Globalization and Health 18, pp.105.
ISSN/ISBN: Not available at this time. DOI: 10.1186/s12992-022-00899-1
Abstract: Background Claims of inconsistency in epidemiological data have emerged for both developed and developing countries during the COVID-19 pandemic. Methods In this paper, we apply first-digit Newcomb-Benford Law (NBL) and Kullback-Leibler Divergence (KLD) to evaluate COVID-19 records reliability in all 20 Latin American countries. We replicate country-level aggregate information from Our World in Data. Results We find that official reports do not follow NBL’s theoretical expectations (n = 978; chi-square = 78.95; KS = 4.33, MD = 2.18; mantissa = .54; MAD = .02; DF = 12.75). KLD estimates indicate high divergence among countries, including some outliers. Conclusions This paper provides evidence that recorded COVID-19 cases in Latin America do not conform overall to NBL, which is a useful tool for detecting data manipulation. Our study suggests that further investigations should be made into surveillance systems that exhibit higher deviation from the theoretical distribution and divergence from other similar countries.
Bibtex:
@article{,
author = {Dalson Figueiredo Filho and Lucas Silva and Hugo Medeiros},
title = {{“Won’t get fooled again”: statistical fault detection in COVID-19 Latin American data}},
journal = {Globalization and Health}.
volume = {18},
pages = {105},
doi = {10.1186/s12992-022-00899-1},
}
Reference Type: Journal Article
Subject Area(s): Medical Sciences