Improving the precision of public health interventions
In Malawi, Google.org grantee Cooper/Smith combined Earth AI’s PDFM and AlphaEarth satellite embeddings with local data to predict health service utilization at local clinics. This can help decision-makers spot early warning signs of disease outbreaks and allocate limited resources more efficiently.
To combat the rise of measles, researchers at Mount Sinai and Boston Children’s Hospital/Harvard, used Earth AI’s PDFM to fill gaps and produce “superresolution” estimates of vaccination coverage. Based on privacy-preserving, aggregated data, researchers can map vaccination rates down to the ZIP-code level without revealing sensitive personal information, and identify localized clusters of undervaccination that align with recent outbreaks.
Forecasting for diseases where weather and geography matter
Weather influences the pace of many diseases, and specific weather patterns can signal health crises. For example, summer rains can cause dengue fever to spike, while flooding can significantly increase cholera outbreaks. Combining population dynamics with predictive weather models helps improve forecasting of health emergencies weeks or months in advance.
In collaboration with the WHO Regional Office for Africa, we evaluated a sub-national forecasting model for cholera cases utilizing the WHO centralized Integrated Disease Surveillance Data. We found that by combining Google’s TimesFM time-series model with PDFM and weather data, we were able to improve the forecasting accuracy of cholera cases by over 35% compared to standard models. Better forecasting could enable public health officials to plan proactively, rather than react after a crisis — for example by moving life-saving rehydration supplies to where they will be needed.
Furthermore, researchers at the University of Oxford have successfully used Earth AI models and datasets to improve forecasting of dengue fever in Brazil. Including PDFM embeddings significantly raised the predictive accuracy of six-month forecasts, giving local authorities more time to implement preventative measures.
Understanding chronic disease needs
Earth AI is also unlocking critical insights into non-communicable diseases. In a recent initiative in Australia, we partnered with the Victor Chang Cardiac Research Institute, Wesfarmers Health and Latrobe Health Services to deploy Population Health AI (PHAI). Currently available as a proof-of-concept to select partners, PHAI uses Earth AI’s PDFM embeddings alongside other key datasets like air quality, pollen and places insights to uncover the health needs of communities in rural Australia, aiming to support their chronic disease needs and prevention efforts.
A proactive, healthier future
Technology is most powerful when it leads to real-world action. By fusing Google Earth AI’s planetary intelligence along with the deep health expertise of our partners, we are moving toward a future goal where health systems everywhere possess the data-driven insights needed to protect and improve public health.
