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Modelling monthly influenza cases in Malaysia
Muhammad Adam Norrulashikin1,2, Fadhilah Yusof1, Nur Hanani Mohd Hanafiah2
1Department of Mathematical Science, Universiti Teknologi Malaysia, Skudai, Malaysia.
Abstract:
The increasing trend in the number new cases of influenza every year as reported by WHO is concerning, especially in Malaysia. To date, there is no local research under healthcare sector that implements the time series forecasting methods to predict future disease outbreak in Malaysia, specifically influenza. Addressing the problem could increase awareness of the disease and could help healthcare workers to be more prepared in preventing the widespread of the disease. This paper intends to perform a hybrid ARIMA-SVR approach in forecasting monthly influenza cases in Malaysia. Autoregressive Integrated Moving Average (ARIMA) model (using Box-Jenkins method) and Support Vector Regression (SVR) model were used to capture the linear and nonlinear components in the monthly influenza cases, respectively. It was forecasted that the performance of the hybrid model would improve. The data from World Health Organization (WHO) websites consisting of weekly Influenza Serology A cases in Malaysia from the year 2006 until 2019 have been used for this study. The data were recategorized into monthly data. The findings of the study showed that the monthly influenza cases could be efficiently forecasted using three comparator models as all models outperformed the benchmark model (Naïve model). However, SVR with linear kernel produced the lowest values of RMSE and MAE for the test dataset suggesting the best performance out of the other comparators. This suggested that SVR has the potential to produce more consistent results in forecasting future values when compared with ARIMA and the ARIMA-SVR hybrid model.
Insights
Forecasting influenza cases in Malaysia is crucial for public health preparedness. This study found that Support Vector Regression (SVR) outperformed ARIMA and hybrid models in predicting monthly influenza outbreaks.
Area of Science:
- Epidemiology
- Public Health
- Data Science
Background:
- Influenza cases are increasing globally and in Malaysia.
- Lack of local research on time series forecasting for influenza outbreaks in Malaysia.
- Need for improved disease surveillance and preparedness in the healthcare sector.
Purpose of the Study:
- To forecast monthly influenza cases in Malaysia using time series methods.
- To evaluate the performance of hybrid ARIMA-SVR models.
- To identify the most effective forecasting model for influenza surveillance.
Main Methods:
- Utilized monthly influenza case data from WHO (2006-2019).
- Applied Autoregressive Integrated Moving Average (ARIMA) for linear components.
- Employed Support Vector Regression (SVR) for nonlinear components.
- Developed and compared a hybrid ARIMA-SVR model against individual models and a Naïve benchmark.
Main Results:
- All tested models (ARIMA, SVR, ARIMA-SVR) outperformed the Naïve benchmark.
- Support Vector Regression (SVR) with a linear kernel demonstrated the best performance.
- SVR achieved the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) on the test dataset.
Conclusions:
- Time series forecasting models can efficiently predict monthly influenza cases in Malaysia.
- Support Vector Regression (SVR) shows significant potential for accurate and consistent influenza forecasting.
- Findings can enhance disease awareness and healthcare preparedness for influenza outbreaks.
