Modeling Occurrence of Dengue Cases in Malaysia

Dengue fever (DF) is the single most important vector-borne viral disease in tropical and subtropical areas worldwide. Globally, estimated 390 million dengue virus infection cases and 100 million incidences of DF occur each year (1). According to the WHO (2), southeast and western Pacific areas carry approximately 75% of global DF infections. This is a serious issue because the severity resulting from DF is considerably larger than that of other infectious diseases. In Malay-sia, DF infection rates have continually increased from 376 infections in 1985 to 49355 infections in 2008 (3). Over 131 fold growth in incidence this period. In fact, the prevalence of DF is increasing at an alarming pace and has caused significant health, economic, and social burdens on communities and their occupants. The increase in DF incidence from 2011 to 2012 was 10.14% (4). In 2014, there was a 17.118% increase in DF cases in comparison with 1981 cases and 3.640% compared with 1991 cases. Current dengue prevention approaches in Malaysia are largely inadequate or underdeveloped and provide little assistance in preventing the transmission and regulation of DF. Timely, forecasts for possible DF outbreaks would be useful for the prevention and control of DF. Time series methods process have been increasingly used in surveys to forecast communicable diseases, including DF (5). The objective of this study was to develop a model that can predict DF for the period from 2016 to 2025 in Malaysia based on a 34-yr time series data for the period from1981 to 2014. Such forecasting would be useful in improving outbreak preparation and growing public health interventions to address dengue incidence. The yearly data on DF cases in Malaysia from 1981 to 2014 were collected from the Malaysian Health Ministry's published reports other documents. An ARIMA model was fitted using a training dataset from 1981 to 1999, and the fitted model was used to predict values for a validation period from 2000 to 2014 to evaluate the fitted model. Overall, the data showed a tendency toward increasing DF incidence. The smallest incidence was 376 (1985), and the largest incidence was 108698 (2014). Over these 34 yr, 69669 cases were reported with a mean incidence of 20473 (median =12201) and a standard deviation of 22850 cases, reflecting a positive skew in the data. The time sequence plot of reported DF incidences exhibited an upward trend assumed non-stationary. We considered a first-lag difference span in …