Pandemic Growth and Benfordness: Empirical Evidence from 176 Countries Worldwide

Noah Farhadi,Hooshang Lahooti

doi:10.3390/covid1010031

Abstract

In the battle against the Coronavirus, over 190 territories and countries independently work on one end goal: to stop the pandemic growth. In this context, a tidal wave of data has emerged since the beginning of the COVID-19 crisis. Extant research shows that the pandemic data are partially reliable. Only a small group of nations publishes reliable records on COVID-19 incidents. We collected global data from 176 countries and explored the causal relationship between average growth ratios and progress in the reliability of pandemic data. Furthermore, we replicated and operationalized the results of prior studies regarding the conformity of COVID-19 data to Benford’s law. Our outcomes confirm that the average growth rates of new cases in the first nine months of the Coronavirus pandemic explain improvement or deterioration in Benfordness and thus reliability of COVID-19 data. We found significant evidence for the notion that nonconformity to BL rises by the growth of new cases in the initial phases of outbreaks.

Highlights

In January 2020, the World Health Organization (WHO) confirmed the first cases of Coronavirus, known as COVID-19 or severe acute respiratory syndrome (SARS)-CoV-2 in Wuhan City, China [1]
By simplifying the reflexive indicators and the endogenous construct change in Benfordness, we found a significant improvement in the out-of-sample measures and the predictive power of the partial least squares (PLS)-structural equation modeling (SEM)
Initial exponential growth in the first nine months of the global pandemic explains the overall progress in line with Benford’s law (BL) and the future development of the pandemic

Summary

Introduction

In January 2020, the World Health Organization (WHO) confirmed the first cases of Coronavirus, known as COVID-19 or SARS-CoV-2 in Wuhan City, China [1]. With millions of incidents and deaths to date, a tidal wave of data on COVID-19 has emerged. Since the outbreak of the virus, countries unanimously reported two metrics, “new cases” (individuals testing positive for the virus) and “new deaths” (the daily number of deaths) [2]. Having access to reliable data is vital. Policymakers use statistics to make life-saving decisions on restricting interventions colloquially known as lockdowns, travel bans, and social distancing. Scientists use pandemic data to detect the characteristics of the germ and respond

Methods

Results

Conclusion