On the authenticity of COVID-19 case figures

PLoS One. 2020 Dec 8;15(12):e0243123. doi: 10.1371/journal.pone.0243123. eCollection 2020.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19 / epidemiology*
  • Humans
  • Models, Theoretical
  • Pandemics / statistics & numerical data*
  • Reproducibility of Results
  • SARS-CoV-2 / pathogenicity

Grants and funding

APK acknowledges the Hong Kong University Grant Council for their support of him pursuing his PhD at the Chinese University of Hong Kong, and that this work will constitute part of his dissertation. SCPY acknowledges financial support from HKGRF-14300717 with the project title “New kinds of Forward-backward Stochastic Systems with Applications”, HKGRF-14300319 with the project title “Shape-constrained Inference: Testing for Monotonicity”, and Direct Grant for Research 2014/15 (Project No. 4053141) offeredby CUHK. No additional funding was received for this study.