Sarbhan Singh1, Bala Murali Sundram2, Kamesh Rajendran3, Kian Boon Law4, Tahir Aris5, Hishamshah Ibrahim6, Sarat Chandra Dass7, Balvinder Singh Gill8. 1. Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, Malaysia. lssarbhan@imr.gov.my. 2. Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, Malaysia. bala.murali@moh.gov.my. 3. Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, Malaysia. kamesh@moh.gov.my. 4. Institute for Clinical Research (ICR), Ministry of Health, Shah Alam, Malaysia. kblaw@crc.gov.my. 5. Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, Malaysia. tahir.a@moh.gov.my. 6. Ministry of Health, Putrajaya, Malaysia. drhishamshah@moh.gov.my. 7. Heriot-Watt University, Putrajaya, Malaysia. s.dass@hw.ac.uk. 8. Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, Malaysia. drbsgill@moh.gov.my.
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. Copyright (c) 2020 Sarbhan Singh, Bala Murali Sundram, Kamesh Rajendran, Kian Boon Law, Tahir Aris, Hishamshah Ibrahim, Sarat Chandra Dass, Balvinder Singh Gill.
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. Copyright (c) 2020 Sarbhan Singh, Bala Murali Sundram, Kamesh Rajendran, Kian Boon Law, Tahir Aris, Hishamshah Ibrahim, Sarat Chandra Dass, Balvinder Singh Gill.
Authors: 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 Journal: Int J Environ Res Public Health Date: 2022-01-28 Impact factor: 3.390
Authors: W W Wu; Q Li; D C Tian; H Zhao; Y Xia; Y Xiong; K Su; W G Tang; X Chen; J Wang; L Qi Journal: Epidemiol Infect Date: 2022-04-21 Impact factor: 4.434