COVID-19 DATA RELIABILITY RANKING OF COUNTRIES WITH GREY RELATIONAL ANALYSIS AND BENFORD’S LAW / Gri İlişkisel Analiz Ve Benford Yasası Yardımıyla Ülkelerin Covid-19 Veri Güvenirliği Sıralaması

Yıl 2022, , 156 - 171, 30.04.2022

Necati Alp Erilli

https://doi.org/10.29216/ueip.1086687

Cited By: 1

Öz

Covid-19 hastalığı, dünya çapında beklenmedik bir hızla yayılan ve pandemi olarak ilan edilen küresel bir hastalıktır. Mart 2022'nin başında dünya çapında 450 milyondan fazla vaka ve 6 milyondan fazla ölüm rapor edilmiştir. Benford yasası, tekrarlayan sayıları kullanan bir veri yapısında veri sahtekârlığı yapılıp yapılmadığını belirlemeye yarayan istatistiksel bir tekniktir. Bu çalışmada, etkin bir veri sahtekârlığı yöntemi olan Benford yasası yardımıyla dünya genelinde 5 milyondan fazla vakaya sahip 18 ülke gri ilişkisel analiz kullanılarak veri sahteciliğine göre sıralanmıştır. Gri ilişkisel analiz yöntemi ve Benford analizi sonuçları yardımıyla 2 yıllık veriler için 18 ülke ayrı ayrı listelenmiştir. Çalışmanın sonuçlarına göre bazı ülkelerin 2020 ile 2021 yılları arasında veri güvenirliğinde değişiklik gösterdiği belirlenmiştir. En güvenilir verilerin Almanya, Fransa ve Hollanda olduğu belirlenmiştir.

Anahtar Kelimeler

Benford Yasası, Gri İlişkisel Analiz, Veri Sahtekarlığı, Covid-19.

COVID-19 DATA RELIABILITY RANKING OF COUNTRIES WITH GREY RELATIONAL ANALYSIS AND BENFORD’S LAW / Gri İlişkisel Analiz Ve Benford Yasası Yardımıyla Ülkelerin Covid-19 Veri Güvenirliği Sıralaması

Öz

The covid-19 disease has become a pandemic that spreads at an unexpected pace around the world. There are more than 450 million cases and 6 million deaths worldwide at the start of March 2022. Benford's law is a statistical technique that serves to determine whether data fraud has been committed in a data structure that uses repetitive numbers. In this study, 18 countries with more than 5 million cases worldwide were ranked using grey relational analysis with the help of Benford's law, an effective method of data fraud. 18 countries are listed separately for 2 years of data with the help of the grey relational analysis method and Benford’s analysis results. According to the results of the study, it was determined that some countries showed changes in data reliability between 2020 and 2021. It has been determined that the data of Germany, France, and the Netherlands are the most reliable.

Anahtar Kelimeler

Benford’s Law, Grey Relational Analysis, Data Fraud, Covid-19.

Kaynakça

• Benford, F. (1938). The Law of Anomalous Numbers. Proceedings of the American Philosophical Society, 78, 551-572.
• Berton, L. (1955). He’s Got their Number. Scholar Uses Math to Foil Financial Fraud. Wall Street Journal, July10.
• Buck, B., Merchant, A. C. and Perez, S. M. (1993). An illustration of Benford’s First Digit Law Using Alpha Decay Half-Lives. European Journal of Physics, 14, 59–63.
• Carslaw, C. (1988). Anomalies in Income Numbers. Evidence of Goal Oriented Behavior. The Accounting Review, 63(2), 321-327.
• Cho, W.K.T. and Gaines, B.J. (2007). Breaking the (Benford’s) Law: Statistical Fraud Detection in Campaign Finance. American Statistician, 61(3), 218–223.
• Costas, E., López-Rodas, V., Javier Toro, F., and Flores-Moya, A. (2008). The Number of Cells in Colonies of the Cyanobacterium Microcystis Aeruginosa Satisfies Benford’s Law. Aquatic Botany, 89(3), 341–343.
• Deng, J. (1982). Control Problems of Grey Systems. Systems and Control Letters, 1(5), 288-294.
• Docampo, S. del Mar Trigo, M., Aira, M. J., Cabezudo, B. and Flores-Moya, A. (2009). Benford’s Law Applied to Aerobiological Data and Its Potential as A Quality Control Tool. Aerobiologia, 25(4), 275–283.
• Drake, P. D. and Nigrini, M. J. (2000). Computer Assisted Analytical Procedures Using Benford’s Law. Journal of Accounting Education, 18(2), 127-146.
• Feng, C. M. and Wang, R. T. (2000). Performance Evaluation for Airlines Including the Consideration of Financial Ratios. Journal of Air Transport Management, 6(3), 133-142.
• Gonzales-Garcia J. and Pastor, G. (2009). Benford’s Law and Macroeconomic Data Quality. International Monetary Fund Working Paper. Access address: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1356437
• Goodman, W. (2016). The Promises and Pitfalls of Benford's Law. Significance, 13(3), 38-41.
• Hill, T. P. (1995). Statistical Derivation of the Significant-Digit Law. Statistical Science, 10(4), 354-363.
• Holz, C. A. (2014). The Quality of China’s GDP Statistics. China Economic Review, 30, 309–338.
• Idrovo, A. J. and Manrique-Hernández, E. F. (2020). Data Quality of Chinese Surveillance of COVID-19: Objective Analysis Based on WHO's Situation Reports. Asia Pac J Public Health, 32(4), 165-7.
• Koch, C. and Okamura, K. (2020). Benford’s Law and COVID-19 Reporting. Economics Letters, 196, 1-4.
• Kose, E., Temiz, I. and Erol, S. (2011). Grey System Approach for Economic Order Quantity Models Under Uncertainty, The Journal of Grey System, 1(1), 71-82.
• Kuo, Y., Yang, T. and Huang, G. W. (2008). The Use of Grey Relational Analysis in Solving Multiple Attribute Decision-Making Problems. Computers & Industrial Engineering, 55(1), 80-93.
• Lee, K.B., Han, S. and Jeong, Y. (2020). Covid-19, Flattening The Curve, And Benford’s Law. Physica A: Statistical Mechanics and its Applications, 559, 1-12.
• Ley, E. (1996). On The Peculiar Distribution of The US Stock Indexes’ Digits. The American Statistician, 50(4), 311–313.
• Miller, S. J. (2015). Benford’s Law: Theory and Applications. UK:Princeton University Press.
• Newcomb, S. (1881). Note on the Frequency of Use of the Different Digits in Natural Numbers. American Journal of Mathematics, 4(1), 39-40.
• Nigrini, M. J. (1996). A Taxpayer Compliance Application of Benford’s Law. The Journal of the American Taxpayer Association, 18(1), 72-91.
• Nigrini, M.J. (2012). Benford's Law: Applications for Forensic Accounting, Auditing and Fraud Detection. New Jersey: John Wiley & Sons.
• Nye, J. and Moul, C. (2007). The Political Economy of Numbers: On The Application of Benford’s Law to International Macroeconomic Statistics. BE Journal of Macroeconomics, 7(1), 1-14.
• Rauch, B., Göttsche, M., Brähler, G. and Engel, S. (2011). Fact and fiction in EU Governmental Economic Data. German Economic Review, 12(3), 243–255.
• Raul, I. (2020). How Valid are the Reported Cases of People Infected with Covid-19 in the World? International Journal of Coronaviruses, 1(2), 53-56.
• Sambridge, M. and Jackson, A. (2020). National COVID Numbers - Benford's law Looks for Errors. Nature. Access address: https://media.nature.com/ original/magazine-assets/d41586-020-01565-5/d41586-020-01565-5.pdf
• Schäfer, C, Schräpler, J. P., Müller, K. R. and Wagner, G. G. (2004). Automatic Identification of Faked and Fraudulent Interviews in Surveys by Two Different Methods. DIW Discussion Papers, Access address: https://www.econstor.eu/bitstream/10419/18293/1/dp441.pdf
• Wei, A. and Vellwock, A.E. (2020). Is COVID-19 data reliable? A Statistical Analysis with Benford's Law. Access address: www.researchgate.net /publication/344164702_Is_COVID-19_data_reliable_A_statistical _analysis_with_Benford's_Law
• WHO (2022). World Health Organization. Access address: https://covid19.who.int/table
• Wu, H. H. (2002). A Comparative Study of Using Grey Relational Analysis in Multiple Attribute Decision Making Problems, Quality Engineering, 159(2), 209-217.
• Zhai, L.Y., Khoo, L.P. and Zhong, Z.W. (2009). Design Concept Evaluation in Product Development Using Rough Sets and Grey Relation Analysis. Expert System with Applications, 36(3), 7072-7079.