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Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models
Sarbhan Singh1, Bala Murali Sundram2, Kamesh Rajendran3
1Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur, Malaysia. lssarbhan@imr.gov.my.
Journal of Infection in Developing Countries
|October 8, 2020
Summary
This study developed an Autoregressive Integrated Moving Average (ARIMA) model to predict daily COVID-19 cases in Malaysia. The model accurately forecasted a downward trend, demonstrating its utility for disease monitoring.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- The novel coronavirus (COVID-19) pandemic presents a significant global health challenge.
- Understanding disease transmission trends is crucial for effective mitigation strategies.
- Malaysia, like other nations, requires accurate forecasting of COVID-19 cases.
Purpose of the Study:
- To develop and evaluate a predictive model for daily confirmed COVID-19 cases in Malaysia.
- To identify the optimal subset of covariates for accurate case prediction.
- To assess the performance of Autoregressive Integrated Moving Average (ARIMA) models in forecasting COVID-19 trends.
Main Methods:
- Utilized daily confirmed COVID-19 case data from the Ministry of Health, Malaysia, and Johns Hopkins University.
- Applied an Autoregressive Integrated Moving Average (ARIMA) model, specifically ARIMA (0,1,0), for time series forecasting.
- Trained and validated the model using data from January 22 to April 17, 2020, with testing from April 18 to May 1, 2020.
Main Results:
- The ARIMA (0,1,0) model demonstrated the best fit, achieving a Mean Absolute Percentage Error (MAPE) of 16.01 and a Bayes Information Criteria (BIC) of 4.170.
- Forecasted cases indicated a downward trend in COVID-19 infections until May 1, 2020.
- The model accurately predicted observed cases within the generated prediction intervals.
Conclusions:
- ARIMA models, when utilizing optimally selected covariates, are effective tools for monitoring and predicting COVID-19 case trends in Malaysia.
- The study highlights the value of statistical modeling in public health surveillance during pandemics.
- Accurate forecasting aids in resource allocation and the implementation of timely public health interventions.
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