Google Says Its AI Mapped 48 Ebola-Exposed Settlements in Minutes

Public-health responders in the Democratic Republic of Congo used an AI prototype to locate remote communities exposed to Ebola in minutes, Google said in an October 6 research disclosure. The WHO Regional Office for Africa identified 48 settlements and more than 45,500 at-risk people—a mapping task Google says would normally have taken weeks.

Connecting geography with local health records

Google Earth AI combines environmental signals, satellite imagery and mobility data with models that help analyze places and populations. Local health information supplies the disease-specific context. The research uses AlphaEarth Foundations and the Population Dynamics Foundation Model, or PDFM, alongside a prototype Geospatial Reasoning agent.

PDFM combines aggregated search trends with movement and environmental patterns to describe communities. Partners can integrate those signals into their own systems. For emergency work, Google gave partners access to two research prototypes: an agent for mapping through plain-language conversations, and a planetary prediction engine for automated disease forecasting.

From mining corridors to response decisions

In the Ebola response, the WHO team used the mapping agent to examine remote mining corridors with high exposure risk and human movement. Google says the resulting population map allowed local responders to deploy mobile laboratories proactively and coordinate border surveillance.

Forecasting was a separate task within that response. Working with the epidemic modeling unit at the DRC’s National Institute of Biomedical Research, Google built models estimating Ebola’s risk of spreading into uninfected zones. The models combine mobility flows, historical case trends and Earth AI models and datasets. Weekly estimates are intended to give coordinators planning time before cases arrive.

Other evaluations, different prediction targets

The disclosure also describes partner research beyond emergency mapping. Each project pairs population signals with information specific to the health question, rather than using geography alone. Google reports the following results:

• Cardiovascular mortality: NYU Langone Health researchers added real-time population signals to chronic disease models. Same-year projections performed similarly to conventional approaches, and better in some cases.
• Vaccination coverage: Mount Sinai and Boston Children’s Hospital researchers incorporated behavioral patterns on both sides of the U.S.-Canada border to estimate U.S. county vaccination rates more accurately.
• Dengue: University of Oxford and Tecnológico de Monterrey researchers combined PDFM with local climate models to improve outbreak forecasts across Mexico. Google says earlier warning could support action such as killing mosquito larvae.

Access differs between the emergency prototypes and the underlying population dataset. Google says PDFM embeddings—model-generated data representations—are commercially available in Preview as Population Dynamics Insights through Google Maps Platform. Researchers can request no-cost access for selected uses, and eligible organizations can apply for Google Earth credits. That dataset access is distinct from the prototype tools used by outbreak-response partners.