Application of geographical information system-based analytical hierarchy process as a tool for dengue risk assessment

Objective: To highlight the use of analytical hierarchy process (AHP) in geographical information system that incorporates environmental indices to generate dengue risk zonation area. Methods: The medical database considered for the study was referenced to the environmental data layers. Factors...

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Bibliographic Details
Main Authors: Che Dom, Nazri, Ahmad, Abu Hassan, Abd Latif, Zulkiflee, Ismail, Rodziah
Format: Article
Published: Elsevier 2016
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Online Access:http://eprints.usm.my/38351/
https://doi.org/10.1016/S2222-1808(16)61158-1
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Summary:Objective: To highlight the use of analytical hierarchy process (AHP) in geographical information system that incorporates environmental indices to generate dengue risk zonation area. Methods: The medical database considered for the study was referenced to the environmental data layers. Factors related to the risk of dengue fever (DF) were selected throughout previous research and were arranged in a hierarchical structure. The relative weights of factors were calculated, which were within acceptable range with the consistency ratio being less than 0.1. The outcomes from AHP based DF risk zonation area produced useful information on different levels of risks. Results: As a result, factor weights used in AHP were evaluated and found to be acceptable as the consistency ratio of 0.05, which was < 0.1. The most influential factors were found to be housing types, population density, land-use and elevation. Findings from this study provided valuable insights that could potentially enhance public health initiatives. The geographical information system and spatial analytical method could be applied to augment surveillance strategies of DF and other communicable diseases in an effort to promote actions of prevention and control. The disease surveillance data obtained could be integrated with environmental database in a synergistic way, which will in turn provide additional input towards the development of epidemic forecasting models.