ISSN: 2222-6990
Open access
COVID-19 is rapidly expanding across the globe. As a Southeast Asian region, Malaysia has also been affected by COVID-19. Since the COVID-19 outbreak first emerged in China at the end of 2019, Malaysia has taken precautionary measures to prevent entering the nation. However, since COVID-19 is more than undoubtedly unstoppable, Malaysia eventually received the first case in early January 2020. The increase in the epidemic scale has led to the (preface of non-pharmaceutical countermeasures). Hence, it is of utmost importance to analyze the trends of the cases to develop a forecasting model that could anticipate the number of confirmed COVID-19 cases in Malaysia and select the best forecasting model based on forecast measure accuracy to forecast the future course of outcomes. For this purpose, the number of daily cases from 15 March 2020 to 31 March 2021 was retrieved from the Ministry of Health (MOH) website and estimated using the Box-Jenkins approach. There were five models developed such ARIMA (1,1,1), ARIMA (1,1,2), ARIMA (1,1,3), ARIMA (2,1,1) and ARIMA (2,1,2). The models’ effectiveness is evaluated based on AIC, BIC and RMSE criteria. The findings indicate that ARIMA (1,1,3) is the preferred model for forecasting since it has better performance regarding adopted criteria than compared models. The forecasted values showed an upward trend of COVID-19 cases until January 2022. In conclusion, subsequent studies would yield more discoveries and a more systematic approach to have better and more accurate forecasting. In the instance of the COVID-19, the recommended model appears to be correct. More complex modelling methodologies and extensive information on the disease are required to forecast the pandemic.
Alabdulrazzaq, H., Alenezi, M. N., Rawajfih, Y., Alghannam, B. A., Al-Hassan, A. A., & Al-Anzi, F. S. (2021). On the accuracy of Arima based prediction of COVID-19 spread. Results in Physics, 27, 104509. https://doi.org/10.1016/j.rinp.2021.104509
BERNAMA. (2020). Malaysia recognised as one of most successful in handling covid-19.
Bevans, R. (2020). Akaike information criterion: When amp; how to use it. Retrieved September 18, 2021, from https://www.scribbr.com/statistics/akaike-information-criterion/.
Chintalapudi, N., Battineni, G., & Amenta, F. (2020). Covid-19 virus outbreak forecasting of registered and recovered cases after sixty-day lockdown in Italy: A data driven model approach. Journal of Microbiology, Immunology and Infection, 54(3), 396-403. https://doi.org/10.1016/j.jmii.2020.04.004
Elengoe, A. (2020). Covid-19 outbreak in Malaysia. Osong Public Health and Research Perspectives, 11(3), 93.
Gupta, R., & Pal, S. K. (2020). Trend analysis and forecasting of covid-19 outbreak in India. MedRxiv. https://doi.org/10.1101/2020.03.26.20044511
Hyndman, R. J. (2018). Forecasting: Principles and practice, 2nd edition (G. Athanasopoulos, Ed.). OTexts. Retrieved 2020-12-29, from
https://otexts.com/fpp2/ stationarity.html#fn14.
Khan, F. M., & Gupta, R. (2020). Arima and Nar based prediction model for time series analysis of covid-19 cases in India. Journal of Safety Science and Resilience, 1(1), 12–18.
Khounpaseuth, S. (2020). Ministry of health and who respond to first case of covid-19 in Laos.
Konarasinghe, K. M. U. B. (2020a). Forecasting covid-19 spread in Malaysia, Thailand, and Singapore. doi: 10.5281/ZENODO.3935923
Konarasinghe, K. M. U. B. (2020b). Modeling covid-19 epidemic of USA, UK and Russia. Journal of New Frontiers in Healthcare and Biological Sciences, 1(1), 1–14.
Kufel, T. (2020). Arima-based forecasting of the dynamics of confirmed covid-19 cases for selected European countries. Equilibrium, 15, 181-204. doi: 10.24136/ eq.2020.009.
Lazim, M. A. (2011). Introductory business forecasting: A practical approach 3rd edition UiTM Press.
Palachy, S. (2019). Stationarity in time series analysis - towards data science. Towards Data Science. Retrieved 2020-12-29, from https://towardsdatascience.com/ stationarity-in-time-series-analysis-90c94f27322.
Sharma, V. K., & Nigam, U. (2020). Modeling and forecasting of covid-19 growth curve in India. Transactions of the Indian National Academy of Engineering, 5(4), 697–710.
Singh, S., Sundram, B. M., Rajendran, K., Law, K. B., Aris, T., Ibrahim, H., . . . Gill, B. S. (2020). Forecasting daily confirmed covid-19 cases in Malaysia using Arima models. The Journal of Infection in Developing Countries, 14(09), 971–976.
Tee, K. (2020). Dr Noor Hisham: Malaysia records zero local covid-19 transmissions for the second time, three imported cases.
WHO. (2020). Universal health coverage and covid-19 preparedness response in Malaysia.
Yonar, H., Yonar, A., Tekindal, M. A., & Tekindal, M. (2020). Modeling and forecasting for the number of cases of the covid-19 pandemic with the curve estimation models, the Box-Jenkins and exponential smoothing methods, EJMO, 4(2), 160-165.
In-Text Citation: (Januri et al., 2022)
To Cite this Article: Januri, S. S., Malek, I. A., Nasir, N., & Yasin, Z. A. M. M. (2022). Forecasting the Spread of Daily Confirmed Covid-19 Cases in Malaysia. International Journal of Academic Research in Business and Social Sciences, 12(2), 322–334.
Copyright: © 2022 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non0-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode