Spatial pattern of 2009 dengue distribution in Kuala Lumpur using GIS application

• Main Authors: Aziz, S., Romano, Ngui, Yvonne Ai Lian, Lim, Sholehah, I., Nur Farhana, J., Azizan, Abu Samah, Wan Yusoff, Wan Sulaiman
• Format: Article
• Language: English
• Published: Malaysian Society of Parasitology and Tropical Medicine (MSPTM) 2012
• Subjects: QR Microbiology; RA0421 Public health. Hygiene. Preventive Medicine
• Online Access: http://ir.unimas.my/id/eprint/42317/1/Spatial.pdf; http://ir.unimas.my/id/eprint/42317/; https://msptm.org/journal-vol-29-no-1/

In the last few years in Malaysia, dengue fever has increased dramatically and has caused huge public health concerns. The present study aimed to establish a spatial distribution of dengue cases in the city of Kuala Lumpur using a combination of Geographic Information System (GIS) and spatial statistical tools. Collation of data from 1,618 dengue cases in 2009 was obtained from Kuala Lumpur City Hall (DBKL). These data were processed and then converted into GIS format. Information on the average monthly rainfall was also used to correlate with the distribution pattern of dengue cases. To asses the spatial distribution of dengue cases, Average Nearest Neighbor (ANN) Analysis was applied together with spatial analysis with the ESRI ArcGIS V9.3 programme. Results indicated that the distribution of dengue cases in Kuala Lumpur for the year 2009 was spatially clustered with R value less than 1 (R = 0.42; z-scores = - 4.47; p < 0.001). Nevertheless, when this pattern was further analyzed according to month by each zone within Kuala Lumpur, two distinct patterns were observed which include a clustered pattern (R value < 1) between April to June and a dispersed pattern (R value > 1) between August and November. In addition, the mean monthly rainfall has not influenced the distribution pattern of the dengue cases. Implementation of control measures is more difficult for dispersed pattern compared to clustered pattern. From this study, it was found that distribution pattern of dengue cases in Kuala Lumpur in 2009 was spatially distributed (dispersed or clustered) rather than cases occurring randomly. It was proven that by using GIS and spatial statistic tools, we can determine the spatial distribution between dengue and population. Utilization of GIS tools is vital in assisting health agencies, epidemiologist, public health officer, town planner and relevant authorities in developing efficient control measures and contingency programmes to effectively combat dengue fever.