COVID-19’s daily new confirmed cases and deaths in Tunisia : A forensic analysis and machine learning algorithms for outlier detection

Montasser, Ghassen El

doi:10.47974/JIM-2112

Open Access Research Article

COVID-19’s daily new confirmed cases and deaths in Tunisia : A forensic analysis and machine learning algorithms for outlier detection

^*Ghassen El MontasserCorresponding authorg.elmontasser@gmail.comDepartment of Quantitative MethodsESC Business School of TunisCampus UniversitaireUniversity of ManoubaLa Manouba, 2010, Tunisia0000-0003-1513-9630View full profile →

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pp. 2751–2770Vol. 28Issue 8December 2025DOI: 10.47974/JIM-2112XML

Received:: 10 Jan 2024
Published Online:: 03 Jun 2025
Article type:: Research Article
Language:: EN
Article no.:: JIM-2112
Pages:: 2751–2770

Abstract

Effective management of health crises requires a good diagnosis of the current situation and an update of relevant data. Therefore, reliability of this data is fundamental to implement the necessary measures to manage health crises. In this paper, we study the compliance of COVID-19 cases and deaths reported in Tunisia with Benford’s law. Using a panoply of goodness-of-fit tests, we found that this compliance is disproved. This may raise doubts about the manipulation of this data. A search for outliers in these data followed this analysis using two machine learning algorithms, namely the local outlier factor (LOF) and isolation forests. The number of outliers in the data has heightened doubts about their reliability.

Keywords

COVID-19 Benford’s law Local outlier factor Isolation forests

Subject Classifications

62Q0568W99

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