Evidence map›Paper›PMID 32993434›Full record

ArticleJournal of the Royal Society, Interface2020

Patterns of the COVID-19 pandemic spread around the world: exponential versus power laws.

Natalia L Komarova, Luis M Schang, Dominik Wodarz

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed.

  1. Article
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  7. A Statistical Definition of Epidemic Waves.Epidemiologia (Basel, Switzerland) · 2023
    Article
  8. Article
  9. Article
  10. Article
  11. The distribution of COVID-19 mortality.Infectious Disease Modelling · 2022
    Article
  12. Article
  13. Article
  14. Article
  15. On the global time evolution of the Covid-19 pandemic: Logistic modeling.Technological forecasting and social change · 2022
    Article
  16. Article
  17. 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 · 2022
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Natalia L KomarovaDepartment of Mathematics, University of California Irvine, Irvine, CA 92697, USA.
Luis M SchangDepartment of Microbiology and Immunology, Cornell University College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA.
Dominik WodarzDepartment of Population Health and Disease Prevention, Program in Public Health, Susan and Henry Samueli College of Health Science, University of California Irvine, Irvine, CA 92697, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Computer SimulationPublic Health InformaticsBetacoronavirusCommunicable Disease ControlCoronavirus InfectionsCOVID-19Data CollectionGeographyGlobal HealthHumansKineticsPandemicsPneumonia, ViralSARS-CoV-2growth lawsinfection dynamicsmathematical modelspower law

Identifiers

PMID32993434
PMCPMC7536045

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Registered trials

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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.