COVID-19: Tracking the Pandemic with A Simple Curve Approximation Tool (SCAT)

  1. Jane Courtney

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Abstract

In the current COVID-19 pandemic, much focus is put on ‘flattening the curve’. This epidemiological ‘curve’ refers to the cases versus time graph, which shows the rise of a disease to its peak before descending. The aim in a pandemic is to flatten this curve by reducing the peak and spreading out the timeline. However, the models used to predict this curve are often not clearly outlined, no model parameters are given, and models are not tested against real data. This lack of detail makes it difficult to recreate the curve. What is much needed is a simple tool for approximating the curve to allow ideas to be tested and comparisons made.

This work presents a Simple Curve Approximation Tool (SCAT) which can be used by anyone. This tool allows the user to approximate and draw the curve and allows testing of assumptions, trajectories and the wildly varying figures reported in the media. The mathematics behind SCAT is clearly outlined here but understanding of this is not required. SCAT is provided online as a downloadable MS Excel workbook with some sample cases shown. Throughout this work, the parameters used are specified so that all results can be easily reproduced.

Although not intended as a prediction tool, SCAT has achieved less than 0.5 % error in short-term forward prediction. It also shows a very significant improvement on the pandemic exponential approximations found throughout media reporting. As a comparison tool, it highlights obvious differences between COVID-19 and other diseases, such as influenza, and between countries at different stages of the pandemic (China, Italy and the UK are used here for demonstration purposes).

SCAT allows for quick approximation of the curve and creates meaningful comparisons and understandable visualisations for COVID-19 and other diseases.

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    SciScore for 10.1101/2020.04.06.20055467: (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: We detected the following sentences addressing limitations in the study:
    Limitations: SCAT is approximate and is not intended as a replacement for epidemiological models as diseases are rarely well behaved and are likely to stray from the curve. It is also only designed to approximate one ‘season’ of the virus, and not its entire lifetime. Any long-term prediction would be untrustworthy, and variations are expected as mitigation measures are put in place. To demonstrate this, SCAT was applied to the H1N1 epidemic in Hong Kong27 from 1 May 2009 to 15 November 2009. Although SCAT reaches a similar final tally (with less than 0.6 % error), it does not fit the data well in the rise phase as the rise phase is much slower for this disease (a key difference between COVID-19 and other similar pandemics). The curve does not capture re-infections or seasonal recurrence. Figure 17 shows data for seasonal flu in New York from 2009 to 2019 with SCAT only capturing the 2017-18 season. While China’s Case Fatality Rate (CFR) has settled to approximately 4 %, the fatality rates for both Italy and the UK are still rising, with Italy currently at 12 % and UK approaching 8 %, as seen in Figure 12. CFR is not a reliable figure due to inconsistent testing and omission of comorbidity considerations. However, it indicates that there is some way to go before final figures are reached. Functionality: SCAT has been shared with epidemiologists, medical editors and lay users and has been found to be very easy to use. Figure 18 shows a snapshot of SCAT in MS Excel. Date and da...

    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.

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  2. Version published to 10.1101/2020.04.06.20055467v1 on medRxiv