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

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...

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Bibliographic Details
Published in:Journal of infection in developing countries 2020-09, Vol.14 (9), p.971-976
Main Authors: Singh, Sarbhan, Murali Sundram, Bala, Rajendran, Kamesh, Boon Law, Kian, Aris, Tahir, Ibrahim, Hishamshah, Chandra Dass, Sarat, Singh Gill, Balvinder
Format: Article
Language:English
Subjects:
Bayes Theorem
Betacoronavirus - isolation & purification
Clinical Laboratory Techniques
Coronavirus Infections - diagnosis
Coronavirus Infections - epidemiology
Coronaviruses
COVID-19
COVID-19 Testing
Forecasting
Humans
Malaysia - epidemiology
Models, Statistical
Pandemics
Pneumonia, Viral - diagnosis
Pneumonia, Viral - epidemiology
Public Health Surveillance
SARS-CoV-2
Trends
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Summary: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. 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). 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. This study finds that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Malaysia.
ISSN:1972-2680
2036-6590
1972-2680
DOI:10.3855/jidc.13116