Abstract
The novel coronavirus (COVID-19) was first identified in China in December 2019. Within a short period of time, the infectious disease has spread far and wide. This study focuses on the distribution of COVID-19 confirmed cases in China—the original epicenter of the outbreak. We show that the upper tail of COVID-19 cases in Chinese cities is well described by a power law distribution, with exponent less than one, and that a random proportionate growth model predicated by Gibrat’s law is a plausible explanation for the emergence of the observed power law behavior. This finding is significant because it implies that COVID-19 cases in China is heavy-tailed and disperse, that a few cities account for a disproportionate share of COVID-19 cases, and that the distribution has no finite mean or variance. The power-law distributedness has implications for effective planning and policy design as well as efficient use of government resources.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
No external funding is received to complete this project.
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
The research project does not require IRB review.
All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.
Yes
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Yes
Data Availability
The data used in this research project is publicly available. We provide online links to the data in the project.