Using Exploratory Data Analysis for Generating Inferences on the Correlation of COVID-19 cases

Abstract

Exploratory Data Analysis (EDA) is a field of data analysis used to visually represent the knowledge embedded deep in the given data set. The technique is widely used to generate inferences from a given data set. Data set of current pandemic, the COVID-19 is widely made available by the standard dataset repository. EDA can be applied to these standard dataset to generate inferences. In this paper, data visualization technique is applied to the dataset and is used to formulate patterns for better insights on the effects of the pandemic with respect to the variables/ labels given in the dataset. A Web application tool called Jupyter Notebook is used to generate graphs using python language as it consists of libraries which are used for the process of EDA and the visualization is depicted for the attributes showing higher correlation. Based on the graphs obtained, we can draw conclusions from the current situation based on the data available, understand why a certain variable is increasing/decreasing with respect to another and what can be done to improve the drawbacks found.

Statistical Comparison of COVID-19 Infections Based Upon the Food Habits/Diets in Countries Using RStudio

“…The concept of Exploratory Data Analysis (EDA) has been applied by Joanita and Senthil Velan [5] on developing correlation among the COVID-19 cases. Based on the statistical analysis it was found that there exists higher correlation between the identified characteristics of the COVID-19 patients in the countries of study.…”

Similarity Search and Correlation-Based Exploratory Analysis in EHRs: A Case Study with COVID-19 Databases

“…Correlation Heuristics. Correlation coefficients provide the existing association between variables in a dataset [DSouza and Velan S. 2020]. Examples of well-known correlations employed in the literature are Pearson, Spearman, and ANOVA.…”

“…In the exploratory data analysis, correlation measures can uncover, expose and show relationships between attributes of unlabeled data [Hoshen and Wolf 2018]. Analyzing the correlation between variables and their underlying interactions is essential for multivariable datasets [Zhang et al 2016] and has been the subject of study for decades now [DSouza and Velan S. 2020, Yang et al 2019, Kaieski et al 2016. This work aims to take advantage of correlation measures and visual tools to support the exploratory analysis and data understanding.…”

A Survey on Big Data Research in Fighting COVID-19: Contributions and Techniques

“…Pandemic control is essential to prevent the spread of this outbreak from getting worse more widely. The literature suggests some official data sources issued by the government or agencies to be used to capture the evolutionary trajectory of COVID-19 [39], analyze infodemiology data for surveillance [40], formulate case patterns [41], and arranging appropriate quarantines activities [42]. Apart from official COVID-19 data, health insurance data can also be used to analyze the risk of being exposed to COVID-19.…”