“Won’t get fooled again”: statistical fault detection in COVID-19 Latin American data

Globalization and Health

Research

Lucas Silva¹  Dalson Figueiredo Filho²  Hugo Medeiros² 

[1] Department of Medicine, Universidade Estadual de Ciências da Saúde do Estado de Alagoas, Rua Dr. Jorge de Lima, 113 - Trapiche da Barra, 57010-300, Maceió, Alagoas, Brazil;Department of Political Science, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil;

关键词: Public health surveillance;    Data reliability;    Newcomb-Benford law;    Kullback-Leibler divergence;    Latin America;   

DOI  :  10.1186/s12992-022-00899-1

 received in 2022-01-07, accepted in 2022-12-07,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundClaims of inconsistency in epidemiological data have emerged for both developed and developing countries during the COVID-19 pandemic.MethodsIn 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.ResultsWe 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.ConclusionsThis 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.

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

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