Proceedings of 2023 IEEE International Conference on Big Data (BigData), pp. 4848-4853.
ISSN/ISBN: Not available at this time. DOI: 10.1109/BigData59044.2023.10386606
Abstract: We examine the principles of Benford’s Law on a data set of the daily number of COVID-19 cases and deaths per country. Benford’s Law was used before on the coronavirus-related data sets to find anomalies; however, our work differs from existing ones by first using the latest data set covering almost 3 years of coronavirus cases and second, proposing a new analysis, checking monotony, for Benford’s Law. First, the chi-square test was performed. The expected percentages for each case were calculated according to Benford’s Law and compared to the observed values. Second, the mean absolute distance score was also calculated for each country. These two analyses gave similar results: many countries in various regions of the world were well above the threshold value. Third, it was examined whether the frequencies of the first digit were monotonically decreasing. The analysis of all the results obtained reveals that Benford’s Law is applicable to health-related data analysis, as it was previously used in financial or network data sets.
Bibtex:
@INPROCEEDINGS {,
author = {Sena Atakan and Günce Keziban Orman},
booktitle = {2023 IEEE International Conference on Big Data (BigData)},
title = {On the usage of Benford’s Law on coronavirus data statistics},
year = {2023},
pages = {4848-4853},
doi = {10.1109/BigData59044.2023.10386606},
url = { https://www.computer.org/csdl/proceedings-article/bigdata/2023/10386606/1TUOVs9TBAs},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
Reference Type: Conference Paper
Subject Area(s): Medical Sciences, Statistics