Preprint.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: In this paper, we use Monte Carlo simulations with a SIRD model parameterised from the literature and test with many metrics if Benford’s Law is fulfilled in 4 different scenarios. The results confirm that the Newcomb–Benford law could theoretically be an adequate tool to assess Covid-19 infected data reporting. The challenges in using Benford’s law in epidemics reporting are posed by the counting process in the real world where non malignant errors are introduced by lack of tests. One should as such see Benford’s law not as fraud detection tool, than as a assistive tool to measure reporting effectiveness in the real world.
Bibtex:
@misc{,
AUTHOR = {Nils Koesters and Andrena McMenemy and Yohan B{\'e}langer},
TITLE = {Simulating Epidemics with a SIRD Model and Testing with Benford’s Law}
YEAR = {2020},
URL = {https://www.researchgate.net/profile/Nils_Koesters/publication/344671690_SIMULATING_EPIDEMICS_WITH_A_SIRD_MODEL_AND_TESTING_WITH_BENFORD'S_LAW_A_PREPRINT/links/5f88558ea6fdccfd7b62bfd9/SIMULATING-EPIDEMICS-WITH-A-SIRD-MODEL-AND-TESTING-WITH-BENFORDS-LAW-A-PREPRINT.pdf},
}
Reference Type: Preprint
Subject Area(s): Medical Sciences