Hrvoje Jošić, Berislav Žmuk

DOI Number
First page
Last page


The COVID-19 infection started in Wuhan, China, spreading all over the world, creating global healthcare and economic crisis. Countries all over the world are fighting hard against this pandemic; however, there are doubts on the reported number of cases. In this paper Newcomb-Benford Law is used for the detection of possible false number of reported COVID-19 cases. The analysis, when all countries have been observed together, showed that there is a doubt that countries potentially falsify their data of new COVID-19 cases of infection intentionally. When the analysis was lowered on the individual country level, it was shown that most countries do not diminish their numbers of new COVID-19 cases deliberately. It was found that distributions of COVID-19 data for 15% to 19% of countries for the first digit analysis and 30% to 39% of countries for the last digit analysis do not conform with the Newcomb-Benford Law distribution. Further investigation should be made in this field in order to validate the results of this research. The results obtained from this paper can be important for economic and health policy makers in order to guide COVID-19 surveillance and implement public health policy measures.


COVID-19, misreporting, Newcomb-Benford Law, Kolmogorov-Smirnov Z test, chi-square test

Full Text:



Alwine, J., Goodrum Sterling, F. (2020). Manipulation of pandemic numbers for politics risks lives. Available at: [Accessed: 2021-03-10].

Balashov, V., S., Yan, Y., Zhu, X. (2020). Who Manipulates Data During Pandemics? Evidence from Newcomb-Benford Law, doi: 10.2139/ssrn.3662462. Available at: [Accessed: 2021-03-10].

Benford, F. (1938). The law of anomalous numbers, Proceedings of the American Philosophical Society, 78(4), pp. 551-572.

Cambell, C., Gunia, A. (2020). China says it’s beating coronavirus. But can we believe its numbers? Available at: . [Accessed: 2021-03-10].

Cho, W. K. T., Gaines, B. J. (2007). Breaking the (Benford) Law: Statistical Fraud Detection in Campaign Finance, American Statistician, 61(3), pp. 218-223.

Diekmann, A. (2007). Not the First Digit! Using Benford’s Law to Detect Fraudulent Scientific Data, Journal of Applied Statistics, 34(3), pp. 321-329.

El Sehity, T. J., Hoelzl, E., Kirchler, E. (2005). Price Developments after a Nominal Shock: Benford’s Law and Psychological Pricing after the Euro Introduction, International Journal of Research in Marketing (IJRM), 4(22), pp. 471-480.

EU Open Data Portal (2020). COVID-19 Coronavirus data [online]. Available at: [Accessed: 2021-03-10].

Hindls, R., Hronová, S. (2015). Benford’s Law and Possibilities for Its Use in Governmental Statistics, Statistika, 95(2), pp. 54-64.

Jošić, H., Žmuk B. (2018). The application of Benford’s law in psychological pricing detection, Zbornik radova Ekonomskog fakulteta Sveučilišta u Mostaru, 24, pp. 37-57.

Kennedy, A. P., Yam, S. C. P., (2020). On the authenticity of COVID-19 case figures, PLoS ONE, 15(12), pp. 1-22.

Kilani, A., Georgiou, G. P. (2020). The Full Database of Countries with Potential COVID-19 Data Misreport based on Benford’s Law. Available at: [Accessed: 2021-03-10].

Koch, C., Okamura, K. (2020). Benford’s law and COVID-19 reporting, Economics Letters, 196 (2020), 109573.

Lee, K.-B., Han, S., Jeong, Y. (2020). COVID-19, flattening the curve, and Benford’s law, Physica A, pp. 1-12.

Michalski, T., Stoltz, G. (2013). Do countries falsify economic data strategically? Some evidence that they might, The Review of Economics and Statistics, 95(2), pp. 591-616.

Moreno-Montoya, J. (2020). Benford´s Law with small sample sizes: A new exact test useful in health sciences during epidemics, Salud UIS, 52(2), pp. 161-163. doi:

Newcomb, S. (1881). Note on the Frequency of Use of the Different Digits in Natural Numbers. American Journal of Mathematics, 4(1), pp. 39-40.

Nigrini, M. J. (1996). A taxpayer compliance application of Benford’s law. Journal of the American Taxation Association, 18(1), pp. 72-91.

Nigrini, M. J. (2012). Benford’s Law: Application for Forencis Accounting. Auditing, and Fraud detection, 2012, John Wiley and Sons.

Peng, Y., Nagata, M. H. (2020). Statistical analysis of the Chinese COVID-19 data with Benford’s law and clustering. Available at: [Accessed: 2021-03-10].

Shohini, R. (2020). Economic impact of COVID-19 pandemic. Technical report. Available at: [Accessed: 2021-03-10].

Silva, L., Figueiredo Filho, D, B. F. (2020). Using the Benford’s Law to Assess the Quality of COVID-19 Register Data in Brazil, Journal of Public Health, fdaa193,,doi:10.17605/OSF.IO/74XJC.

United Nations (2020). Impact of the COVID-19 Pandemic on Trade and Development, Transitioning to a new normal, United Nations Conference on Trade and Development. Available at: [Accessed: 2021-03-10].

Wagner, U., Jamsawang, J. (2012). Several Aspects of Psychological Pricing: Empirical Evidence from some Austrian Retailers. In: Rudolph T., Foscht T., Morschett D., Schnedlitz P., Schramm-Klein H., Swoboda B. (eds) European Retail Research. European Retail Research. Gabler Verlag, Wiesbaden.

Zhang, J. (2020). Testing Case Number of Coronavirus Disease 2019 in China with Newcomb-Benford Law, Physics and Society, pp. 1-7.

Žmuk, B., Jošić, H. (2020) Do countries diminish the number of new COVID-19 cases? A test using Benford’s law and uniform distribution, 3rd Conference titled "Economic System of the European Union and Accession of Bosnia and Herzegovina - Challenges and Policies Ahead", Mostar, Bosnia and Herzegovina, online, 24 October, 2020.



  • There are currently no refbacks.

© University of Niš, Serbia
Creative Commons License CC BY-NC-ND
ISSN 0354-4699 (Print)
ISSN 2406-050X (Online)