A community’s health is deeply connected to its geography, which dictates climate patterns, physical infrastructure, and the ways people live and move. But public health teams frequently grapple with geographic blind spots — as well as fragmented data and reporting delays — whether they’re tracking a fast-moving viral outbreak or projecting cardiovascular mortality. Equipping health leaders with advanced predictive tools can better protect at-risk communities, and help shift emergency response from reactive management to proactive prevention.
In new research papers co-authored with global health partners, we demonstrate how Google Earth AI can bridge critical gaps across diverse geographies, diseases, and areas of public health using autonomous predictions and population dynamics.
By combining environmental signals with satellite imagery, mobility data, and foundation models — such as AlphaEarth Foundations and our Population Dynamics Foundation Model (PDFM) — and pairing them with a prototype Geospatial Reasoning agent, we help communities uncover the complex connections between people and their environments.
This enables researchers and public health officials to complement existing health information, bridge reporting gaps, and address health challenges proactively.
Bringing agentic capabilities to the front lines
Acute health crises demand rapid, intuitive tools. To validate the power of Earth AI during active emergencies, several partners were given access to two research prototypes: the Geospatial Reasoning agent using Google Earth AI capabilities for conversational spatial mapping, and the planetary prediction engine for autonomous disease forecasting. Through plain-language conversations, public health teams can streamline complex manual data processing with timely insights and clear decision support.
During the ongoing Ebola outbreak in the Democratic Republic of Congo (DRC), our partners at the World Health Organization’s Regional Office for Africa (WHO AFRO) Emergency Preparedness and Response Hub in Dakar and the Epidemic Modeling and Intelligence Unit (UMIE) at the DRC’s National Institute of Biomedical Research (INRB) put these prototypes to work.
- Uncovering transmission blind spots: The WHO AFRO team used our Geospatial Reasoning agent prototype to map remote mining corridors where the exposure risk and human mobility are high. In minutes, the team pinpointed 48 exposed settlements and located more than 45,500 at-risk people — a process that normally would have taken weeks. This allowed local responders to proactively deploy mobile laboratories and coordinate border surveillance.
- Simulating outbreak trajectories: Working alongside UMIE, we built predictive models that estimate the risk of Ebola spread into uninfected zones. By combining mobility flows with historical case trends and Earth AI models and datasets, these weekly insights give coordinators critical planning time before cases arrive.
- Scaling health predictions: Beyond fast-moving outbreaks, our research shows pairing Earth AI’s capabilities with public health data can predict broader community health trends more accurately than manual analysis.
Source: blog.google
