Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models

Sarbhan Singh; Bala Murali Sundram; Kamesh  Rajendran; Kian Boon Law; Tahir Aris; Hishamshah Ibrahim; Sarat Chandra Dass; Balvinder  Singh Gill

doi:10.3855/jidc.13116

Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models

Authors

Sarbhan Singh Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, Malaysia
Bala Murali Sundram Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, Malaysia
Kamesh Rajendran Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, Malaysia
Kian Boon Law Institute for Clinical Research (ICR), Ministry of Health, Shah Alam, Malaysia
Tahir Aris Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, Malaysia
Hishamshah Ibrahim Ministry of Health, Putrajaya, Malaysia
Sarat Chandra Dass Heriot-Watt University, Putrajaya, Malaysia
Balvinder Singh Gill Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.3855/jidc.13116

Keywords:

COVID-19, ARIMA, Forecast, Pandemic

Abstract

Introduction: The novel coronavirus infection has become a global threat affecting almost every country in the world. As a result, it has become important to understand the disease trends in order to mitigate its effects. The aim of this study is firstly to develop a prediction model for daily confirmed COVID-19 cases based on several covariates, and secondly, to select the best prediction model based on a subset of these covariates.

Methodology: This study was conducted using daily confirmed cases of COVID-19 collected from the official Ministry of Health, Malaysia (MOH) and John Hopkins University websites. An Autoregressive Integrated Moving Average (ARIMA) model was fitted to the training data of observed cases from 22 January to 31 March 2020, and subsequently validated using data on cases from 1 April to 17 April 2020. The ARIMA model satisfactorily forecasted the daily confirmed COVID-19 cases from 18 April 2020 to 1 May 2020 (the testing phase).

Results: The ARIMA (0,1,0) model produced the best fit to the observed data with a Mean Absolute Percentage Error (MAPE) value of 16.01 and a Bayes Information Criteria (BIC) value of 4.170. The forecasted values showed a downward trend of COVID-19 cases until 1 May 2020. Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model.

Conclusions: This study finds that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Malaysia.

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Published

2020-09-30

How to Cite

Singh S, Murali Sundram B, Rajendran K, Boon Law K, Aris T, Ibrahim H, Chandra Dass S, Singh Gill B (2020) Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models. J Infect Dev Ctries 14:971–976. doi: 10.3855/jidc.13116

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Issue

Vol. 14 No. 09: September 2020

Section

Coronavirus Pandemic

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