SPATIOTEMPORAL ANALYSIS OF DENGUE CASES IN CEBU CITY FROM YEAR 2015 TO 2022
Keywords: Dengue, Land surface temperature, NDVI, NDBI, NDWI, Climate Engine
Abstract. Long-term climate changes, including increased temperature, shift in precipitation, wind patterns, and other climate factors, can disrupt the balance of nature and have significant implications in the transmission of dengue fever. This study investigated the spatial and temporal dynamics of dengue cases in Cebu City, a key metropolitan area in the Philippines characterized by a significant rate of urbanization in recent years. Climate Engine (CE), a cloud-based computing and visualization tool, was utilized in this study for database sources of Landsat 8 pre-processed satellite images. Time-series dataset of land surface temperatures (LST) and varying environmental indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) were examined to investigate the effects of increased urban surface temperatures, expanding urban structures, and diminishing vegetation in Cebu City on dengue cases from 2015 to 2022. The spatial distribution of dengue cases was analyzed through GeoDA to identify hotspots within the city. The annual dengue cases in Cebu City exhibit a temporal trend with a peak in 2016 (4637 cases) and a lowest point in 2021 (399 cases), the year when the pandemic struck. Most dengue cases were recorded between June and December, exhibiting a strong seasonal pattern, and primarily concentrated within the wet season. Barangay Guadalupe topped the number of cases (1781) followed by Barangay Lahug (1219 cases), and Barangay Labangon (1128 cases) from 2015 to 2022. These three residential barangays are in proximity to each other, indicating a potential localized clustering of dengue cases in neighboring areas. The equation derived from the linear regression model serves as a predictive tool for estimating dengue cases in Cebu City and is expressed as Dengue cases = −28.436 + 2.137 (LST) − 13.943 (NDVI) + 8.565 (NDWI) − 10.217 (NDBI). The findings of this study will have practical implications for urban planning and the development of local policies aimed at mitigating the rise in dengue cases.