Planetary Geospatial Foundation Models: A New Paradigm for Global Public Health

2026-10-05

AI Summary

This study addresses the surveillance blind spots and inefficient resource allocation in traditional disease prediction caused by spatial gaps and temporal lags by proposing a novel geospatial foundation model paradigm. Leveraging the Population Dynamics Foundation Model (PDFM) developed by Google Earth AI, this work encodes multimodal signals—including search queries, human mobility, and environmental data—into universal place representations that integrate seamlessly into existing epidemiological workflows. The proposed framework enables transferable public health surveillance across diverse domains and national borders. Empirical evaluations demonstrate that it significantly improves predictive accuracy for vaccine coverage estimation, cardiovascular disease prevalence assessment, dengue and cholera forecasting, and postpartum depression risk identification, thereby offering a scalable solution for global health monitoring.

Abstract

The efficacy of traditional disease prediction is limited by spatial gaps and temporal lags, which impact the timing and targets of resource deployments. Outbreaks escalate undetected, chronic disease burdens are quantified years later, and at-risk populations in data-sparse regions remain unaddressed. Planetary geospatial foundation models complement existing epidemiological workflows to provide operational improvements, encoding multimodal search, mobility, and environmental signals into generalizable place representations. As illustrations of this complementarity, we present independent global health case studies of Google Earth AI's Population Dynamics Foundation Model (PDFM) -- a foundation model for geospatial inference -- across four domains (vaccine-preventable, communicable, noncommunicable, maternal mental health), five tasks (spatial extrapolation, interpolation/nowcasting, probabilistic forecasting, prospective forecasting, risk stratification), and four countries (USA, Canada, Mexico, and the Democratic Republic of the Congo). Across these case studies, PDFM addresses critical surveillance gaps across domains: improving US-Canada border MMR vaccination coverage predictions by capturing cross-border behavioral spillovers domestic models miss; nowcasting cardiovascular disease to accelerate data availability; enhancing short-term municipal Mexican dengue forecasts for timely outbreak vector control; improving forecasts of cholera hotspots; and adding a transferable signal to individual-level postpartum-depression risk prediction in US states the model had never seen, while not replacing individual socioeconomic data or closing demographic screening gaps. Together, these results showcase capabilities of geospatial foundation models for public health surveillance.

Problem

• disease prediction
• public health surveillance
• spatial gaps
• temporal lags
• geospatial foundation models

Innovation

• Geospatial Foundation Model
• Public Health Surveillance
• Multimodal Representation
• Spatial Extrapolation
• Nowcasting