Applying Benford’s law to COVID-19 data: The case of the European Union

Abstract

Previous studies have used Benford's distribution to assess whether there is misreporting of COVID-19 cases and deaths. Data inaccuracies provide false information to the media, undermine global response and hinder the preventive measures taken by countries worldwide. In this study, we analyze daily new cases and deaths from all the countries of the European Union and estimate the conformance to Benford's distribution. For each country, two statistical tests and two measures of deviations are calculated to determine whether the reported statistics comply with the expected distribution. Four country-level developmental indexes are also included, the GDP per capita, health expenditures, the Universal Health Coverage index, and full vaccination rate. Regression analysis is implemented to show whether the deviation from Benford's distribution is affected by the aforementioned indexes. The findings indicate that only three countries were in line with the expected distribution, Bulgaria, Croatia, and Romania. For daily cases, Denmark, Greece, and Ireland, showed the greatest deviation from Benford's distribution, and for deaths, Malta, Cyprus, Greece, Italy, and Luxemburg had the highest deviation from Benford's law. Furthermore, it was found that the vaccination rate is positively associated with deviation from Benford's distribution. These results suggest that overall official data provided by authorities are not confirming Benford's law, yet this approach acts as a preliminary tool for data verification. More extensive studies should be made with a more thorough investigation of countries that showed the greatest deviation.

“Won’t get fooled again”: statistical fault detection in COVID-19 Latin American data

“…NBL states that the first digit is not uniformly distributed in several naturally occurring collections of numbers. Therefore, many empirical studies use the deviation from NBL as a measure of data reliability [9,10,[12][13][14][15][16]. Figueiredo Filho et al Globalization and Health (2022) 18:105 We also employ Kullback-Leibler Divergence (KLD) to compare the asymmetry among COVID-19 data reports [14].…”