On the authenticity of COVID-19 case figures

• Published in: PloS one Vol. 15; no. 12; p. e0243123
• Main Authors: Kennedy, Adrian Patrick, Yam, Sheung Chi Phillip
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.12.2020; Public Library of Science (PLoS)
• Subjects: Analysis; Coronaviruses; COVID-19; COVID-19 - epidemiology; Dengue fever; Economic development; Empirical analysis; Epidemics; Epidemiology; False information (Law); Federal government; Fraud; Fraud investigation; Health surveillance; Hong Kong; Humans; Legislation; Medicine and Health Sciences; Methods; Models, Theoretical; Pandemics; Pandemics - statistics & numerical data; People and Places; Public health; Regions; Reproducibility of Results; SARS-CoV-2 - pathogenicity; Severe acute respiratory syndrome coronavirus 2; Social Sciences; Statistical analysis; Statistics; Surveillance systems; Hong Kong; Asia; Europe; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0243123

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.