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Potential COVID-19 test fraud detection: Findings from a pilot study comparing conventional and statistical
Michael Bosnjak1,2, Stefan Dahm2, Ronny Kuhnert2
1Trier University, Department for Psychological Research Methods, Trier, Germany.
Statistical methods offer a more effective and systematic approach to detecting fraud in COVID-19 antigen/rapid test reporting compared to traditional methods. These techniques identified additional potentially fraudulent testing centers, improving overall detection rates.
Area of Science:
- Epidemiology
- Data Science
- Public Health
Background:
- Concerns exist regarding manipulated COVID-19 antigen/rapid test numbers reported by testing centers.
- This study evaluates statistical fraud detection methods against traditional approaches.
Purpose of the Study:
- To compare the effectiveness of statistical methods versus traditional fraud detection.
- To determine the extent to which statistical approaches can identify additional potential fraud cases.
Main Methods:
- Analysis of billing data from 907 German testing centers (April 2021-August 2022).
- Employed outlier detection, Poisson Regression for positivity rate modeling, and Benford's Law for digit analysis.
Main Results:
- Positive agreement (sensitivity) between methods ranged from 8.6% to 24.7%.
- Negative agreement (specificity) ranged from 91.3% to 94.6%.
- Statistical methods additionally identified 7.0%–8.7% of potentially fraudulent centers, with combined methods yielding optimal detection.
Conclusions:
- Statistical approaches are more effective and systematic for identifying fraudulent testing centers.
- Recommends mapping of paradata, such as testing timestamps, for future pandemic preparedness.
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