ArticleChaos (Woodbury, N.Y.)2020
Power-law distribution in the number of confirmed COVID-19 cases.
Article in Chaos (Woodbury, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- COVID-19 transmission dynamics: age-specific patterns and super spreaders in South Korea.BMC public health · 2026Article
- Beyond scale-free networks: integrating multilayer social networks with molecular clusters in the local spread of COVID-19.Scientific reports · 2023Article
- Collective Dynamics, Diversification and Optimal Portfolio Construction for Cryptocurrencies.Entropy (Basel, Switzerland) · 2023Article
- Power law in a bounded range: Estimating the lower and upper bounds from sample data.The Journal of chemical physics · 2023Article
- What is the optimal country for minimum COVID-19 morbidity and mortality rates?Environmental science and pollution research international · 2023Article
- Article
- Power Law for Estimating Underdetection of Foodborne Disease Outbreaks, United States.Emerging infectious diseases · 2023Article
- A coalitional game-based joint monitoring mechanism for combating COVID-19.Computer communications · 2023Article
- 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
- Temporal and spatial evolution of the distribution related to the number of COVID-19 pandemic.Physica A · 2022Article
- The CP-ABM approach for modelling COVID-19 infection dynamics and quantifying the effects of non-pharmaceutical interventions.Pattern recognition · 2022Article
- Power law in COVID-19 cases in China.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022Article
- Comparing the dynamics of COVID-19 infection and mortality in the United States, India, and Brazil.Physica D. Nonlinear phenomena · 2022Article
- The allometric propagation of COVID-19 is explained by human travel.Infectious Disease Modelling · 2022Article
- Functional observability and target state estimation in large-scale networks.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Do population density, socio-economic ranking and Gini Index of cities influence infection rates from coronavirus? Israel as a case study.The Annals of regional science · 2022Article
- Estimating a continuously varying offset between multivariate time series with application to COVID-19 in the United States.The European physical journal. Special topics · 2022Article
- An active learning-based approach for screening scholarly articles about the origins of SARS-CoV-2.PloS one · 2022Article
Corrections and comments
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1 author.
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No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is an emerging respiratory infectious disease caused by the coronavirus SARS-CoV-2. It was first reported on in early December 2019 in Wuhan, China and within three months spread as a pandemic around the whole globe. Here, we study macro-epidemiological patterns along the time course of the COVID-19 pandemic. We compute the distribution of confirmed COVID-19 cases and deaths for countries worldwide and for counties in the US and show that both distributions follow a truncated power-law over five orders of magnitude. We are able to explain the origin of this scaling behavior as a dual-scale process: the large-scale spread of the virus between countries and the small-scale accumulation of case numbers within each country. Assuming exponential growth on both scales, the critical exponent of the power-law is determined by the ratio of large-scale to small-scale growth rates. We confirm this theory in numerical simulations in a simple meta-population model, describing the epidemic spread in a network of interconnected countries. Our theory gives a mechanistic explanation why most COVID-19 cases occurred within a few epicenters, at least in the initial phase of the outbreak. By combining real world data, modeling, and numerical simulations, we make the case that the distribution of epidemic prevalence might follow universal rules.
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