Detection of anomaly in socio-economic databases, by Benford probability law

Said, Timjerdine; Kaicer, Mohammed

doi:10.1109/icoa49421.2020.9094466

Cited by 1 publication

(1 citation statement)

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“…According to Hill [30], "this law implies that a number has leading significant digit 1 with probability log 10 2 ≅ .301, leading significant digit 2 with probability log 10 3 ≅ .176 and so on monotonically down to probability .046 for leading digit 9". NBL has been used as a forensic tool to detect data irregularities in several fields, such as religious activity [31], scientific data [32], socio-economic datasets [33], electoral processes [34], international trade [35], and academic misconduct [36]. In epidemiological data, deviations from NBL may be associated with inadequate capacity in surveillance systems or intentional fraud [13].…”

Section: Discussionmentioning

confidence: 99%

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

2022

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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.

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