Forecasting Malaysia COVID-19 Incidence based on Movement Control Order using ARIMA and Expert Modeler
Coronavirus disease (COVID-19) is a novel pandemic that affects every other country in the world. Various countries have adopted control measures involving restriction of movement. Several studies have used mathematical modelling to predict the dynamic of this pandemic. Forecasting techniques can be...
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my.iium.irep.819202021-04-02T01:22:09Z http://irep.iium.edu.my/81920/ Forecasting Malaysia COVID-19 Incidence based on Movement Control Order using ARIMA and Expert Modeler Mohammad Aidid, Edre Zainal Abidin, Muhammad 'Adil Ab Rahman, Jamalludin RA643 Public Health. Hygiene. Preventive Medicine - Communicable Diseases and Public Health Coronavirus disease (COVID-19) is a novel pandemic that affects every other country in the world. Various countries have adopted control measures involving restriction of movement. Several studies have used mathematical modelling to predict the dynamic of this pandemic. Forecasting techniques can be used to predict the incidence cases for the short term. The study aims to forecast the COVID-19 incidence using the Auto Regressive Integrated Moving Average (ARIMA) method. MATERIALS AND METHODS: Using publicly available data, we performed a forecast of Malaysia COVID-19 new cases using Expert Modeler Method in SPSS and ARIMA model in R to predict COVID-19 cases in Malaysia. We compare 3 different time frames based on different Movement Control Order (MCO) period. We compare the model fit and prediction across models. RESULTS: All models show static cases for each MCO 7-day prediction. For prediction until 12 May, the third MCO time frame shows the best model fit for both techniques. Both software shows a stationary trend of cases of below 100. CONCLUSION: These MCO models have shown to stabilize the rate of new cases. Further sub analysis and quality of data is needed to improve the accuracy of the model. IIUM Press 2020-07-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/81920/2/Forecasting%20Malaysia%20COVID-19%20Incidence%20based%20on.pdf application/pdf en http://irep.iium.edu.my/81920/8/81920_Forecasting%20Malaysia%20COVID-19%20Incidence%20based%20on%20Movement%20Control%20Order_WOS.pdf Mohammad Aidid, Edre and Zainal Abidin, Muhammad 'Adil and Ab Rahman, Jamalludin (2020) Forecasting Malaysia COVID-19 Incidence based on Movement Control Order using ARIMA and Expert Modeler. IIUM Medical Journal Malaysia (IMJM), Vol. 19 (2). pp. 1-4. ISSN 1823-4631 https://journals.iium.edu.my/kom/index.php/imjm |
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RA643 Public Health. Hygiene. Preventive Medicine - Communicable Diseases and Public Health Mohammad Aidid, Edre Zainal Abidin, Muhammad 'Adil Ab Rahman, Jamalludin Forecasting Malaysia COVID-19 Incidence based on Movement Control Order using ARIMA and Expert Modeler |
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Coronavirus disease (COVID-19) is a novel pandemic that affects every other country in the world. Various countries have adopted control measures involving restriction of movement. Several studies have used mathematical modelling to predict the dynamic of this pandemic. Forecasting techniques can be used to predict the incidence cases for the short term. The study aims to forecast the COVID-19 incidence using the Auto Regressive Integrated Moving Average (ARIMA) method. MATERIALS AND METHODS: Using publicly available data, we performed a forecast of Malaysia COVID-19 new cases using Expert Modeler Method in SPSS and ARIMA model in R to predict COVID-19 cases in Malaysia. We compare 3 different time frames based on different Movement Control Order (MCO) period. We compare the model fit and prediction across models. RESULTS: All models show static cases for each MCO 7-day prediction. For prediction until 12 May, the third MCO time frame shows the best model fit for both techniques. Both software shows a stationary trend of cases of below 100. CONCLUSION: These MCO models have shown to stabilize the rate of new cases. Further sub analysis and quality of data is needed to improve the accuracy of the model. |
format |
Article |
author |
Mohammad Aidid, Edre Zainal Abidin, Muhammad 'Adil Ab Rahman, Jamalludin |
author_facet |
Mohammad Aidid, Edre Zainal Abidin, Muhammad 'Adil Ab Rahman, Jamalludin |
author_sort |
Mohammad Aidid, Edre |
title |
Forecasting Malaysia COVID-19 Incidence based on Movement Control Order using ARIMA and Expert Modeler |
title_short |
Forecasting Malaysia COVID-19 Incidence based on Movement Control Order using ARIMA and Expert Modeler |
title_full |
Forecasting Malaysia COVID-19 Incidence based on Movement Control Order using ARIMA and Expert Modeler |
title_fullStr |
Forecasting Malaysia COVID-19 Incidence based on Movement Control Order using ARIMA and Expert Modeler |
title_full_unstemmed |
Forecasting Malaysia COVID-19 Incidence based on Movement Control Order using ARIMA and Expert Modeler |
title_sort |
forecasting malaysia covid-19 incidence based on movement control order using arima and expert modeler |
publisher |
IIUM Press |
publishDate |
2020 |
url |
http://irep.iium.edu.my/81920/2/Forecasting%20Malaysia%20COVID-19%20Incidence%20based%20on.pdf http://irep.iium.edu.my/81920/8/81920_Forecasting%20Malaysia%20COVID-19%20Incidence%20based%20on%20Movement%20Control%20Order_WOS.pdf http://irep.iium.edu.my/81920/ https://journals.iium.edu.my/kom/index.php/imjm |
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13.18916 |