Modelling monthly influenza cases in Malaysia

• Published in: PloS one 2021-07, Vol.16 (7), p.e0254137-e0254137
• Main Authors: Norrulashikin, Muhammad Adam, Yusof, Fadhilah, Hanafiah, Nur Hanani Mohd, Norrulashikin, Siti Mariam
• Format: Article
• Language: English
• Subjects: Autoregressive models; Biology and life sciences; Comparators; Developing countries; Diagnosis; Disease prevention; Engineering and Technology; Epidemics; Forecasting; Health care; Influenza; LDCs; Medical personnel; Medicine and Health Sciences; Methods; Pandemics; Parameter estimation; People and Places; Physical Sciences; Public health; Regression models; Research and Analysis Methods; Serology; Statistical analysis; Statistics; Support vector machines; Time series; Websites
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0254137

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.