AI-Driven Public Health Surveillance: Analyzing Vulnerable Areas in Brazil Using Remote Sensing and Socioeconomic Data

Urban vulnerability assessment is crucial for understanding the spatial distribution of deprived areas and associated risks. Slum residents face significantly worse health outcomes than non-slum urban populations, with neighborhood effects being critical in social epidemiology. Identifying such areas is vital because they present public health challenges that climate change and increased air pollution can exacerbate. Accordingly, this study proposed an AI-driven methodology that integrates remote sensing data, socioeconomic indicators, and machine learning algorithms to identify and analyze vulnerable areas in Brazil. To create a vulnerability index, we incorporate multiple data sources, including Sentinel-2 and Sentinel-5P imagery, Brazilian socioeconomic indicators, and OpenStreetMap. Hence, we predicted pollution indicators using regression algorithms such as Random Forest, XGBoost, and Linear Regression. Our findings demonstrate that integrating multi-source data is a promising approach for better understanding deprived areas, indicating that slums (called “favelas” in Brazil) exhibit an intense concentration of the sociocconomic vulnerability index, a key determinant of deprivation. However, non-slum areas may present heterogeneous conditions, with some regions showing vulnerability levels comparable to those of slums while others show better conditions. Our results highlight the potential of AIdriven approaches for urban vulnerability assessment, offering insights for policymakers and researchers.