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

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 inform...

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Published in:	Science progress (1916) Vol. 104; no. 2; pp. 1 - 19
Main Author:	Farhadi, Noah
Format:	Journal Article
Language:	English
Published:	London, England Sage Publications, Ltd 01.04.2021 SAGE Publications Sage Publications Ltd
Subjects:	Americas - epidemiology Asia - epidemiology Bias Coronaviruses COVID-19 COVID-19 - epidemiology COVID-19 - transmission COVID-19 - virology Data Accuracy Digits Disease Notification - statistics & numerical data Europe - epidemiology Fatalities Frequency distribution Health Impact Assessment - ethics Health Impact Assessment - statistics & numerical data Humans Models, Statistical Pandemics Reliability analysis Research Design - standards Research Design - statistics & numerical data SARS-CoV-2 - pathogenicity SARS-CoV-2 - physiology Severe acute respiratory syndrome coronavirus 2 Viral diseases Americas Asia Europe Germany COVID-19 Benford’s law data analysis data manipulation public health
ISSN:	0036-8504, 2047-7163, 2047-7163
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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.
AbstractList	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. 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.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.
Author	Farhadi, Noah
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References	Goodman 2016; 13 Benford 1938; 78 Simard, L’Ecuyer 2011; 39 Alberti, Faranda 2020; 90 Cho, Gaines 2007; 61 Lee, Han, Jeong 2020; 559 Durtschi, Hillison, Pacini 2004; 5 Newcomb 1881; 4 Roukema 2014; 41 Grammatikos, Papanikolaou 2020; 59 bibr6-00368504211021232 bibr15-00368504211021232 Durtschi C (bibr13-00368504211021232) 2004; 5 bibr22-00368504211021232 bibr12-00368504211021232 bibr2-00368504211021232 bibr19-00368504211021232 LV (bibr11-00368504211021232) 1948 bibr14-00368504211021232 bibr21-00368504211021232 bibr4-00368504211021232 bibr7-00368504211021232 bibr10-00368504211021232 bibr17-00368504211021232 bibr1-00368504211021232 bibr20-00368504211021232 bibr8-00368504211021232 Benford F (bibr9-00368504211021232) 1938; 78 bibr5-00368504211021232 bibr16-00368504211021232 bibr18-00368504211021232 bibr3-00368504211021232 34236923 - Sci Prog. 2021 Jul-Sep;104(3):368504211030581
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SubjectTerms	Americas - epidemiology Asia - epidemiology Bias Coronaviruses COVID-19 COVID-19 - epidemiology COVID-19 - transmission COVID-19 - virology Data Accuracy Digits Disease Notification - statistics & numerical data Europe - epidemiology Fatalities Frequency distribution Health Impact Assessment - ethics Health Impact Assessment - statistics & numerical data Humans Models, Statistical Pandemics Reliability analysis Research Design - standards Research Design - statistics & numerical data SARS-CoV-2 - pathogenicity SARS-CoV-2 - physiology Severe acute respiratory syndrome coronavirus 2 Viral diseases
Title	Can we rely on COVID-19 data? An assessment of data from over 200 countries worldwide
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