Development and application of Pandemic Projection Measures (PPM) for forecasting the COVID-19 outbreak

  1. Shu-Ren Chang
  2. Illinois State Board of Education

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Abstract

This study aims to provide an accessible and dynamic estimate method to project the Covid-19 trend and hopefully it will help inform policymakers to allocate the needed medical equipment and supplies for saving more lives. A set of newly developed Pandemic Projection Measures (PPM) had been successfully applied to project daily new cases across countries. During the development, numerous trial and error iterations had been performed and then improved with live data. The procedures and computations for the PPM including Uphill Index (UHI), Downhill Indices (DHI), and Error Band Projection (EBP) estimates were explained and discussed along with graphical projections. The PPM was computed with daily live data for the USA, four U.S. states (Illinois, Massachusetts, New Jersey, and New York), France, Italy, Spain, Germany, and China. The results indicated that with the PPM estimations, the daily projections for the future trend were robust to reflect the most plausibility, since the PPM can be updated frequently. With the most up-to-date predictions, governments should be able to monitor the values of UHI and DHI for making a better decision for “flattening the curve”. Based on the empirical data, policymakers should pay more attentions for the following two scenarios: a) When expecting an apex of the outbreak, the UHI is higher than 1.20; and b) After passing a peak day, the DHI is still larger than 0.925. The applications of the PPM estimates are not designed for a one-time projection rather than updated frequently to improve the prediction precisions. With the same concepts from the PPM computations, the peak day and the number of new deaths could be predictable if more data are collected. Like many country leaders saying, “We will win the battle of coronavirus pandemic”, the author hopes to use this easily applicable estimate method to save more lives and to win. The results, currently presented with the data on April 16 and 17, 2020, were only used to explain how to apply the PPM estimates for predictions. The outdated results should not be used to compared with today’s outbreak trend.

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    SciScore for 10.1101/2020.05.30.20118158: (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: Thank you for sharing your code and data.

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.05.30.20118158v1 on medRxiv