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Statistical Analysis of Covid-19 Outbreak with Benford’s Law

Year 2023, , 120 - 133, 11.07.2023

Ali Hasan Doğan , Cemali Altuntaş , Caneren Gül , Nursu Tunalıoğlu , Bahattin Erdoğan

https://doi.org/10.11611/yead.1078847

Abstract

The coronavirus disease first identified in mid-December 2019 in Wuhan, China is an ongoing pandemic and the virus has spread around the world. As of 13 March 2020, the number of new cases started to increase significantly in Europe, and Europe was considered as the new center of the Covid-19 pandemic as announced by the WHO. Confirmed case rate (CCR), computed from the numbers of confirmed cases over numbers of tests of the countries can be used to confirm the quality of the numbers, and to detect the manipulation for health surveillance systems of the countries for managing the situation by testing whether or not follow Benford’s Law (BL). The main aim of this study is to test CCRs of the countries in Europe by BL to detect the data qualities and to monitor the manipulations, which can help to take precautions for the health surveillance systems of the countries.

Keywords

Outbreak Covid-19 Benford's Law Chi-Squared Test Kolmogorov-Smirnov Test

References

  • Ausloos M, Herteliu C, Ileanu B. Breakdown of Benford’s law for birth data. Physica A. 2015;419:736–745.
  • Balashov VS, Yan Y, Zhu X. Are less developed countries more likely to manipulate data during pandemics? Evidence from Newcomb-Benford law. arXiv:2007.14841. 2020
  • Becker PW. Patterns in listings of failure-rate & MTTF values and listings of other data. IEEE Trans Reliab. 1982;31:132–134
  • Benford F. The Law of Anomalous Numbers. Proceeding of the American Philosophical Society. 1938;78(4):551-573
  • Berger A, Hill TP, Rogers E. Benford online bibliography. http://www.benfordonl ine.net. 2009; Accessed March 25, 2020.
  • Gómez-Camponovo M, Moreno J, Idrovo AJ, Páez M, Achkar M. Monitoring the Paraguayan epidemiological dengue surveillance system (2009-2011) using Benford’s law. Biomédica. 2016; 36:583-92.
  • Hill TP. A statistical derivation of the significant-digit law. Stat Sci. 1995;10(4):354–363
  • Idrovo AJ, Fernández-Nino JA, Bojorquez-Chapela I, Moreno-Montoya J. Performance of public health surveillance systems during the influenza A(H1N1) pandemic in the Americas: testing a new method based on Benford’s Law. Epidemiol. Infect. 2011;139, 1827–1834.
  • Idrovo AJ, Manrique-Hernández EF. Data Quality of Chinese Surveillance of COVID-19: Objective Analysis Based on WHO’s Situation Reports. Asia Pacific Journal of Public Health. 2020;32(4):165–167.
  • Manrique-Hernández EF, Fernández-Nino JA, Idrovo AJ. Global performance of epidemiologic surveillance of Zika virus: rapid assessment of an ongoing epidemic. Public Health. 2017;143:14-16.
  • Marsaglia G, Tsang WW, Wang J. Evaluating Kolmogorov’s distribution. Journal of Statistical Software. 2003;8(18):1-4.
  • Massey Jr FJ. The Kolmogorov-Smirnov test for goodness of fit. Journal of the American statistical Association. 1951;46(253):68-78.
  • Mir TA. The law of the leading digits and the world religions. Physica A. 2012;391:792–798.
  • Mir TA. The Benford law behavior of the religious activity data. Physica A. 2014;408: 1–9.
  • Moosa IA. The effectiveness of social distancing in containing Covid-19. Applied Economics. 2020. DOI: 10.1080/00036846.2020.1789061
  • Nagasaka K. On Benford’s law. Ann Inst Stat Math. 1984;36(Part A):337–352
  • Newcomb S. Note on the frequency of use of the different digits in natural numbers. Am J Math. 1881;4(1):39–40
  • Roser M, Ritchie H, Ortiz-Ospina E, Hasell J. Coronavirus pandemic (COVID-19). Our World in Data. 2020.
  • Shao L, Ma BQ. First digit distribution of hadron full width. Mod. Phys. Lett. 2009;A24: 3275–3282.
  • Tunalioglu N, Erdogan B. Usability of the Benford’s law for the results of least square Estimation. Acta Geodaetica et Geophysica. 2019;54:315–331.
  • Walpole RE, Myers RH, Myers SL. Probability and Statistics for Engineers and Scientists. 1998; sixth edition, Prentice.
  • WHO. 2020. Situation reports. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/ Accessed April 30, 2020.
  • Zhang J. Testing case number of coronavirus disease 2019 in China with Newcomb-Benford law. arXiv:2002.05695. 2020.
  • [URL-1] https://covid19.who.int/ (Accessed: 31.10.2020)

Year 2023, , 120 - 133, 11.07.2023

Ali Hasan Doğan , Cemali Altuntaş , Caneren Gül , Nursu Tunalıoğlu , Bahattin Erdoğan

https://doi.org/10.11611/yead.1078847

Abstract

References

  • Ausloos M, Herteliu C, Ileanu B. Breakdown of Benford’s law for birth data. Physica A. 2015;419:736–745.
  • Balashov VS, Yan Y, Zhu X. Are less developed countries more likely to manipulate data during pandemics? Evidence from Newcomb-Benford law. arXiv:2007.14841. 2020
  • Becker PW. Patterns in listings of failure-rate & MTTF values and listings of other data. IEEE Trans Reliab. 1982;31:132–134
  • Benford F. The Law of Anomalous Numbers. Proceeding of the American Philosophical Society. 1938;78(4):551-573
  • Berger A, Hill TP, Rogers E. Benford online bibliography. http://www.benfordonl ine.net. 2009; Accessed March 25, 2020.
  • Gómez-Camponovo M, Moreno J, Idrovo AJ, Páez M, Achkar M. Monitoring the Paraguayan epidemiological dengue surveillance system (2009-2011) using Benford’s law. Biomédica. 2016; 36:583-92.
  • Hill TP. A statistical derivation of the significant-digit law. Stat Sci. 1995;10(4):354–363
  • Idrovo AJ, Fernández-Nino JA, Bojorquez-Chapela I, Moreno-Montoya J. Performance of public health surveillance systems during the influenza A(H1N1) pandemic in the Americas: testing a new method based on Benford’s Law. Epidemiol. Infect. 2011;139, 1827–1834.
  • Idrovo AJ, Manrique-Hernández EF. Data Quality of Chinese Surveillance of COVID-19: Objective Analysis Based on WHO’s Situation Reports. Asia Pacific Journal of Public Health. 2020;32(4):165–167.
  • Manrique-Hernández EF, Fernández-Nino JA, Idrovo AJ. Global performance of epidemiologic surveillance of Zika virus: rapid assessment of an ongoing epidemic. Public Health. 2017;143:14-16.
  • Marsaglia G, Tsang WW, Wang J. Evaluating Kolmogorov’s distribution. Journal of Statistical Software. 2003;8(18):1-4.
  • Massey Jr FJ. The Kolmogorov-Smirnov test for goodness of fit. Journal of the American statistical Association. 1951;46(253):68-78.
  • Mir TA. The law of the leading digits and the world religions. Physica A. 2012;391:792–798.
  • Mir TA. The Benford law behavior of the religious activity data. Physica A. 2014;408: 1–9.
  • Moosa IA. The effectiveness of social distancing in containing Covid-19. Applied Economics. 2020. DOI: 10.1080/00036846.2020.1789061
  • Nagasaka K. On Benford’s law. Ann Inst Stat Math. 1984;36(Part A):337–352
  • Newcomb S. Note on the frequency of use of the different digits in natural numbers. Am J Math. 1881;4(1):39–40
  • Roser M, Ritchie H, Ortiz-Ospina E, Hasell J. Coronavirus pandemic (COVID-19). Our World in Data. 2020.
  • Shao L, Ma BQ. First digit distribution of hadron full width. Mod. Phys. Lett. 2009;A24: 3275–3282.
  • Tunalioglu N, Erdogan B. Usability of the Benford’s law for the results of least square Estimation. Acta Geodaetica et Geophysica. 2019;54:315–331.
  • Walpole RE, Myers RH, Myers SL. Probability and Statistics for Engineers and Scientists. 1998; sixth edition, Prentice.
  • WHO. 2020. Situation reports. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/ Accessed April 30, 2020.
  • Zhang J. Testing case number of coronavirus disease 2019 in China with Newcomb-Benford law. arXiv:2002.05695. 2020.
  • [URL-1] https://covid19.who.int/ (Accessed: 31.10.2020)
There are 24 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Ali Hasan Doğan 0000-0002-8490-890X

Cemali Altuntaş 0000-0002-9660-6124

Caneren Gül 0000-0002-9491-7113

Nursu Tunalıoğlu 0000-0001-9345-5220

Bahattin Erdoğan 0000-0002-8060-9208

Early Pub Date July 12, 2023
Publication Date July 11, 2023
Published in Issue Year 2023

Cite

APA Doğan, A. H., Altuntaş, C., Gül, C., Tunalıoğlu, N., et al. (2023). Statistical Analysis of Covid-19 Outbreak with Benford’s Law. Yönetim Ve Ekonomi Araştırmaları Dergisi, 21(2), 120-133. https://doi.org/10.11611/yead.1078847
AMA Doğan AH, Altuntaş C, Gül C, Tunalıoğlu N, Erdoğan B. Statistical Analysis of Covid-19 Outbreak with Benford’s Law. Yönetim ve Ekonomi Araştırmaları Dergisi. July 2023;21(2):120-133. doi:10.11611/yead.1078847
Chicago Doğan, Ali Hasan, Cemali Altuntaş, Caneren Gül, Nursu Tunalıoğlu, and Bahattin Erdoğan. “Statistical Analysis of Covid-19 Outbreak With Benford’s Law”. Yönetim Ve Ekonomi Araştırmaları Dergisi 21, no. 2 (July 2023): 120-33. https://doi.org/10.11611/yead.1078847.
EndNote Doğan AH, Altuntaş C, Gül C, Tunalıoğlu N, Erdoğan B (July 1, 2023) Statistical Analysis of Covid-19 Outbreak with Benford’s Law. Yönetim ve Ekonomi Araştırmaları Dergisi 21 2 120–133.
IEEE A. H. Doğan, C. Altuntaş, C. Gül, N. Tunalıoğlu, and B. Erdoğan, “Statistical Analysis of Covid-19 Outbreak with Benford’s Law”, Yönetim ve Ekonomi Araştırmaları Dergisi, vol. 21, no. 2, pp. 120–133, 2023, doi: 10.11611/yead.1078847.
ISNAD Doğan, Ali Hasan et al. “Statistical Analysis of Covid-19 Outbreak With Benford’s Law”. Yönetim ve Ekonomi Araştırmaları Dergisi 21/2 (July 2023), 120-133. https://doi.org/10.11611/yead.1078847.
JAMA Doğan AH, Altuntaş C, Gül C, Tunalıoğlu N, Erdoğan B. Statistical Analysis of Covid-19 Outbreak with Benford’s Law. Yönetim ve Ekonomi Araştırmaları Dergisi. 2023;21:120–133.
MLA Doğan, Ali Hasan et al. “Statistical Analysis of Covid-19 Outbreak With Benford’s Law”. Yönetim Ve Ekonomi Araştırmaları Dergisi, vol. 21, no. 2, 2023, pp. 120-33, doi:10.11611/yead.1078847.
Vancouver Doğan AH, Altuntaş C, Gül C, Tunalıoğlu N, Erdoğan B. Statistical Analysis of Covid-19 Outbreak with Benford’s Law. Yönetim ve Ekonomi Araştırmaları Dergisi. 2023;21(2):120-33.