Application of remote sensing and geospatial technologies in predicting vector-borne disease outbreaks.

Abstract

Vector-borne diseases (VBDs) pose significant global health threats, particularly in tropical and subtropical regions. Remote sensing (RS) and geospatial technologies offer valuable tools for monitoring environmental changes and predicting disease transmission patterns, thereby supporting proactive public health interventions. This study reviews the application of RS and geospatial methods in the prediction, monitoring and control of VBDs. A systematic approach was employed to analyse existing literature, focusing on RS platforms such as Landsat, MODIS and Sentinel-2, alongside geographical information systems and machine learning models used for predictive modelling. The review reveals that these technologies play a crucial role in identifying environmental drivers of disease dynamics, including temperature, precipitation and land-use changes. However, challenges remain in terms of data resolution, model generalizability and the integration of socio-economic factors into predictive frameworks. The integration of early warning systems and participatory surveillance is highlighted as a promising avenue for improving disease forecasting. The study emphasizes the need for enhanced data accessibility, cross-sector collaboration and the inclusion of socio-economic variables in future research to improve the scalability and accuracy of disease prediction models.