Coronavirus record fraud estimation by Benford's Law analytics

Year 2024, Volume: 1 Issue: 1, 17 - 23, 30.05.2024

Günce Keziban Orman Sena Atakan Elif Ece Erdem Şükrü Demir İnan Özer Timoteos Onur Özçelik

Abstract

The COVID-19 pandemic has generated vast amounts of data, including daily case and death counts by country. Analyzing the reliability of this data is crucial, and Benford's Law, a statistical principle that predicts the frequency of leading digits in naturally occurring datasets, can serve as a valuable tool. This study explores Benford's Law applications to these COVID-19 data, departing from previous work in two key ways. First, we leverage the most comprehensive dataset to date, spanning nearly three years of the pandemic, offering a broader and more robust picture. Second, we introduce a novel analysis technique – monotony checking – to assess Benford compliance by examining the decreasing frequency of leading digits. We employ a multi-pronged approach, encompassing chi-square tests, expected frequency calculations, mean absolute distance scores and exponential smoothing. Strikingly, these analyses converge in showcasing significant deviations from Benford's Law in numerous countries across diverse regions. Furthermore, our monotony analysis reinforces these findings, suggesting potential anomalies in data reporting. This research showcases the potential of Benford's Law for scrutinizing health-related data, much like its applications in financial and network domains. The observed discrepancies warrant further investigation to ensure data transparency and reliability in the ongoing fight against COVID-19.

Keywords

Benford, Coronavirus, Statistically Significance

References

• Alsunaidi, S. J., Almuhaideb, A. M., Ibrahim, N. M., Shaikh, F. S., Alqudaihi, K. S., Alhaidari, F. A., Khan, I. U., Aslam, N., & Alshahrani, M. S. (2021). Applications of Big Data Analytics to Control COVID-19 Pandemic. Sensors (Basel, Switzerland), 21(7), 2282. https://doi.org/10.3390/s21072282.
• Arshadi, L., & Jahangir, A. H. (2014). Benford's law behavior of Internet traffic. Journal of Network and Computer Applications, 40, 194-205.
• Balashov, V. S., Yan, Y., & Zhu, X. (2021). Using the Newcomb-Benford law to study the association between a country's COVID-19 reporting accuracy and its development. Scientific reports, 11(1), 22914. https://doi.org/10.1038/s41598-021-02367-z.
• Berger, A., & Hill, T. P. (2011). Benford’s law strikes back: No simple explanation in sight for mathematical gem. The Mathematical Intelligencer, 33(1), 85.
• Busta, B., & Weinberg, R. (1998). Using Benford’s law and neural networks as a review procedure. Managerial Auditing Journal, 13(6), 356-366.
• Caffarini, J., Gjini, K., Sevak, B., Waleffe, R., Kalkach-Aparicio, M., Boly, M., & Struck, A. F. (2022). Engineering nonlinear epileptic biomarkers using deep learning and Benford's law. Scientific reports, 12(1), 5397. https://doi.org/10.1038/s41598-022-09429-w.
• Campolieti, M. (2022). COVID-19 deaths in the USA: Benford’s law and under-reporting. Journal of Public Health, 44(2), e268-e271.
• Formann, A. K. (2010). The Newcomb-Benford law in its relationto some common distributions. PloS one, 5(5), e10541.
• Kilani, A. (2021). Authoritarian regimes' propensity to manipulateCovid-19 data: a statistical analysis using Benford's Law. Commonwealth & Comparative Politics, 59(3), 319-333.
• Kolias, P. (2022). Applying Benford’s law to COVID-19 data: the case of the European Union. Journal of Public Health, 44(2), e221-e226.
• Lee, K. B., Han, S., & Jeong, Y. (2020). COVID-19, flattening thecurve, and Benford's law. Physica A, 559, 125090. https://doi.org/10.1016/j.physa.2020.125090
• Li, F., Han, S., Zhang, H., Ding, J., Zhang, J., & Wu, J. (2019, February). Application of Benford’s law in Data Analysis. In Journal of Physics: Conference Series (Vol. 1168, No. 3, p. 03213.3). IOP Publishing.
• Michalski, T., & Stoltz, G. (2013). Do countries falsify economicdata strategically? Some evidence that they might. Review ofEconomics and Statistics, 95(2), 591-616.
• Rahimi, I., Chen, F., & Gandomi, A. H. (2023). A review on COVID-19 forecasting models. Neural Computing and Applications, 35(33), 23671-23681.
• Zhao, S., Gao, D., Zhuang, Z., Chong, M. K., Cai, Y., Ran, J., Cao,P., Wang, K., Lou, Y., Wang, W., Yang, L., He, D., Wang, MH. (2020). Estimating the serial interval of the novel coronavirus disease (COVID-19): a statistical analysis using the publicdata in Hong Kong from January 16 to February 15, 2020. Frontiers in Physics, 8, 347.