The Data Science Met with the COVID-19: Revealing the Most Critical Measures Taken for the COVID-19 Pandemic

Abstract

The whole world has been fighting against the novel coronavirus 2019 (COVID-19) for months. Despite the advances in medical sciences, more than 235,000 people have died so far. And, despite all the measures taken for it, more than 3 million people have become sick of the COVID-19. The measures taken for the COVID-19 vary through countries. So, revealing the most critical measures is necessary for a better fight against both the COVID-19 and possible similar pandemics in the future. To this end, an analysis of the worldwide measures, which were taken so far, for the COVID-19 pandemic was proposed within this paper. Since it is still early days, for the best of our knowledge, there does not exist a single dataset contains all the features utilized within this study. Therefore, a novel global dataset containing the data regarding the COVID-19 for 52 countries around the world was constructed by combining various datasets. Then, the feature importance techniques were employed to reveal the importance of the utilized features which means revealing the most important measures taken for the COVID-19 pandemic for our case. Within the analysis, four features were utilized, namely, the population density, the walking mobility, the driving mobility, and the number of lockdown days. According to the experimental result, the population density was found as the most important feature which means the most critical measure in terms of increasing the spread of the COVID-19 pandemic. The order of the importance of the other features was found as the walking mobility, the driving mobility, and the number of lockdown days, respectively.

Keywords

• covid-19
• coronavirus
• pandemic
• feature analysis
• feature importance