ArticlePloS one2020
On the authenticity of COVID-19 case figures.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Investigating and preventing scientific misconduct using Benford's Law.Research integrity and peer review · 2023Article
- "Won't get fooled again": statistical fault detection in COVID-19 Latin American data.Globalization and health · 2022Article
- Terminal digit preference and the accuracy of breast cancer diameter reporting based on Benford's law.Annals of medicine and surgery (2012) · 2022Article
- Combining rank-size and k-means for clustering countries over the COVID-19 new deaths per million.Chaos, solitons, and fractals · 2022Article
- Testing for Benford's Law in very small samples: Simulation study and a new test proposal.PloS one · 2022Article
- Linguistic laws in biology.Trends in ecology & evolution · 2022Review
- Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks.PloS one · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this article, we study the applicability of Benford's law and Zipf's law to national COVID-19 case figures with the aim of establishing guidelines upon which methods of fraud detection in epidemiology, based on formal statistical analysis, can be developed. Moreover, these approaches may also be used in evaluating the performance of public health surveillance systems. We provide theoretical arguments for why the empirical laws should hold in the early stages of an epidemic, along with preliminary empirical evidence in support of these claims. Based on data published by the World Health Organization and various national governments, we find empirical evidence that suggests that both Benford's law and Zipf's law largely hold across countries, and deviations can be readily explained. To the best of our knowledge, this paper is among the first to present a practical application of Zipf's law to fraud detection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.