Visual Exploratory Data Analysis of COVID-19 Pandemic: One Year After the Outbreak
Abstract
Background
Governments across the globe have taken different measures to handle the Covid-19 outbreak since it began in early 2020. Countries implemented various policies and restrictive measures to prevent transmission of the virus, reduce the impacts of the outbreak (i.e., individual, social, and economic), and provide effective control measures. Although it has been over one year since the outbreak started, few studies have examined the long-term effects of the pandemic. Furthermore, researchers need to examine which government intervention variables are the most, and least, effective. Such analysis is critical to determine the best practices in support of policy decisions.
Methods
Visual exploratory data analysis (V-EDA) offers a user-friendly data visualization model to evaluate the impact of the pandemic. It allows one to observe visual patterns of trends. The V-EDA was conducted on one year data for the COVID-19 Pandemic, one year after the outbreak between 1 January and 31 December, 2020. The data were analyzed using the student’s t-test to verify if there was a statistical difference between two independent groups, and the Spearman test was also used to analyze the correlation coefficient between two quantitative datasets and their positive or negative inclination.
Findings
We found that high-testing countries had more cases per million than low-testing countries. For low-testing countries, however, there was a positive correlation between the testing level and the number of cases per million. This suggests that high-testing countries tested in a preventive manner while low-testing countries may have a higher number of cases than those confirmed. The poorest developing countries have reduced testing which can coincide with the reduction in new cases, which we did not observe in the high-testing countries. Among the restrictive measures analyzed, a higher population aged 70 or older and lower GDP per capita was related to a higher case fatality ratio. Restrictive measures reduce the number of new cases after four weeks, indicating the minimum time required for the measures to have a positive effect. Finally, public event cancellation, international travel control, school closing, contact tracing, and facial coverings were the most important measures to reduce virus spread. We observed that countries with the lowest number of cases had a higher stringency index.
Related articles
Related articles are currently not available for this article.