Literature DB >> 35325235

Applying Benford's law to COVID-19 data: the case of the European Union.

Pavlos Kolias1.   

Abstract

BACKGROUND: Previous studies have used Benford's distribution to assess the accuracy of COVID-19 data. Data inaccuracies provide false information to the media, undermine global response and hinder the preventive measures taken by authorities.
METHODS: Daily new cases and deaths from all the countries of the European Union were analyzed and the conformance to Benford's distribution was estimated. Two statistical tests and two measures of deviation were calculated to determine whether the reported statistics comply with the expected distribution. Four country-level developmental indexes were included, the GDP per capita, health expenditures, the Universal Health Coverage (UHC) Index and the full vaccination rate. Regression analysis was implemented to examine whether the deviation from Benford's distribution is affected by the aforementioned indexes.
RESULTS: The findings indicate that Bulgaria, Croatia, Lithuania and Romania were in line with Benford's distribution. Regarding daily cases, Denmark, Ireland and Greece, showed the greatest deviation from Benford's distribution. Furthermore, it was found that the vaccination rate is positively associated with deviation from Benford's distribution.
CONCLUSIONS: The findings suggest that overall, official data provided by authorities are not confirming Benford's law, yet this approach acts as a preliminary tool for data verification. More extensive studies should be made with a more thorough investigation of countries that showed the greatest deviation.
© The Author(s) 2022. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  Benford’s law; COVID-19; EU; goodness-of-fit test

Mesh:

Year:  2022        PMID: 35325235      PMCID: PMC9234504          DOI: 10.1093/pubmed/fdac005

Source DB:  PubMed          Journal:  J Public Health (Oxf)        ISSN: 1741-3842            Impact factor:   5.058


  8 in total

1.  Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods.

Authors:  M Aickin; H Gensler
Journal:  Am J Public Health       Date:  1996-05       Impact factor: 9.308

2.  Using Benford's law to assess the quality of COVID-19 register data in Brazil.

Authors:  Lucas Silva; Dalson Figueiredo Filho
Journal:  J Public Health (Oxf)       Date:  2021-04-12       Impact factor: 2.341

3.  COVID-19 deaths in the USA: Benford's law and under-reporting.

Authors:  Michele Campolieti
Journal:  J Public Health (Oxf)       Date:  2022-06-27       Impact factor: 5.058

4.  Application of artificial neural networks to predict the COVID-19 outbreak.

Authors:  Hamid Reza Niazkar; Majid Niazkar
Journal:  Glob Health Res Policy       Date:  2020-11-23

5.  Using the Newcomb-Benford law to study the association between a country's COVID-19 reporting accuracy and its development.

Authors:  Vadim S Balashov; Yuxing Yan; Xiaodi Zhu
Journal:  Sci Rep       Date:  2021-11-25       Impact factor: 4.379

6.  Benford's Law and COVID-19 reporting.

Authors:  Christoffer Koch; Ken Okamura
Journal:  Econ Lett       Date:  2020-09-14

7.  Can we predict the occurrence of COVID-19 cases? Considerations using a simple model of growth.

Authors:  Fábio A M Cássaro; Luiz F Pires
Journal:  Sci Total Environ       Date:  2020-04-20       Impact factor: 7.963

  8 in total

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