Dengue Hemorrhagic Fever Risk Modeling Using Climate Reanalysis and Remote Sensing Data in Bantul Regency

Abstract

Dengue Hemorrhagic Fever (DHF) is an environmentally-based disease whose spread is influenced by climate and population factors. The use of remote sensing and climate analysis can support the identification of dengue risk areas spatially. This study aims to describe the level of dengue risk in Bantul Regency using the Multi-Criteria Decision Analysis approach through the Analytical Hierarchy Process (AHP) method based on Geographic Information Systems (GIS). The analysis was carried out by integrating remote sensing data, climate analysis, and AHP. The parameters used include population density, rainfall, air temperature, air humidity, and the vegetation index (Normalized Difference Vegetation Index/NDVI). Data processing was carried out using Google Earth Engine (GEE), then analyzed using the Weighted Overlay method in GIS. The AHP weighting results show that population density has the largest influence with a weight of 0.416, followed by rainfall (0.262), air temperature (0.161), air humidity (0.099), and NDVI (0.062). A Consistency Ratio (CR) value of 0.015 indicates that the pairwise comparison matrix has a good level of consistency. The spatial analysis results classify the level of dengue fever risk into five classes: very low, low, medium, high, and very high. The high to very high-risk zone is located in the north-central region of Bantul Regency, which is filled with high population density and climatic conditions that support vector development. The results show that the integration of remote sensing, climate analysis, and GIS-based AHP is effective in identifying priority areas for dengue control spatially.