Reliability of COVID-19 data and government policies

TL;DR

This study assesses the reliability of COVID-19 data across countries using a modified Newcomb-Benford law, revealing that less democratic and more corrupt nations tend to have less reliable data.

Contribution

It introduces a modified application of the Newcomb-Benford law to evaluate COVID-19 data reliability and correlates deviations with social and economic indices.

Findings

Less democratic countries show greater deviation from the law.

Data quality correlates with transparency and corruption levels.

The method highlights potential data reliability issues.

Abstract

We study how available data on COVID-19 cases and deaths in different countries are reliable. Our analysis is based on a modification of the law of anomalous numbers, the Newcomb-Benford law, applied to the daily number of deaths and new cases in each country. We first revisit the Newcomb-Benford law and show how to avoid false negative compliance of the data. We then compared deviation from this law, to a number of social and economic indices for each country by computing the Spearman rank order correlation between each index and the \c{hi}2 deviation of COVID- 19 data to the modified NB law. A similar analysis for excess deaths for the same countries with sufficient available data was performed. We conclude that in general less democratic, less transparent and more corrupt countries tend to have data of lesser quality. We also discuss the limitations of the present approach.