Coronavirus record fraud estimation by Benford's Law analytics

Abstract

The COVID-19 pandemic has generated vast amounts of data, including daily case and death counts by country. Analyzing the reliability of this data is crucial, and Benford's Law, a statistical principle that predicts the frequency of leading digits in naturally occurring datasets, can serve as a valuable tool. This study explores Benford's Law applications to these COVID-19 data, departing from previous work in two key ways. First, we leverage the most comprehensive dataset to date, spanning nearly three years of the pandemic, offering a broader and more robust picture. Second, we introduce a novel analysis technique – monotony checking – to assess Benford compliance by examining the decreasing frequency of leading digits. We employ a multi-pronged approach, encompassing chi-square tests, expected frequency calculations, mean absolute distance scores and exponential smoothing. Strikingly, these analyses converge in showcasing significant deviations from Benford's Law in numerous countries across diverse regions. Furthermore, our monotony analysis reinforces these findings, suggesting potential anomalies in data reporting. This research showcases the potential of Benford's Law for scrutinizing health-related data, much like its applications in financial and network domains. The observed discrepancies warrant further investigation to ensure data transparency and reliability in the ongoing fight against COVID-19.

Keywords

• Benford
• Coronavirus
• Statistically Significance

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