On short-term trends and predictions for COVID-19 in France and the USA: comparison with Australia

  1. Henry C. Tuckwell
  2. Mohsen Dorraki
  3. Stephen J. Salamon
  4. Andrew Allison
  5. Derek Abbott

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Abstract

In Europe and the USA daily new COVID-19 cases have recently been occurring in record numbers, which has created an alarming situation. The CDC in conjunction with several University groups gives forecasts for each county in the USA for several weeks at a time, but they have very large confidence intervals typified by the most recent national prediction of between 310,000 and 710,000 new cases for the week ending November 21, 2020. We have examined recent data for France and the USA over 10, 15 and 20 days. Using such data with simple fitting techniques, which do not require knowledge of any parameters, it has been possible to predict new case numbers fairly accurately for a week or more. A best-fitting polynomial of high order was only useful for a few days, after which it severely overestimated case numbers. A more detailed analysis with confidence intervals was performed for polynomials of orders one to six, which showed that lower order polynomials were more useful for prediction. Using the packages PCHIP and a POLYFIT (with degree one) in MATLAB gave smooth curves from which future case numbers could be reasonably well estimated. With PCHIP the average errors over 7 days were remarkably small, being −0.16% for France and +0.19% for the USA. A comparison is made between the temporal patterns of new cases for France, the USA and Australia. For Australia the second wave has dwindled to close to zero due to hard lock down conditions, which are discussed.

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  1. ScreenIT

    SciScore for 10.1101/2020.11.17.20233718: (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
    Data on the numbers of daily new cases for France, the USA and Australia were obtained for the period January 1 to November 3, 2020 from the downloadable daily postings at the European Centre for Disease Prevention and Control’s website2.
    Control’s
    suggested: None
    2.2 Using PCHIP: The MATLAB program called PCHIP (Piecewise Cubic Hermite Interpolating Polynomial) is primarily a tool for interpolation at points between data points but as suggested by the documentation, it can also be used to extrapolate as shown in a successful extrapolation of US census data3.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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

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  2. Version published to 10.1101/2020.11.17.20233718v1 on medRxiv