An integrated GIS and remote-sensing framework for district-level dengue risk assessment in northern India: development of a spatial Dengue Risk Index and assessment of its spatial concordance with reported incidence

Abstract Background Dengue is an increasingly important vector-borne disease driven by climate variability, urbanization, environmental change, and population growth. Despite its rising burden in northern India, district-level spatial epidemiological frameworks integrating environmental suitability, transmission hazard, and population vulnerability remain limited. This study developed an integrated geographic information system (GIS)- and remote-sensing (RS)-based Dengue Risk Index (DRI) describing long-term spatial susceptibility to dengue in Gorakhpur district, Uttar Pradesh, and assessed its spatial concordance with reported dengue incidence. Methods A GIS-based multi-criteria decision analysis (MCDA) framework incorporating the analytic hierarchy process (AHP) integrated 14 environmental, topographic, hydrological, and socio-demographic variables into three thematic components: the Environmental Suitability Index (ESI; six variables), Hazard Index (HI; five variables), and Vulnerability Index (VI; three variables). Relative humidity and the Normalized Difference Moisture Index were excluded after redundancy screening. Rainfall was derived from India Meteorological Department (IMD) 0.25° daily gridded data for 2025, elevation from the Copernicus 30 m DEM, and population density from WorldPop 2025 constrained estimates. Standardized (1–5) raster layers were integrated using the Weighted Linear Combination (WLC) approach to construct the DRI. Spatial concordance between block-level mean DRI and laboratory-confirmed dengue incidence in 2025 (225 cases, 20 blocks) was assessed using Pearson’s and Spearman’s correlation, linear and negative binomial regression, influence diagnostics, and global Moran’s I with Lagrange multiplier tests for spatial dependence. Findings The DRI (1–5 relative scale) showed marked spatial heterogeneity, with higher values in the central and western parts of the district. Chargawan, which contains Gorakhpur city, was the only block classified as Very High risk, followed by Sahjanwa, Piprauli, and Jungle Kaudia (High), whereas Brahmpur was classified as Very Low risk. Overall, 32.87% of the district (1,090.2 km 2 ) was classified as High or Very High risk. The single very high-risk block accounted for 48.9% of reported cases. However, concordance between block-level DRI and incidence was weak and not statistically significant (Pearson's r = 0.16, p = 0.49; Spearman's ρ = −0.003, p = 0.99; R 2 = 0.03; incidence rate ratio per 0.1 DRI = 1.05, 95% CI 0.91–1.21). Neither incidence nor the regression residuals showed significant spatial autocorrelation (Moran's I = −0.13 and −0.17; p ≥ 0.44), so spatial regression was not required. Interpretation The DRI provides a transparent, reproducible description of long-term spatial susceptibility to dengue that integrates environmental suitability, transmission hazard, and population vulnerability. Because its concordance with single-year, block-level incidence was weak and non-significant, it should not be interpreted as a predictor of dengue incidence or as a validated risk model. The framework may help district programs structure surveillance planning, but multi-year incidence, finer spatial units, and entomological data are needed before it is used to allocate interventions.