Evaluating data-driven methods for short-term forecasts of cumulative SARS-CoV2 cases

  1. Ghufran Ahmad
  2. Furqan Ahmed
  3. Muhammad Suhail Rizwan
  4. Javed Muhammad
  5. Syeda Hira Fatima
  6. Aamer Ikram
  7. Hajo Zeeb

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The WHO announced the epidemic of SARS-CoV2 as a public health emergency of international concern on 30th January 2020. To date, it has spread to more than 200 countries and has been declared a global pandemic. For appropriate preparedness, containment, and mitigation response, the stakeholders and policymakers require prior guidance on the propagation of SARS-CoV2.

Methodology

This study aims to provide such guidance by forecasting the cumulative COVID-19 cases up to 4 weeks ahead for 187 countries, using four data-driven methodologies; autoregressive integrated moving average ( ARIMA ), exponential smoothing model ( ETS ), and random walk forecasts ( RWF ) with and without drift. For these forecasts, we evaluate the accuracy and systematic errors using the Mean Absolute Percentage Error ( MAPE ) and Mean Absolute Error ( MAE ), respectively.

Findings

The results show that the ARIMA and ETS methods outperform the other two forecasting methods. Additionally, using these forecasts, we generate heat maps to provide a pictorial representation of the countries at risk of having an increase in the cases in the coming 4 weeks of February 2021.

Conclusion

Due to limited data availability during the ongoing pandemic, less data-hungry short-term forecasting models, like ARIMA and ETS , can help in anticipating the future outbreaks of SARS-CoV2.

Article activity feed

  1. Version published to 10.1371/journal.pone.0252147
  2. ScreenIT

    SciScore for 10.1101/2020.08.03.20167189: (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: 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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.08.03.20167189v1 on medRxiv