Time series forecasting of COVID-19 confirmed cases with ARIMA model in the South East Asian countries of India and Thailand: a comparative case study

  1. Viswa Chaitanya Chandu

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background

As economic burden makes it increasingly difficult for countries to continue imposing control measures, it is vital for countries to make predictions using time series forecasting before making decisions on lifting the restrictions.

Aim

Since apparent differences were noted in the disease transmission between the two South East Asian countries of India and Thailand, the study aims to draw comparative account of the progression of COVID 19 in near future between these two countries.

Methods

The study used data of COVID 19 confirmed cases in India and Thailand from WHO COVID 19 situation reports during the time period between 25 th March, 2020 and 14 th May, 2020. After determination of stationarity in the data and differencing, observation of autocorrelation function (ACF) and partial autocorrelation function (PACF), Auto Regressive Integrated Moving Average (ARIMA) (2,2,1) model was used to forecast the COVID 19 confirmed cases in both these countries for two weeks (i.e. 28 th May, 2020). IBM SPSS version 20.0 software was used for data analysis.

Results

The study demonstrated a possible increasing trend in number of COVID 19 cases in India in the coming two weeks with an estimated point forecast of 1,28,772 (95% CI 115023–142520) by 28 th May, 2020. A stationary phase was forecasted for Thailand with a difference of only 43 cases between 14 th May (the last case of input data) and 28 th May.

Conclusion

The time series forecasting employed in the present study warrants thorough preparation on part of the Indian health care system and authorities and calls for caution with regard to decisions made on lifting the control measures. The difference in the time series forecasting between these two South East Asian countries also highlights the need for strengthening of public health systems.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.05.16.20103895: (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

    Software and Algorithms
    SentencesResources
    IBM SPSS Version 20.0 software (IBM SPSS statistics for windows version 20, Armonk, NY, USA) was used for data analysis.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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 10.1101/2020.05.16.20103895v1 on medRxiv