The Epidemic Severity Index: Estimating Relative Local Severity of Novel Disease Outbreaks

  1. Gareth Davies

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Abstract

Determining the severity of a novel pathogen in the early stages is difficult in the absence of reliable data. The pattern of outbreaks for COVID-19 across the globe have differed markedly above and below 30°N latitudes, suggesting very different levels of severity, but countries worldwide have implemented the same lockdown strategies. Existing methods for estimating severity appear not to have been useful in informing strategic decisions, possibly due to mismatches between the data required and those available, overly sophisticated methods with undesirable biases, or perhaps confusion and uncertainly generated by the wide range of estimates these methods produced early on.

The Epidemic Severity Index (ESI) is a simple, robust method for estimating the local severity of novel epidemic outbreaks using early and widely-available data and that does not depend on any estimated values. ESI allows rapid, meaningful comparisons across territories that can be tracked as the outbreaks unfold. The ESI quantifies severity relative to a parameterised baseline rather than attempting to estimate values for infection fatality rates, case fatality rates or transmission rates. The relative nature of the ESI sidesteps any problems of confidence associated with absolute rate estimation methods and offers immediate practical strategic value.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.23.20077685: (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

    Software and Algorithms
    SentencesResources
    Some files containing data for deaths and recoveries in CSV format were preprocessed in Excel before being imported into Matlab.
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Caveat Interpretor: The somewhat paradoxical goal of a high-level statistic is to distil a complex-hard-to-understand picture into a simple-easy-to-understand one whilst faithfully retaining salient information. As is true with any picture, reducing complexity (“resolution”) always comes at the direct cost of losing detail. Any picture reduced to a single point has necessarily lost most of the detailed information. It is therefore vitally important to check that the remaining datum usefully reflects that which has been discarded in the summarisation process. Whilst the ESI has been designed with stringent core design principles, the numerical values that it generates must still be interpreted with great care. As with all formulae, the quality the output depends on the quality of the inputs, and errors or differences in reported deaths and recoveries may account for differences in ESI.

    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 10.1101/2020.04.23.20077685v2 on medRxiv
  3. Version published to 10.1101/2020.04.23.20077685v1 on medRxiv