Forecasting COVID-19 cases in the Philippines using various mathematical models

  1. Monica C. Torres
  2. Christian Alvin H. Buhat
  3. Ben Paul B. Dela Cruz
  4. Edd Francis O. Felix
  5. Eleanor B. Gemida
  6. Jonathan B. Mamplata

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Due to the rapid increase of COVID-19 infection cases in many countries such as the Philippines, many efforts in forecasting the daily infections have been made in order to better manage the pandemic, and respond effectively. In this study, we consider the cumulative COVID-19 infection cases in the Philippines from March 6 to July 31,2020 and forecast the cases from August 1 - 15, 2020 using various mathematical models —weighted moving average, exponential smoothing, Susceptible-Exposed-Infected-Recovered (SEIR) model, Ornstein-Uhlenbeck process, Autoregressive Integrated Moving Average (ARIMA) model, and random forest. We then compare the results to the actual data using traditional error metrics. Our results show that the ARIMA(1,2,1) model has the closest forecast values to the actual data. Policymakers can use our result in determining which forecast method to use for their community in order to have a data-based information for the preparation of their personnel and facilities.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.10.07.20208421: (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.

    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.

  2. Version published to 10.1101/2020.10.07.20208421v1 on medRxiv