A multivariate data analysis approach for investigating daily statistics of countries affected with COVID-19 pandemic.

ABSTRACT:

Background

To understand the impact and volume of coronavirus (COVID-19) crisis, univariate analysis is tedious for describing the datasets reported daily. However, to capture the full picture and be able to compare situations and consequences for different countries, multivariate analytical models are suggested in order to visualize and compare the situation of different countries more accurately and precisely.

Aims

We aimed to utilize data analysis tools that display the relative positions of data points in fewer dimensions while keeping the variation of the original data set as much as possible, and cluster countries according to their scores on the formed dimensions.

Methods

Principal component analysis (PCA) and Partitioning around medoids (PAM) clustering algori

SUBMITTER: Ramadan A 

PROVIDER: S-EPMC7685045 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature