ArticleJournal of the Royal Society, Interface2020
Patterns of the COVID-19 pandemic spread around the world: exponential versus power laws.
Article in Journal of the Royal Society, Interface, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed.
- A modeling study on the critical sensitivity of epidemic peaks and final size to marginal changes in non-pharmaceutical intervention efficacy.BMC public health · 2026Article
- Remarks on a Scaling Theory of Spread of COVID-19 with an Application to the Case of Bulgaria.Entropy (Basel, Switzerland) · 2026Article
- Dynamic data visualizations as events: effects of framing and change salience on segmenting dynamic maps.Cognitive research: principles and implications · 2025Article
- Model predicted human mobility explains COVID-19 transmission in urban space without behavioral data.Scientific reports · 2025Article
- The great divide between employees: Clustering employee "well-being" during a pandemic.PloS one · 2025Article
- Bringing leaders of network subgroups closer together does not facilitate consensus.Scientific reports · 2024Article
- A Statistical Definition of Epidemic Waves.Epidemiologia (Basel, Switzerland) · 2023Article
- Power Law for Estimating Underdetection of Foodborne Disease Outbreaks, United States.Emerging infectious diseases · 2023Article
- Article
- The impact of a power law-induced memory effect on the SARS-CoV-2 transmission.Chaos, solitons, and fractals · 2022Article
- The distribution of COVID-19 mortality.Infectious Disease Modelling · 2022Article
- Very high-resolution remote sensing-based mapping of urban residential districts to help combat COVID-19.Cities (London, England) · 2022Article
- The allometric propagation of COVID-19 is explained by human travel.Infectious Disease Modelling · 2022Article
- Challenges for modelling interventions for future pandemics.Epidemics · 2022Article
- On the global time evolution of the Covid-19 pandemic: Logistic modeling.Technological forecasting and social change · 2022Article
- Understanding the uneven spread of COVID-19 in the context of the global interconnected economy.Scientific reports · 2022Article
- A Spreadsheet-Based Short Time Forecasting Method for the COVID-19 Pandemic.Transactions of the Indian National Academy of Engineering : an international journal of engineering and technology · 2022Article
- A Protective HLA Extended Haplotype Outweighs the Major COVID-19 Risk Factor Inherited From Neanderthals in the Sardinian Population.Frontiers in immunology · 2022Article
- Inferring the effect of interventions on COVID-19 transmission networks.Scientific reports · 2021Article
- Stochastic probical strategies in a delay virus infection model to combat COVID-19.Chaos, solitons, and fractals · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We have analysed the COVID-19 epidemic data of more than 174 countries (excluding China) in the period between 22 January and 28 March 2020. We found that some countries (such as the USA, the UK and Canada) follow an exponential epidemic growth, while others (like Italy and several other European countries) show a power law like growth. Regardless of the best fitting law, many countries can be shown to follow a common trajectory that is similar to Italy (the epicentre at the time of analysis), but with varying degrees of delay. We found that countries with 'younger' epidemics, i.e. countries where the epidemic started more recently, tend to exhibit more exponential like behaviour, while countries that were closer behind Italy tend to follow a power law growth. We hypothesize that there is a universal growth pattern of this infection that starts off as exponential and subsequently becomes more power law like. Although it cannot be excluded that this growth pattern is a consequence of social distancing measures, an alternative explanation is that it is an intrinsic epidemic growth law, dictated by a spatially distributed community structure, where the growth in individual highly mixed communities is exponential but the longer term, local geographical spread (in the absence of global mixing) results in a power law. This is supported by computer simulations of a metapopulation model that gives rise to predictions about the growth dynamics that are consistent with correlations found in the epidemiological data. Therefore, seeing a deviation from straight exponential growth may be a natural progression of the epidemic in each country. On the practical side, this indicates that (i) even in the absence of strict social distancing interventions, exponential growth is not an accurate predictor of longer term infection spread, and (ii) a deviation from exponential spread and a reduction of estimated doubling times do not necessarily indicate successful interventions, which are instead indicated by a transition to a reduced power or by a deviation from power law behaviour.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.