Forecasting COVID-19 new cases in Algeria using Autoregressive fractionally integrated moving average Models (ARFIMA)

  1. Belkacem Balah
  2. Messaoud Djeddou

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Abstract

In this research, an ARFIMA model is proposed to forecast new COVID-19 cases in Algeria two weeks ahead. In the present study, public health database from Algeria health ministry has been used to build an ARFIMA model and used to forecast COVID-19 new cases in Algeria until May 11, 2020.

Background

The aim of this study is first to find the best prediction method among the two techniques used and type of memory, either short or long, of the model constructed for the daily confirmed cases in Algeria, then make forecasts of the confirmed cases in the fifteen next days.

Methods

This study was conducted based on daily new cases of COVID-19 that were collected from the official website of Algerian Ministry of Health from March 1, 2020 to April 26, 2020. Auto Regressive Integrated Moving Average (ARFIMA) model was used to predict the trend of confirmed cases. The evaluation of the fractional differentiation parameter ( d ) is carried out using OxMetrics 6 software.

Results

The ARFIMA model (0, 0.431779, 0) build for Algeria, has a long memory and an upward trend over the next fifteen days and which coincides with the holy month of Ramadhan.

Conclusions

The forecasted results obtained by the proposed ARFIMA model can be used as a decision support tool to manage medical efforts and facilities against the COVID-19 pandemic crisis.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.03.20089615: (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: 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.

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  2. Version published to 10.1101/2020.05.03.20089615v1 on medRxiv