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Benford's Law and COVID-19 reporting.
Christoffer Koch1, Ken Okamura2
1Research Department, Federal Reserve Bank of Dallas, 2200 North Pearl Street, Dallas, TX 75201, United States.
Economics Letters
|September 21, 2020
Summary
Chinese COVID-19 case data aligns with Benford's Law, similar to US and Italy, suggesting data reliability. Issues likely stem from poor international data sharing on testing and sampling, not fabricated case numbers.
Area of Science:
- Epidemiology
- Data Science
- Public Health Policy
Background:
- Real-time data on contagious diseases is crucial for effective policymaking.
- Recent skepticism has been cast upon Chinese reported COVID-19 case data by media and politicians.
- Assessing the reliability of reported infectious disease data is vital for global health security.
Purpose of the Study:
- To evaluate the validity of Chinese COVID-19 case data using statistical analysis.
- To compare the distribution of reported Chinese COVID-19 cases with international data.
- To identify potential sources of error or distrust in infectious disease reporting.
Main Methods:
- Application of Benford's Law to analyze the distribution of first digits in reported Chinese COVID-19 infection numbers.
- Comparative analysis of Chinese data distribution against data from the United States and Italy.
- Examination of multilateral data sharing practices concerning testing and sampling.
Main Results:
- The distribution of confirmed Chinese COVID-19 infections adheres to the expected patterns of Benford's Law.
- The data distribution for Chinese COVID-19 cases shows similarity to that observed in the U.S. and Italy.
- Deficiencies in multilateral data sharing related to testing and sampling were identified as a significant policy-making challenge.
Conclusions:
- Chinese reported COVID-19 case data appears statistically consistent and comparable to that of other nations.
- The primary concern regarding data trust may lie in systemic issues of international data collaboration rather than data fabrication.
- Improving global cooperation in sharing testing and sampling data is recommended to enhance policy-making confidence.
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