Correlating Covid-19 mortality and infection levels

  1. Mugdha Gadgil
  2. Chetan Gadgil

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Abstract

Covid-19 deaths and positive cases show a remarkable heterogeneity across countries which cannot be easily explained on the basis of similarities or differences in the quality of healthcare, access to healthcare, testing facilities, or preventive measures such as lockdowns. Here we show that there is a distinct correlation between the mortality level and the infection level across countries, which can explain the mortality levels for a wide spectrum of countries. This implies that the number of deaths per 100 infected individuals is approximately the same across diverse countries and can be estimated from the slope of the mortality level-infection level plot. The correlation presented here can potentially be combined with estimates of infection spread to forecast future mortality levels and therefore future needs in terms of healthcare and other resources. Tracking of an individual location’s temporal path on this plot can potentially serve as a visual assessment of the nature of the epidemic. Methods presented here are not specific to the current epidemic. This is a preliminary report and uses data from a single source at a single time-point to demonstrate the capability of such an analysis.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.01.20087320: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    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: We detected the following sentences addressing limitations in the study:
    This relationship and plot can be used in several ways: There are several caveats about the assumptions and therefore predictive ability of the model even if the data is extremely accurate. The accuracy of the reported numbers is unknown and is expected to vary across countries. We have assumed a linear model since it is the simplest possible fitting function. This choice however does imply any mechanistic basis. All estimates for mortality numbers critically depend on the validity of the estimate for infection level in addition to the uncertainty inherent in the correlation presented here. With the data available, it is not possible to discount the possibility of the correlation in Figure 1 or Figure 2 not applying to some countries/locations due to differences in population susceptibility arising from factors such as population demographics. Given these caveats, any estimates should be taken as numbers resulting from back-of-the-envelope calculations which is what this analysis amounts to. Nevertheless, given the apparent consistency across countries, we believe that such an analysis can contribute to the estimates for healthcare capacity planning, and such mortality-infection plots can be an aid to a visual analysis of epidemic progression.

    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

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  2. Version published to 10.1101/2020.05.01.20087320v1 on medRxiv