What is it about?
This paper intends to ascertain the veracity of reported data on deaths and testing pertaining to the novel coronavirus in India. We use a widely used forensic audit technique called Benford's law to analyze the data. The reported data have also been under the scanner with anecdotal evidence suggesting massive under-reporting of deaths in particular. This issue has also attracted considerable negative press, including in premier outlets like Time Magazine and New York Times. To be fair such concerns were also raised for the reported data from China, but Koch and Okamura (2020) debunk that speculation and find no evidence that the Chinese authorities massaged the COVID-19 statistics.
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Why is it important?
The implications of the study are manifold, especially on the trajectory of policy-making, vaccination strategy, and preparedness for future waves and new variants.
Perspectives
Our findings suggest anomalies in the reported numbers and the reported data for most of the states do not adhere to the Benford distribution. Therefore, we strongly argue for the need for a robust data collection and reporting mechanism, creating a central data repository, and instituting a data-driven policy framework as key steps in the process management bulwark for managing such future pandemics and other events concerning public health.
Kiran Mahasuar
Indian Institute of Management Kozhikode
Read the Original
This page is a summary of: Lies, damned lies, and statistics: The uncertainty over
COVID
‐19 numbers in India, Knowledge and Process Management, August 2021, Wiley,
DOI: 10.1002/kpm.1685.
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