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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
IIUM Press
2020
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| Subjects: | |
| Online Access: | 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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| Summary: | 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. |
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