Exploring the spatial and temporal distribution of dengue fever in Kuantan of east coast Peninsular Malaysia / Zulkifli Abdul Hadi

Dengue fever is rapidly becoming Malaysia's most vital health concern, with cases nearly doubling in the previous decade. Given the uncertainties surrounding the recently announced tetravalent vaccination and the lack of efficient antiviral medications, vector management remains the most essential technique in the fight against dengue fever. A retrospective study was carried out using epidemiological data in Kuantan, Pahang. The confirmed dengue cases from the year 2011 to 2020 was retrieved and analysed using spatial temporal and time series analysis. The time series model could potentially provide useful information that could be further used to facilitate the planning of public health interventions in an effort to minimize dengue outbreaks. The objective of this study was first, to determine the spatial temporal distribution pattern of dengue fever cases in the study area. Secondly, to investigate the variation of dengue fever hot-spot in peri-urban area in Kuantan. Lastly, to developed a prediction model of dengue fever cases using SARIMA model. Moran's index for DF transmissions in the Kuantan area was tabulated monthly from 2011 to 2020. The lowest reading of Moran's index was -0.685 in May 2015, while the highest reading was 0.338 in May 2019. This reflects the strong spatial autocorrelation of dengue transmission over the last decade. According to the Getis-Ord Gi* statistic, there were four hot spots of dengue fever in 2011, and the locality increased to twenty two hot spots around Kuantan district in 2020. High and low clusters of attributes were created using the z-scores and p-values. The expected outcome was a statistically significant hot spot of spatially clustered features and a statistically significant z-score. The time series prediction was made using the dengue cases from January to December 2011-2019. The study revealed that SARIMA (0, 1, 0) (3, 0, 2)12 was the best fitted model and could be used to predict the cases up to twelve months in advance. The prediction of the cases in 2020 was relatively close to the actual cases within the confidence interval limit. Thus, the model derived from this study has the capability to not only forecast but also anticipate the future dengue cases. This would in turn enhance the current intervention program which is vital in minimizing the burden of the disease in Kuantan specifically.