Subsequent waves of viral pandemics, a hint for the future course of the SARS-CoV-2 pandemic

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Background

It is unknown if the SARS-CoV-2 pandemic will have a second wave. We analysed published data of five influenza pandemics (such as the Spanish Flu and the Swine Flu) and the SARS-CoV-1 pandemic to describe whether there were subsequent waves and how they differed.

Methods

We reanalysed literature and WHO reports on SARS-CoV-1 and literature on five influenza pandemics. We report frequencies of second and third waves, wave heights, wavelengths and time between subsequent waves. From this, we estimated peak-to-peak ratios to compare the wave heights, and wave-length-to-wave-length ratios to compare the wavelengths differences in days. Furthermore, we analysed the seasonality of the wave peaks and the time between the peak values of two waves.

Results

Second waves, the Spanish Flu excluded, were usually about the same height and length as first waves and were observed in 93% of the 57 described epidemic events of influenza pandemics and in 42% of the 19 epidemic events of the SARS-CoV-1 pandemic. Third waves occurred in 54% of the 28 influenza and in 11% of the 19 SARS-CoV-1 epidemic events. Third waves, the Spanish Flu excluded, usually peaked higher than second waves with a peak-to-peak ratio of 0.5.

Conclusion

While influenza epidemics are usually accompanied by 2nd waves, this is only the case in the minority of SARS-Cov1 epidemics.

Article activity feed

  1. SciScore for 10.1101/2020.07.10.20150698: (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

    Software and Algorithms
    SentencesResources
    For COV1, we collected 11 events from the literature and eight from the WHO reports.(26-31) Relevant literature was identified by PubMed.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    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.