Dengue is a vector borne disease which is one of the major threats to public health in Malaysia. Mapping of dengue distribution in spatial and spatial-temporal aspects can be a useful method in assessing the risk of dengue to the community. This study aimed to map the spatial and spatial-temporal distribution of dengue cases in Seremban district. The Geographical Information System specifically the spatial and spatial-temporal analyses was applied. Spatial statistical analysis of dengue cases used the Moran’s Index, average nearest neighbourhood (ANN) and kernel density estimation. Spatial-temporal analysis was determined through frequency, duration and intensity indices to identify timely dengue risk area. A total of 6076 dengue cases were reported in Seremban Health District Office from 2003-2009. The result showed a high incidence rate in 2003, 2008 dan 2009 with ratio of dengue: dengue hemorrhagic fever of 21.6:1. Moran’s I showed dengue cases occurred in cluster with Z-score of 16.384 (p=0.000). ANN analysis of 0.264 (p= 0.000) where the mean distance between every dengue case is 55 m. Kernel density estimation showed the dengue hotspots concentrated in Nilai and Ampangan. Spatial-temporal analysis with the highest mean of frequency, duration and intensity indices of above 0.023, 0.614 and 0.657 showed that the high risk dengue areas were Nilai, Seremban and Ampangan. The dengue control activities should be targeted at these high risk areas.

Dengue is a major public health threat in Malaysia, which is known for the hyperendemicity with all the four serotypes of the dengue virus circulating concurrently. Annual dengue cases reported were 43,000 cases for 2013, and this imposed a heavy toll on the resources for dengue prevention and control program. The objective of mapping in our study is to determine the spatial clustering of the dengue cases and to identify the areas that are vulnerable to dengue outbreaks. A Geographical Information System (GIS) was used to assess the vulnerability of Seremban district. Dengue data were obtainedfrom the Ministry ofHealth. We determined the spatial distribution, the average distance of dengue cases and identified hotspots areas using the Moran'-s -I, Average Nearest Neighbourhood (ANN), Kernel density estimation. Vulnerability to dengue was assessed with the spatial temporal analyses and Local Indicator for Spatial Autocorrelation (LISA). From 2003-2009 Seremban recorded 6076 dengue cases. Moran'-s I showed the cases occurred in clusters with a Z-score of 16.384 (p<0.001). ANN 0.264 (p<0.001) indicated the mean distance between every dengue case was 55 meters. Kernel density estimation showed hotspots of dengue were concentrated in two subdistricts. This paper discusses how spatial-temporal approach can be used to assess the vulnerability of Seremban to dengue where control activities can be more focused to these high risk areas. Mapping the dengue distribution using spatial-temporal approach is useful and guides the public health management of dengue.