Can we rely on COVID-19 data? An assessment of data from over 200 countries worldwide

Abstract

To fight COVID-19, global access to reliable data is vital. Given the rapid acceleration of new cases and the common sense of global urgency, COVID-19 is subject to thorough measurement on a country-by-country basis. The world is witnessing an increasing demand for reliable data and impactful information on the novel disease. Can we trust the data on the COVID-19 spread worldwide? This study aims to assess the reliability of COVID-19 global data as disclosed by local authorities in 202 countries. It is commonly accepted that the frequency distribution of leading digits of COVID-19 data shall comply with Benford’s law. In this context, the author collected and statistically assessed 106,274 records of daily infections, deaths, and tests around the world. The analysis of worldwide data suggests good agreement between theory and reported incidents. Approximately 69% of countries worldwide show some deviations from Benford’s law. The author found that records of daily infections, deaths, and tests from 28% of countries adhered well to the anticipated frequency of first digits. By contrast, six countries disclosed pandemic data that do not comply with the first-digit law. With over 82 million citizens, Germany publishes the most reliable records on the COVID-19 spread. In contrast, the Islamic Republic of Iran provides by far the most non-compliant data. The author concludes that inconsistencies with Benford’s law might be a strong indicator of artificially fabricated data on the spread of SARS-CoV-2 by local authorities. Partially consistent with prior research, the United States, Germany, France, Australia, Japan, and China reveal data that satisfies Benford’s law. Unification of reporting procedures and policies globally could improve the quality of data and thus the fight against the deadly virus.

Pandemic Growth and Benfordness: Empirical Evidence from 176 Countries Worldwide

“…Table 4 summarizes the correlations between all variables. In previous studies, Tajikistan, Belarus, Bangladesh, Iran, and Turkey [9] were the major BL non-compliant countries [8,9]. These countries have striking statistical characteristics: our evaluation showed nonconformity to BL, low average growth rates, and low turnover in new cases for these countries.…”

“…The "Change in Benfordness" involves three reflective indicators, BL changes captured by Chi-square (CHI-Delta), K-S statistics (KS-Delta), and d* or d-factor (d-Delta). These stats were used in prior studies [2][3][4][5][6][7][8][9]. We regard these items as reflective indicators of the endogenous construct "Change in Benfordness" as they conceptually measure the same phenomenon.…”

“…Prior research already measured and provided countries' distance to BL by applying multiple statistical tests. The variables commonly used in previous studies were Kolmogorov-Smirnov statistic, Chi-square (χ 2 ) goodness-of-fit test, and Euclidean distance [2][3][4][5][6][7][8][9][10]. To be consistent with prior research, we operationalized the same variables to assess the distance to Benford's frequencies.…”

“…Jackson and Sambridge [7] evaluated Coronavirus data from 51 countries from 16 January 2020 to 9 April 2020. In one of the most comprehensive studies, Farhadi examined over 100.000 integers from 154 countries [8] with the primary outcome that approximately 28% of countries published reliable data on Coronavirus spread. In contrast, six countries disclosed entirely inconsistent records in the first nine months of the global pandemic.…”

“…Benfordness can be used on data with a geometrical tendency and is characterized by the absence of minima and maxima. BL is commonly used and has been widely applied in several disciplines such as finance and accounting [12,13], politics [14], and epidemiology [2][3][4][5][6][7][8][9].…”