Geospatial health data are essential to inform public health decision-making and policy. These data can be used to understand geographic and temporal patterns, identify risk factors, measure inequalities, and quickly detect outbreaks. In this talk, I will give an overview of statistical methods and computational tools for geospatial data analysis and health surveillance. I will discuss models for infectious disease nowcasting and forecasting, with a particular focus on dengue surveillance in Brazil during the 2024 dengue epidemic, highlighting challenges related to data biases and availability. I will also present modeling advancements to integrate complex health, climate, and digital data sources at multiple spatial and temporal resolutions to predict disease risk and environmental variables. Finally, I will discuss the importance of effective communication and dissemination to inform policymaking and improve population health.