Identifying leptospirosis hotspots in Selangor: uncovering climatic connections using remote sensing and developing a predictive model

This GIS-based study identified a primarily sporadic occurrence of leptospirosis in Selangor with minimal spatial clustering. The LGBM algorithm effectively predicted leptospirosis hotspots based on the analysed hydroclimatic factors. The integration of GIS and machine learning offers a promising framework for disease surveillance, facilitating targeted public health interventions in areas at high risk for leptospirosis.