Tropical medicine and health
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
By Venkatesh U, Ali A and Ahmed A.
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.