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Carmo, CRS, Nunes, FC and Caneppele, FdL (2023)

The limits of conformity analysis under the Newcomb-Benford law and the COVID-19 pandemic in Brazil

Brazilian Journal of Biometrics 41, pp. 234-248 .

ISSN/ISBN: Not available at this time. DOI: 10.28951/bjb.v41i3.626

Abstract: This research investigated the possible divergences in results due to the use of the Newcomb-Benford Law to assess the conformity of information regarding infections and deaths caused by the SARS-CoV-2 coronavirus, from transversal clippings made two years after the arrival of the COVID-19 pandemic in Brazil, through the comparative analysis of the results of statistics related to the Z test, the Chi-square test (X2), the Kolmogorov-Smirnov test (KS), and also the mean absolute deviation (MAD). For this, two distinct samples were analyzed: one referring to data accumulated over two years of the pandemic in Brazil, from 02/25/2020 to 02/25/2022; and another exclusively to data from the day the pandemic was completed two years of its arrival in Brazil, therefore exclusively on 02/25/2022. Thus, it was possible to observe that the Brazilian epidemiological information system, governed by the health policies adopted by the Government, did not function properly during the COVID-19 pandemic, and still, there was no improvement in quality, even after two years of the pandemic. Additionally, evidence was observed that the data series relating to the contaminations and deaths caused by the SARS-CoV-2 coronavirus present behavior distinct from the expected frequency distributions according to the Newcomb-Benford Law, either as a result of a natural process or due to particular issues of the Brazilian information system itself, for example, logistics, data distortions, forms of geographic aggregation, errors and/or negligence, among other factors.

@article{, title={The limits of conformity analysis under the Newcomb-Benford law and the COVID-19 pandemic in Brazil}, volume={41}, url={}, DOI={10.28951/bjb.v41i3.626}, number={3}, journal={Brazilian Journal of Biometrics}, author={Carmo, Carlos Roberto Souza and Nunes, Fábio Caixeta and Caneppele, Fernando de Lima}, year={2023}, month={Sep.}, pages={234–248} }

Reference Type: Journal Article

Subject Area(s): Medical Sciences