Integration of GIS-Based Model with Epidemiological data as a Tool for Dengue Surveillance
This study aims to fully integrated and validated spatial temporal statistical model using epidemiological data as a predictive model for surveillance and control of DF cases. Kernel-density estimation (KDE) method was carried out by using spatial union analysis in order to predict and visualize the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
TSHE.ORG ,
2017.
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| Subjects: | |
| Online Access: | Get fulltext |
| Summary: | This study aims to fully integrated and validated spatial temporal statistical model using epidemiological data as a predictive model for surveillance and control of DF cases. Kernel-density estimation (KDE) method was carried out by using spatial union analysis in order to predict and visualize the DF hotspot area by monthly basis in the Subang Jaya area. The generated maps were then verified using Receiver operating characteristics (ROC) was performed to validate the DF hotspot simulation model. Spatial analysis showed that the dengue epidemics in Subang Jaya were spatially dependent. This analysis demonstrated spatial clustering of dengue activity which can facilitate prediction of the magnitude, timing and location of future dengue epidemic. The model developed highlights the adaptation capabilities of the approach where the accuracy assessment result showed accuracy about 60% agreements between the hotspot map and the actual DF location data. It can thus be suggested that any future population increase will be associated with increased DF risk in areas which already accommodate this disease environmentally, climatically and socioeconomically. Future risk could be modelled using the same methods. This would help decision maker in choosing which areas should be under intensive treatment to counter mosquito breeding and reduce prevalence of DF. |
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