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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, 125090.

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Ali, A and Haque, S (2022). Application of Benford’s law to COVID-19 cases in selected countries of the Caribbean and globally. Caribbean Medical Journal. ISSN/ISBN:2664-5599. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Anab, F, Khaliq, A and Younas, I (2021). A Statistical Analysis of Covid-19 Data of Pakistan by Applying Benford’s Law. Journal of Applied Pharmacy 13, pp. 55-60. View Complete Reference No online information available Works that this work references No Bibliography works reference this work
Burgos, A and Santos, A (2021). The Newcomb–Benford law: Scale invariance and a simple Markov process based on it (Previous title: The Newcomb–Benford law: Do physicists use more frequently the key 1 than the key 9?). Preprint arXiv:2101.12068 [physics.pop-ph]; last accessed August 8, 2022; Published Am. J. Phys. 89, pp. 851-861. View Complete Reference Online information Works that this work references Works that reference this work
Carmo, CRS, Caneppele, FdL and Nunes, FC (2021). Analysis of Covid-19 Contamination and Deaths Cases in Brazil According to The Newcomb-Benford Law. Revista Brasileira de Biometria 39(4), pp.522-535. DOI:10.28951/rbb.v39i4.535. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Chatterjee, S, Sarkar, A, Karmakar, M, Chatterjee, S and Paul, R (2020). EIRD model to study the asymptomatic growth during COVID-19 pandemic in India. Indian Journal of Physics. DOI:10.1007/s12648-020-01928-8. View Complete Reference Online information Works that this work references No Bibliography works reference this work
D'Alessandro, A (2020). Benford's law and metabolomics: A tale of numbers and blood. Transfusion and Apheresis Science 59(6), pp. 103019. DOI:10.1016/j.transci.2020.103019. View Complete Reference Online information Works that this work references Works that reference this work
Dissanayake, CK and Daniel, J (2021). Do Pandemic Related Datasets with High Artificial Control Still Follow the Benford’s Law?. Proceedings of the 4th European International Conference on Industrial Engineering and Operations Management, Rome, Italy, August 2-5. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Erfani, A, Zhang, K and Cui, Q (2021). TAB Bid Irregularity: Data-Driven Model and Its Application. Journal of Management in Engineering 37(5), p. 04021055. DOI:10.1061/(ASCE)ME.1943-5479.0000958. View Complete Reference No online information available Works that this work references No Bibliography works reference this work
Farhadi, N (2021). Can we rely on COVID-19 data? An assessment of data from over 200 countries worldwide. Science Progress 104(2). DOI:10.1177/00368504211021232. View Complete Reference Online information Works that this work references Works that reference this work
Farhadi, N and Lahooti, H (2021). Are COVID-19 Data Reliable? A Quantitative Analysis of Pandemic Data from 182 Countries. COVID 1, pp. 137–152. DOI:10.3390/covid1010013. View Complete Reference Online information Works that this work references Works that reference this work
Farhadi, N and Lahooti, H (2021). Pandemic Growth and Benfordness: Empirical Evidence from 176 Countries Worldwide. COVID 1(1), pp. 366-383. DOI:10.3390/covid1010031. View Complete Reference Online information Works that this work references Works that reference this work
Farhadi, N and Lahooti, H (2022). Forensic Analysis of COVID-19 Data from 198 Countries Two Years after the Pandemic Outbreak. COVID 2(4), pp. 472-484. DOI:10.3390/covid2040034. View Complete Reference Online information Works that this work references Works that reference this work
Hanci, F (2022). Application of Benford’s law in agricultural production statistics. Journal of the National Science Foundation of Sri Lanka 50 (2), pp. 387-393. DOI:10.4038/jnsfsr.v50i2.10429. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Herteliu, C, Jianu, I, Dragan, IM, Apostu, S and Luchian, I (2021). Testing Benford’s Laws (non)conformity within disclosed companies’ financial statements among hospitality industry in Romania. Physica A: Statistical Mechanics and its Applications 582, p. 126221. DOI:10.1016/j.physa.2021.126221. View Complete Reference Online information Works that this work references Works that reference this work
Ileanu, B-V (2021). Time Lag Evidence of Anti-Abortion Decree and Perturbation of Births Distribution. A Benford Law Approach. Preprint arXiv:2106.15520 [physics.soc-ph]; last accessed July 30, 2021. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Jošić, H and Žmuk, B (2021). Assessing the Quality of COVID-19 Data: Evidence from Newcomb-Benford Law. Facta Universitatis, in press. DOI:10.22190/FUEO210326008J. View Complete Reference Online information Works that this work references Works that reference this work
Kilani, A (2021). Authoritarian regimes' propensity to manipulate Covid-19 data: a statistical analysis using Benford's Law. Commonwealth & Comparative Politics . DOI:10.1080/14662043.2021.1916207. View Complete Reference Online information Works that this work references Works that reference this work
Koesters, N, McMenemy, A and Bélanger, Y (2020). Simulating Epidemics with a SIRD Model and Testing with Benford’s Law. Preprint. View Complete Reference Online information Works that this work references Works that reference this work
Kolias, P (2022). Applying Benford’s law to COVID-19 data: the case of the European Union. Journal of Public Health, fdac005, pp. 1-6. DOI:10.1093/pubmed/fdac005. View Complete Reference Online information Works that this work references Works that reference this work
Malvar, S and Meneghini, JR (2022). Machine learning approaches for localized lockdown during COVID-19: a case study analysis. Preprint arXiv:2201.00715 [cs.LG]; last accessed January 9, 2022. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Morillas-Jurado, FG, Caballer-Tarazona, M and Caballer-Tarazona, V (2022). Applying Benford’s Law to Monitor Death Registration Data: A Management Tool for the COVID-19 Pandemic. Mathematics 10(1), 46. DOI:10.3390/math10010046. View Complete Reference Online information Works that this work references Works that reference this work
Pahuja, D (2021). Application of Benford’s Law to Detect if COVID-19 Data is under Reported or Manipulated. In: Rahul Srivastava & Aditya Kumar Singh Pundir (eds.), New Frontiers in Communication and Intelligent Systems, pp. 85–91. Computing & Intelligent Systems, SCRS, India . DOI:10.52458/978-81-95502-00-4-11. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Us, D (2021). Benford's Law: An Empirical Analysis of Reported Covid-19 Cases and Institutional Structures Around the Globe . Undergraduate Thesis, Università commerciale Luigi Bocconi, Milan. DOI:10.13140/RG.2.2.28839.88488. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Wei, A and Vellwock, AE (2020). Is COVID-19 data reliable? A statistical analysis with Benford's Law. Preprint, posted September. DOI:10.13140/RG.2.2.31321.75365/1. View Complete Reference Online information Works that this work references Works that reference this work
Wong, WK, Juwono, FH, Loh, WN and Ngu, IY (2020). Newcomb-Benford Law Analysis on COVID-19 Daily Infection Cases and Deaths in Indonesia and Malaysia. Research Square preprint. DOI:10.21203/rs.3.rs-131072/v1. View Complete Reference Online information Works that this work references Works that reference this work