IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i3p1504-d737025.html
   My bibliography  Save this article

Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia

Author

Listed:
  • Cia Vei Tan

    (Institute for Medical Research (IMR), Ministry of Health Malaysia, Shah Alam 40170, Malaysia)

  • Sarbhan Singh

    (Institute for Medical Research (IMR), Ministry of Health Malaysia, Shah Alam 40170, Malaysia)

  • Chee Herng Lai

    (Institute for Medical Research (IMR), Ministry of Health Malaysia, Shah Alam 40170, Malaysia)

  • Ahmed Syahmi Syafiq Md Zamri

    (Institute for Medical Research (IMR), Ministry of Health Malaysia, Shah Alam 40170, Malaysia)

  • Sarat Chandra Dass

    (School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, Putrajaya 62200, Malaysia)

  • Tahir Bin Aris

    (Institute for Medical Research (IMR), Ministry of Health Malaysia, Shah Alam 40170, Malaysia)

  • Hishamshah Mohd Ibrahim

    (Ministry of Health, Malaysia, Putrajaya 62590, Malaysia)

  • Balvinder Singh Gill

    (Institute for Medical Research (IMR), Ministry of Health Malaysia, Shah Alam 40170, Malaysia)

Abstract

With many countries experiencing a resurgence in COVID-19 cases, it is important to forecast disease trends to enable effective planning and implementation of control measures. This study aims to develop Seasonal Autoregressive Integrated Moving Average (SARIMA) models using 593 data points and smoothened case and covariate time-series data to generate a 28-day forecast of COVID-19 case trends during the third wave in Malaysia. SARIMA models were developed using COVID-19 case data sourced from the Ministry of Health Malaysia’s official website. Model training and validation was conducted from 22 January 2020 to 5 September 2021 using daily COVID-19 case data. The SARIMA model with the lowest root mean square error (RMSE), mean absolute percentage error (MAE) and Bayesian information criterion (BIC) was selected to generate forecasts from 6 September to 3 October 2021. The best SARIMA model with a RMSE = 73.374, MAE = 39.716 and BIC = 8.656 showed a downward trend of COVID-19 cases during the forecast period, wherein the observed daily cases were within the forecast range. The majority (89%) of the difference between the forecasted and observed values was well within a deviation range of 25%. Based on this work, we conclude that SARIMA models developed in this paper using 593 data points and smoothened data and sensitive covariates can generate accurate forecast of COVID-19 case trends.

Suggested Citation

  • Cia Vei Tan & Sarbhan Singh & Chee Herng Lai & Ahmed Syahmi Syafiq Md Zamri & Sarat Chandra Dass & Tahir Bin Aris & Hishamshah Mohd Ibrahim & Balvinder Singh Gill, 2022. "Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia," IJERPH, MDPI, vol. 19(3), pages 1-12, January.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1504-:d:737025
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/3/1504/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/3/1504/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cheung, Yin-Wong & Lai, Kon S, 1995. "Lag Order and Critical Values of the Augmented Dickey-Fuller Test," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 277-280, July.
    2. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    3. Li-Pang Chen & Qihuang Zhang & Grace Y Yi & Wenqing He, 2021. "Model-based forecasting for Canadian COVID-19 data," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-18, January.
    4. Gaetano Perone, 2020. "An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/07, HEDG, c/o Department of Economics, University of York.
    5. Anil Babu Payedimarri & Diego Concina & Luigi Portinale & Massimo Canonico & Deborah Seys & Kris Vanhaecht & Massimiliano Panella, 2021. "Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review," IJERPH, MDPI, vol. 18(9), pages 1-11, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    2. Francesco Busato & Bruno Chiarini & Gianluigi Cisco & Maria Ferrara & Elisabetta Marzano, 2020. "Lockdown Policies: A Macrodynamic Perspective for Covid-19," CESifo Working Paper Series 8465, CESifo.
    3. Mark J Holmes & Jesús Otero & Theodore Panagiotidis, 2018. "Climbing the property ladder: An analysis of market integration in London property prices," Urban Studies, Urban Studies Journal Limited, vol. 55(12), pages 2660-2681, September.
    4. Keen Meng Choy & Hwee Kwan Chow, 2004. "Forecasting the Global Electronics Cycle with Leading Indicators: A VAR Approach," Econometric Society 2004 Australasian Meetings 223, Econometric Society.
    5. Francis Ahking, 2003. "Efficient unit root tests of real exchange rates in the post-Bretton Woods era," Economics Bulletin, AccessEcon, vol. 6(7), pages 1-12.
    6. Helge Berger & Frank Westermann, 2001. "Factor price equalization? The cointegration approach revisited," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 137(3), pages 525-536, September.
    7. Pawel Milobedzki, 2010. "The Term Structure of the Polish Interbank Rates. A Note on the Symmetry of their Reversion to the Mean," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 10, pages 81-95.
    8. Gyula Dörgő & Viktor Sebestyén & János Abonyi, 2018. "Evaluating the Interconnectedness of the Sustainable Development Goals Based on the Causality Analysis of Sustainability Indicators," Sustainability, MDPI, vol. 10(10), pages 1-26, October.
    9. Rogelio Varela & Lázaro Cruz, 2016. "Inversión extranjera directa y tasa de interés en México: un análisis dinámico," Nóesis. Revista de Ciencias Sociales y Humanidades, Nóesis. Revista de Ciencias Sociales y Humanidades, vol. 25, pages 127-150, 50.
    10. Neil R. Ericsson & James G. MacKinnon, 2002. "Distributions of error correction tests for cointegration," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 285-318, June.
    11. Yin‐Wong Cheung & XingWang Qian, 2010. "Capital Flight: China's Experience," Review of Development Economics, Wiley Blackwell, vol. 14(2), pages 227-247, May.
    12. Cheung, Yin-Wong & Chinn, Menzie D. & Qian, XingWang, 2014. "The structural behavior of China–US trade flows," BOFIT Discussion Papers 23/2014, Bank of Finland Institute for Emerging Economies (BOFIT).
    13. Holmes, Mark J. & Otero, Jesús & Panagiotidis, Theodore, 2013. "On the dynamics of gasoline market integration in the United States: Evidence from a pair-wise approach," Energy Economics, Elsevier, vol. 36(C), pages 503-510.
    14. Cheung, Yin-Wong & Chinn, Menzie D. & Fujii, Eiji, 2003. "China, Hong Kong, and Taiwan: A quantitative assessment of real and financial integration," China Economic Review, Elsevier, vol. 14(3), pages 281-303.
    15. Yin-Wong Cheung & Frank Westermann, 2001. "Equity Price Dynamics Before and After the Introduction of the Euro: A Note," Multinational Finance Journal, Multinational Finance Journal, vol. 5(2), pages 113-128, June.
    16. Luisanna Onnis & Patrizio Tirelli, 2015. "Shadow economy: Does it matter for money velocity?," Empirical Economics, Springer, vol. 49(3), pages 839-858, November.
    17. Jean-Philippe Gervais, 2011. "Disentangling nonlinearities in the long- and short-run price relationships: an application to the US hog/pork supply chain," Applied Economics, Taylor & Francis Journals, vol. 43(12), pages 1497-1510.
    18. Lai, Kon S., 2004. "On structural shifts and stationarity of the ex ante real interest rate," International Review of Economics & Finance, Elsevier, vol. 13(2), pages 217-228.
    19. Haluk Erlat, 2004. "Unit roots or nonlinear stationarity in Turkish real exchange rates," Applied Economics Letters, Taylor & Francis Journals, vol. 11(10), pages 645-650.
    20. Naoufel Mahfoudh & Imen Gmach, 2021. "The Effects of Fiscal Effort in Tunisia: An Evidence from the ARDL Bound Testing Approach," Economies, MDPI, vol. 9(4), pages 1-20, December.

    More about this item

    Keywords

    COVID-19; forecast; ARIMA; Malaysia;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1504-:d:737025. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.