ArticleJournal of the Royal Statistical Society. Series A, (Statistics in Society)2022
Power law in COVID-19 cases in China.
Article in Journal of the Royal Statistical Society. Series A, (Statistics in Society), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- COVID-19 transmission dynamics: age-specific patterns and super spreaders in South Korea.BMC public health · 2026Article
- Article
- Forecasting the Monkeypox Outbreak Using Limited Data: A Case Study of Thailand.Interdisciplinary perspectives on infectious diseases · 2025Article
- Time series analysis and short-term forecasting of monkeypox outbreak trends in the 10 major affected countries.BMC infectious diseases · 2024Article
- Heavy-tailed distributions of confirmed COVID-19 cases and deaths in spatiotemporal space.PloS one · 2023Article
- The distribution of COVID-19 mortality.Infectious Disease Modelling · 2022Article
- The impact of a power law-induced memory effect on the SARS-CoV-2 transmission.Chaos, solitons, and fractals · 2022Article
- Power law in COVID-19 cases in China.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022Article
- Risk perception and oil and gasoline markets under COVID-19.Journal of economics and businessArticle
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3 authors.
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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 epicentre 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 around one in the early phases of the outbreak (when the number of cases was growing rapidly) and less than one thereafter. This finding is significant because it implies that (i) COVID-19 cases in China is heavy tailed and disperse; (ii) a few cities account for a disproportionate share of COVID-19 cases; and (iii) the distribution generally has no finite mean or variance. We find that a proportionate random growth model predicated by Gibrat's law offers a plausible explanation for the emergence of a power law in the distribution of COVID-19 cases in Chinese cities in the early phases of the outbreak.
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