行星地理空间基础模型：全球公共卫生的新范式
Planetary Geospatial Foundation Models: A New Paradigm for Global Public Health
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中文总结 AI 辅助
本文提出行星地理空间基础模型作为公共卫生监测新范式，通过PDFM在四国多任务案例中填补监测空白，改进疾病预测与风险分层。
中文摘要 AI 辅助
传统疾病预测的有效性受到空间空白和时间滞后的限制，这影响了资源部署的时机和目标。疫情在未被察觉的情况下升级，慢性病负担在数年后才被量化，数据稀疏地区的高风险人群仍未得到关注。行星地理空间基础模型补充了现有的流行病学工作流程，提供操作上的改进，将多模态搜索、移动性和环境信号编码为可泛化的地点表示。作为这种互补性的例证，我们展示了谷歌地球AI的人口动态基础模型（PDFM）——一个用于地理空间推断的基础模型——在四个领域（疫苗可预防疾病、传染病、非传染病、孕产妇心理健康）、五个任务（空间外推、插值/即时预测、概率预测、前瞻性预测、风险分层）和四个国家（美国、加拿大、墨西哥和刚果民主共和国）的独立全球健康案例研究。在这些案例研究中，PDFM解决了各领域的关键监测空白：通过捕捉国内模型遗漏的跨境行为溢出效应，改进美加边境的MMR疫苗接种覆盖率预测；即时预测心血管疾病以加速数据可用性；增强墨西哥城市登革热的短期预测，以便及时进行疫情媒介控制；改进霍乱热点地区的预测；以及为模型从未见过的美国各州的个体产后抑郁风险预测添加可转移信号，同时不替代个体社会经济数据或缩小人口筛查差距。总之，这些结果展示了地理空间基础模型在公共卫生监测中的能力。
英文摘要
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.
发表机构
- Google（谷歌）
- NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）
- Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
- University of Oxford（牛津大学）
- University of Washington（华盛顿大学）
- Health Stream Analytics, LLC（Health Stream Analytics有限责任公司）
- KHAI Ventures（KHAI Ventures公司）
机构由 AI 辅助整理，请以论文原文为准。
