Lia Faridah1,2, I Gede Nyoman Mindra3, Ramadhani Eka Putra4, Nisa Fauziah5, Dwi Agustian6, Yessika Adelwin Natalia6, Kozo Watanabe7. 1. Parasitology Division, Department of Biomedical Science, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia. lia.faridah@unpad.ac.id. 2. Foreign Visiting Researcher at Department of Civil and Environmental Engineering, Ehime University, Matsuyama, Japan. lia.faridah@unpad.ac.id. 3. Department of Statistics, Universitas Padjadjaran, Bandung, Indonesia. 4. School of Life Science and Technology, Institut Teknologi Bandung, Jl. Ganeca 10, Bandung, West Java, 40132, Indonesia. 5. Parasitology Division, Department of Biomedical Science, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia. 6. Department of Public Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia. 7. Department of Civil and Environmental Engineering, Ehime University, Matsuyama, Japan.
Abstract
BACKGROUND: Bandung, the fourth largest city in Indonesia and capital of West Java province, has been considered a major endemic area of dengue, and studies show that the incidence in this city could increase and spread rapidly. At the same time, estimation of incidence could be inaccurate due to a lack of reliable surveillance systems. To provide strategic information for the dengue control program in the face of limited capacity, this study used spatial pattern analysis of a possible outbreak of dengue cases, through the Geographic Information System (GIS). To further enhance the information needed for effective policymaking, we also analyzed the demographic pattern of dengue cases. METHODS: Monthly reports of dengue cases from January 2014 to December 2016 from 16 hospitals in Bandung were collected as the database, which consisted of address, sex, age, and code to anonymize the patients. The address was then transformed into geocoding and used to estimate the relative risk of a particular area's developing a cluster of dengue cases. We used the kernel density estimation method to analyze the dynamics of change of dengue cases. RESULTS: The model showed that the spatial cluster of the relative risk of dengue incidence was relatively unchanged for 3 years. Dengue high-risk areas predominated in the southern and southeastern parts of Bandung, while low-risk areas were found mostly in its western and northeastern regions. The kernel density estimation showed strong cluster groups of dengue cases in the city. CONCLUSIONS: This study demonstrated a strong pattern of reported cases related to specific demographic groups (males and children). Furthermore, spatial analysis using GIS also visualized the dynamic development of the aggregation of disease incidence (hotspots) for dengue cases in Bandung. These data may provide strategic information for the planning and design of dengue control programs.
BACKGROUND: Bandung, the fourth largest city in Indonesia and capital of West Java province, has been considered a major endemic area of dengue, and studies show that the incidence in this city could increase and spread rapidly. At the same time, estimation of incidence could be inaccurate due to a lack of reliable surveillance systems. To provide strategic information for the dengue control program in the face of limited capacity, this study used spatial pattern analysis of a possible outbreak of dengue cases, through the Geographic Information System (GIS). To further enhance the information needed for effective policymaking, we also analyzed the demographic pattern of dengue cases. METHODS: Monthly reports of dengue cases from January 2014 to December 2016 from 16 hospitals in Bandung were collected as the database, which consisted of address, sex, age, and code to anonymize the patients. The address was then transformed into geocoding and used to estimate the relative risk of a particular area's developing a cluster of dengue cases. We used the kernel density estimation method to analyze the dynamics of change of dengue cases. RESULTS: The model showed that the spatial cluster of the relative risk of dengue incidence was relatively unchanged for 3 years. Dengue high-risk areas predominated in the southern and southeastern parts of Bandung, while low-risk areas were found mostly in its western and northeastern regions. The kernel density estimation showed strong cluster groups of dengue cases in the city. CONCLUSIONS: This study demonstrated a strong pattern of reported cases related to specific demographic groups (males and children). Furthermore, spatial analysis using GIS also visualized the dynamic development of the aggregation of disease incidence (hotspots) for dengue cases in Bandung. These data may provide strategic information for the planning and design of dengue control programs.
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