Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models
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 conf...
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The Journal of Infection in Developing Countries
2020-09-01
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| Series: | Journal of Infection in Developing Countries |
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| Online Access: | https://jidc.org/index.php/journal/article/view/13116 |
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| author | Sarbhan Singh Bala Murali Sundram Kamesh Rajendran Kian Boon Law Tahir Aris Hishamshah Ibrahim Sarat Chandra Dass Balvinder Singh Gill |
| author_facet | Sarbhan Singh Bala Murali Sundram Kamesh Rajendran Kian Boon Law Tahir Aris Hishamshah Ibrahim Sarat Chandra Dass Balvinder Singh Gill |
| author_sort | Sarbhan Singh |
| collection | DOAJ |
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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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| format | Article |
| id | doaj-art-6af5a8d8a8114904a4e5d87f19413f21 |
| institution | Kabale University |
| issn | 1972-2680 |
| language | English |
| publishDate | 2020-09-01 |
| publisher | The Journal of Infection in Developing Countries |
| record_format | Article |
| series | Journal of Infection in Developing Countries |
| spelling | doaj-art-6af5a8d8a8114904a4e5d87f19413f212025-08-20T03:48:47ZengThe Journal of Infection in Developing CountriesJournal of Infection in Developing Countries1972-26802020-09-01140910.3855/jidc.13116Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA modelsSarbhan Singh0Bala Murali Sundram1Kamesh Rajendran2Kian Boon Law3Tahir Aris4Hishamshah Ibrahim5Sarat Chandra Dass6Balvinder Singh Gill7Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, MalaysiaInstitute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, MalaysiaInstitute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, MalaysiaInstitute for Clinical Research (ICR), Ministry of Health, Shah Alam, MalaysiaInstitute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, MalaysiaMinistry of Health, Putrajaya, MalaysiaHeriot-Watt University, Putrajaya, MalaysiaInstitute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, Malaysia 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. https://jidc.org/index.php/journal/article/view/13116COVID-19ARIMAForecastPandemic |
| spellingShingle | Sarbhan Singh Bala Murali Sundram Kamesh Rajendran Kian Boon Law Tahir Aris Hishamshah Ibrahim Sarat Chandra Dass Balvinder Singh Gill Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models Journal of Infection in Developing Countries COVID-19 ARIMA Forecast Pandemic |
| title | Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models |
| title_full | Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models |
| title_fullStr | Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models |
| title_full_unstemmed | Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models |
| title_short | Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models |
| title_sort | forecasting daily confirmed covid 19 cases in malaysia using arima models |
| topic | COVID-19 ARIMA Forecast Pandemic |
| url | https://jidc.org/index.php/journal/article/view/13116 |
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