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

Arbaaz Muslim; Aviv Slobodkin; Katherine Wheeler-Martin; Eric Zhou; John Brittain; Martin Frasch; Hamsa Subramaniam; Jacob Bien; Hamed Sadeghi; Sarah Conderino; Stone Jiang; Ben Spoer; Daniel Neill; Mimi Sun; Joydeep Paul; Yun Liu; Tomer Shekel; Amy Chung-Yu Chou; Anna Carter; Charles Elliott; Aaron Bell; Reuven Sayag; Avinatan Hassidim; Niv Efron; Yossi Matias; Kasumi Widner; Eduardo Lopez Ortiz; José Alberto Díaz-Quiñonez; Moritz U. G. Kraemer; John Brownstein; Isaias Fernandes Co; Etien Luc Koua; Monica Bharel; Benjamin Rader; Lorna Thorpe; Marc Gourevitch; Shravya Shetty; Gautam Prasad

arXiv · 2610.05699

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

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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.

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Arbaaz Muslim, Aviv Slobodkin, Katherine Wheeler-Martin, Eric Zhou, John Brittain, Martin Frasch, Hamsa Subramaniam, Jacob Bien, Hamed Sadeghi, Sarah Conderino, Stone Jiang, Ben Spoer, Daniel Neill, Mimi Sun, Joydeep Paul, Yun Liu, Tomer Shekel, Amy Chung-Yu Chou, Anna Carter, Charles Elliott, Aaron Bell, Reuven Sayag, Avinatan Hassidim, Niv Efron, Yossi Matias, Kasumi Widner, Eduardo Lopez Ortiz, José Alberto Díaz-Quiñonez, Moritz U. G. Kraemer, John Brownstein, Isaias Fernandes Co, Etien Luc Koua, Monica Bharel, Benjamin Rader, Lorna Thorpe, Marc Gourevitch, Shravya Shetty, Gautam Prasad. 2026-10-05. Planetary Geospatial Foundation Models: A New Paradigm for Global Public Health. https://arxiv.org/abs/2610.05699

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