Cross Reference Up
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.
This work is cited by the following items of the Benford Online Bibliography:
Note that this list may be incomplete, and is currently being updated. Please check again at a later date.
| Chi, D (2024). The Influence of Statistical Information on Number Estimation Under Uncertainty: Why Do People Present Benford Bias?. PhD Thesis, School of Psychology, Faculty of Science, University of Sydney. |  |
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| Dutta-Powell, R (2024). The perils of premature evaluation: reassessing the application of Benford’s Law to the USA’s COVID-19 data. Preprint on ResearchSquare.com. DOI:10.21203/rs.3.rs-5392071/v1. | |
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| 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. | |
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| 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. | |
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| 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. | |
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| Grodner, J and Rubin, AE (2025). Benford’s Law: applications to chondrules and refractory inclusions. Discover Space 129(2). DOI:10.1007/s11038-025-09561-3. | |
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