An ARIMA Model to Forecast the Spread and the Final Size of COVID-2019 Epidemic in Italy

  1. Gaetano Perone

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Abstract

Coronavirus disease (COVID-2019) is a severe ongoing novel pandemic that is spreading quickly across the world. Italy, that is widely considered one of the main epicenters of the pandemic, has registered the highest COVID-2019 death rates and death toll in the world, to the present day. In this article I estimate an autoregressive integrated moving average (ARIMA) model to forecast the epidemic trend over the period after April 4, 2020, by using the Italian epidemiological data at national and regional level. The data refer to the number of daily confirmed cases officially registered by the Italian Ministry of Health ( www.salute.gov.it ) for the period February 20 to April 4, 2020. The main advantage of this model is that it is easy to manage and fit. Moreover, it may give a first understanding of the basic trends, by suggesting the hypothetic epidemic’s inflection point and final size.

Highlights

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    ARIMA models allow in an easy way to investigate COVID-2019 trends, which are nowadays of huge economic and social impact.

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    These data may be used by the health authority to continuously monitor the epidemic and to better allocate the available resources.

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    The results suggest that the epidemic spread inflection point, in term of cumulative cases, will be reached at the end of May.

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    Further useful and more precise forecasting may be provided by updating these data or applying the model to other regions and countries.

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    1. ScreenIT

      SciScore for 10.1101/2020.04.27.20081539: (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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

      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 10.1101/2020.04.27.20081539v1 on medRxiv