The Role of Societal Aspects in the Formation of Official COVID-19 Reports: A Data-Driven Analysis.

This paper investigates the role of socioeconomic considerations in the formation of official COVID-19 reports. To this end, we employ a dataset that contains 1159 pre-processed indicators from the World Bank Group GovData360 and TCdata360 platforms and an additional 8 COVID-19 variables generated based on reports from 138 countries. During the analysis, a rank-correlation-based complex method is used to identify the time- and space-varying relations between pandemic variables and the main topics of World Bank Group platforms. The results not only draw attention to the importance of factors such as air traffic, tourism, and corruption in report formation but also support further discipline-specific research by mapping and monitoring a wide range of such relationships. To this end, a source code written in R language is attached that allows for the customization of the analysis and provides up-to-date results.

A joint dataset of official COVID-19 reports and the governance, trade and competitiveness indicators of World Bank group platforms.

The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries (European Centre for Disease Prevention and Control, 2020 [1] and Beltekian et al. [2]), as well as an additional 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360(World Bank Group, 2020 [3]) and TCdata360(World Bank Group, 2020 [4]) platforms. From these platforms, only annual indicators from 2015 and later were collected, and their missing values were replaced with previous annual values, in descending order by year, until 2015. During preprocessing, indicators (columns) were filtered out when the ratio of missing values exceeded 50%. Then, the same filtration was applied for the ratio of missing values above 25% in the case of countries (rows). Finally, duplicated variables were removed from the dataset. As a result of these steps, the missing value rate of the employed indicators was reduced to 4.25% on average. In addition to the database, the Kendall rank correlation matrix is provided to facilitate subsequent analysis. The dataset and the correlation matrix can be updated and customized with an R Notebook file, which is also available publicly in Mendeley Data (Kurbucz, 2020 [5]).