Spatial Mapping of Dengue Incidence: A Case Study in Hulu Langat District, Selangor, Malaysia

Dengue is a mosquito-borne infection that has peaked to an alarming rate in recent decades. It can be found in tropical and sub-tropical climate. In Malaysia, dengue has been declared as one of the national health threat to the public. This study aimed to map the spatial distributions of dengue cases in the district of Hulu Langat, Selangor via a combination of Geographic Information System (GIS) and spatial statistic tools. Data related to dengue was gathered from the various government health agencies. The location of dengue cases was geocoded using a handheld GPS Juno SB Trimble. A total of 197 dengue cases occurring in 2003 were used in this study. Those data then was aggregated into sub-district level and then converted into GIS format. The study also used population or demographic data as well as the boundary of Hulu Langat. To assess the spatial distribution of dengue cases three spatial statistics method (Moran-s I, average nearest neighborhood (ANN) and kernel density estimation) were applied together with spatial analysis in the GIS environment. Those three indices were used to analyze the spatial distribution and average distance of dengue incidence and to locate the hot spot of dengue cases. The results indicated that the dengue cases was clustered (p < 0.01) when analyze using Moran-s I with z scores 5.03. The results from ANN analysis showed that the average nearest neighbor ratio is less than 1 which is 0.518755 (p < 0.0001). From this result, we can expect the dengue cases pattern in Hulu Langat district is exhibiting a cluster pattern. The z-score for dengue incidence within the district is -13.0525 (p < 0.0001). It was also found that the significant spatial autocorrelation of dengue incidences occurs at an average distance of 380.81 meters (p < 0.0001). Several locations especially residential area also had been identified as the hot spots of dengue cases in the district.

• Er
• A. C.
• Rosli
• M. H.
• Asmahani A.
• Mohamad Naim M. R.
• Harsuzilawati M.

• Dengue
• geographic information system (GIS)
• spatial analysis
• spatial statistics

[1] WHO - Regional Office for South-East Asia, SEARO (1999).
Comprehensive Guidelines: Prevention and control of dengue and dengue
haemorrhagic fever, WHO Regional Publication, SEARO No.29, 1999.
[2] Gubler DJ, Meltzer M. (1999). Impact of dengue/dengue hemorrhagic
fever on the developing world. Adv Virus Res 1999;53:35-70.
[3] Mohd Din, M., M. Shaaban, et al. (2007). A Study of Dengue Disease
Data by GIS Software in Urban Areas of Petaling Jaya Selatan. GIS for
Health and the Environment: 206-213.
[4] Derouich, M., A. Boutayeb, et al. (2003). "A model of dengue fever."
BioMedical Engineering OnLine 2(1): 4.
[5] Nakhapakorn, K. and N. Tripathi (2005). "An information value based
analysis of physical and climatic factors affecting dengue fever and
dengue haemorrhagic fever incidence." International Journal of Health
Geographics 4(1): 13.
[6] Nakhapakorn K, Jirakajohnkool S (2006). Temporal and spatial
autocorrelation statistics of dengue fever. Dengue Bull 30: 177-183.
[7] Mitchell, A. (2005). The ESRI Guide to GIS Analysis: Volume 2: Spatial
Measurements and Statistics: ESRI Press.
[8] Fotheringham, A. S., C. Brunsdon, et al. (2002). Geographically
Weighted Regression: the Analysis of Spatially Varying Relationships,
Wiley.
[9] Srividya, A., Michael, E., Palaniyandi, M., Pani, S. P., & Das, P. K.
(2002). A geostatistical analysis of the geographic distribution of
lymphatic filariasis prevalence in southern India. American Journal of
Tropical Medicine and Hygeine, 67(6).
[10] Wen, T.-H., Lin, N. H., Chao, D.-Y., Hwang, K.-P., Kan, C.-C., Lin, K.
C.-M., et al. (2010). Spatial-temporal patterns of dengue in areas at risk of
dengue hemorrhagic fever in Kaohsiung, Taiwan, 2002. International
Journal of Infectious Diseases, 14(4), e334-e343.
[11] Levine, N. (2007). Crimestat: A Spatial Statistics Program for the
Analysis of Crime Incident Locations (Version 3.0). Washington, D.C.:
Ned Levine & Associates, Houston, TX and the National Institute of
Justice.
[12] Bithell, J. F. (1990). An application of density estimation to geographical
epidemiology. Statistics in Medicine, 9, 691-701.
[13] Malone, J. B. (2005). Biology-based mapping of vector-borne parasites
by geographic information systems and remote sensing. Parassitologia,
47, 27-50.