DYNAMICS OF THE COVID-19 PANDEMICS: GLOBAL PATTERN AND BETWEEN COUNTRIES VARIATIONS

  1. J. Krywyk
  2. W. Oettgen
  3. M. Messier
  4. M. Mulot
  5. A. Ugon
  6. L. Toubiana

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The COVID-19 pandemic affected 203 countries between December 2019 and July 2020. The early epidemic “wave” affected countries which now report a few sporadic cases, achieving a stable late phase of the epidemic. Other countries are beginning their epidemic expansion phase. The objective of our study is to characterize the dynamics of the COVID-19 spread.

Data science methods were applied to pandemic, focusing on the daily fatality in 24 countries with more than 2,000 deaths, our analysis kin the end retaining 14 countries that have completed a full cycle.

The analysis demonstrates a COVID-19 dynamic similar in these studied countries. This 3-phase dynamic is like that of common viral respiratory infections. This pattern, however, shows variability and therefore specificity which the method categorizes into clusters of “differentiated epidemic patterns”. Among the 5 detected clusters, 2 main ones regroup 11 of these countries, representing 65% of the world deaths (as of June 24, 2020).

The pattern seems common to a very large number of countries, and congruent with that of epidemics of other respiratory syndromes, opens the hypothesis that the COVID-19 pandemic would have developed its “natural history” by spreading spontaneously despite the measures taken to contain it. The diversity highlighted by the classification into “formal clusters” suggests explanations involving the notion of demographic and geographic epicenters.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.07.20.20155390: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.07.20.20155390v1 on medRxiv