-evaluating,
title = {{Evaluating Factors Influencing COVID-19 Outcomes Across Countries Using Decision Trees (Student Abstract)}},
author = {Pokhrel, Aniruddha and Subedi, Nikesh and Aryal, Saurav Keshari},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {16302-16303},
doi = {10.1609/AAAI.V37I13.27011},
url = {https://mlanthology.org/aaai/2023/pokhrel2023aaai-evaluating/}
}