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Kennedy, AP and Yam, SCP (2020). On the authenticity of COVID-19 case figures. PLoS ONE 15(12): e0243123.

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Agyemang, EF, Mensah, JA and Nyarko, E (2023). How dependable is World Continental COVID-19 data? Disclosure of Inconsistencies in Daily Reportage Confirmed Cases, Recovered and Deaths During First Wave. Preprint – submitted to Heliyon. DOI:10.2139/ssrn.4516032. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Ahmad, S, Latif, DA, Mahmood, DM, Aslam, R, Abiddin, ZU, Mumtaz, H, Ahmed, K, Mehdi, W and begum, W (2022). Terminal digit preference and the accuracy of breast cancer diameter reporting based on Benford's law. Annals of Medicine and Surgery. DOI:10.1016/ j.amsu.2022.103993. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Awad, MM (2022). Evaluation of COVID-19 Reported Statistical Data Using Cooperative Convolutional Neural Network Model (CCNN). COVID 2(5), pp. 674–690. DOI:10.3390/covid2050051. View Complete Reference Online information 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, Nunes, FC and Caneppele, FdL (2023). The limits of conformity analysis under the Newcomb-Benford law and the COVID-19 pandemic in Brazil . Brazilian Journal of Biometrics 41, pp. 234-248 . DOI:10.28951/bjb.v41i3.626. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Cerasa, A (2022). Testing for Benford’s Law in very small samples: Simulation study and a new test proposal. PLoS ONE 17(7), pp. e0271969. DOI:10.1371/journal.pone.0271969. View Complete Reference Online information Works that this work references Works that reference this work
Eckhartt, GM and Ruxton, GD (2023). Investigating and preventing scientific misconduct using Benford’s Law. Research Integrity and Peer Review 8(1). DOI:10.1186/s41073-022-00126-w. View Complete Reference Online information Works that this work references Works that reference this work
Filho, DF, Silva, L and Medeiros, H (2022). “Won’t get fooled again”: statistical fault detection in COVID-19 Latin American data. Globalization and Health 18, pp.105. DOI:10.1186/s12992-022-00899-1. View Complete Reference Online information Works that this work references Works that reference this work
Filho, TMR, Mendes, JFF, Lucio, ML and Moret, MA (2022). Reliability of COVID-19 data and government policies. Preprint arXiv:2208.11226 [physics.soc-ph]; last accessed August 31, 2022. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Filho, TMR, Mendes, JFF, Lucio, ML and Moret, MA (2023). COVID-19 data, mitigation policies and Newcomb–Benford law. Chaos, Solitons and Fractals 174 p. 113814. DOI:10.1016/j.chaos.2023.113814. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Glen, S (2020). Fraudulent Covid-19 Data and Benford's Law. Blog posted on December 31; last accessed February 15, 2021. 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
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