Analysis of the prediction of the 2021 time-evolution of the Covid-19 Pandemic in Italy using a Planck’s distribution

  1. Ignazio Ciufolini
  2. Antonio Paolozzi

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Abstract

In a previous paper we studied the time evolution of the Covid-19 pandemic in Italy during the first wave of 2020 using a number of distribution laws. We concluded that the best distribution law to predict the evolution of the pandemic, if basic conditions of the pandemic (such as distancing measures, use of masks, start of schools, intensive use of public transportation, beginning and end of holidays, vaccination campaign and no significant onset of new Covid variants) do not appreciably change, is a distribution of the type of Planck’s law with three parameters. In our 2020 study we did not use the number of daily positive cases in Italy but the ratio of daily positive cases per number of daily tests, ratio today sometimes referred to as: “ positivity rate ”. We showed that, if basic conditions do not change, the Planck’s distribution with three parameters provides very good predictions of the positivity rate about one month in advance. In particular, in a second paper, using the Planck’s distribution with three parameters, we predicted, about one month in advance, the spread of the pandemic in Italy during the Christmas 2020 holidays with an error of a few percent only. We then study the present (September 2021) evolution of the pandemic in Italy and we show that the Planck’s distribution, based on the data of July and August, predicts well the evolution of the pandemic. In particular, we show that the peak of the positivity rate was predicted to occur approximately around the middle of August and that the agreement of this Planck’s function (obtained fitting the data up to 10 July 2021) and the positivity rate observed after 5 weeks, on 12 September 2021 is very good. However, the end of the Italian holidays and the start of all the activities including schools, intensive use of public transportation and further changes in distancing measures may cause a discrepancy of the predicted trend of the positivity rate of the pandemic with respect to the real observed values.

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  1. Version published to 10.1101/2021.09.06.21263173v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2021.09.06.21263173: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot 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: 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.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.09.06.21263173v1 on medRxiv