2026年10月6日 (英语).
Arbaaz Muslim，软件工程师，Google研究，和Gautam Prasad，软件工程师，Google研究
Google Earth AI的人口动态基础模型(PDFM)可以解决现有流行病学工作流程的数据差距和时间报告滞后问题。 在最近的工作中，我们介绍了五个伙伴驱动的案例研究，说明这一模型如何体现了全球公共卫生的地球地理空间基础模型范式。
公共卫生决策在很大程度上依赖于及时的颗粒数据。 对于诸如心血管疾病或产后抑郁症等健康状况，数据显示我们把资源和支助集中到哪里。 对于诸如登革热或霍乱等急性疾病爆发，它有助于为紧急业务规程和资源分配提供信息。 然而，传统的流行病监测往往受到数据限制的阻碍：多年报告滞后，数据被僵硬的地缘政治界限所包围，数据充斥。 即使没有这些限制，传统的建模方法也需要广泛的针对具体任务的数据收集和定制数据工程管道，这些管道在迅速爆发或资源受限的情况下难以部署。
为解决这些系统性瓶颈，我们引入了利用地球地理空间基础模型的公共卫生新模式。 使用Google Earth AI的人口动态基础模型(PDFM)作为概念的证明，我们演示如何将自我监督，预先训练的"位置"表达方式作为插图和游戏投入直接融入现有的健康科学和流行病学工作流程——加强统计学和机器学习(ML)模型流行病学家已经使用，而不是从零开始建造新的管道。 PDFM将隐私保护搜索趋势，人类流动性，建筑环境密度和环境决定因素压缩成嵌入位置。 这些现成位置嵌入在各种疾病领域、地理背景和流行病任务的常规投入上，并不需要特定任务微调，而是匹配或改进。
Google Earth AI的一部分——我们连接卫星图像、天气、匿名搜索趋势、人类流动性和其他人口动态的一套地理空间模型——PDFM使用自我监督的学习方式，将下列多样化的、保护隐私的信号合成紧凑的、多功能的嵌入体，作为在每月节点刷新的地点的“指纹”:
- 综合搜索趋势：已引起社区一级关注的专题和资源的搜索频率
- 建筑环境和流动性：药房、诊所和公园等场所的密度和繁忙程度。
- 环境决定因素：高分辨率天气和空气质量指标和统计。
与其要求研究人员自己收集和处理这些原始数据流，PDFM嵌入可以很容易地插入现有的ML工作流程，以提供随时可用的地理空间背景。
先前的研究表明，这些嵌入是任务不可知的。 由于这些日常信号能够捕捉健康的基本社会、行为和环境决定因素，同样的嵌入显示在填补多种疾病控制中心健康指标的差距方面表现强劲。
然而，建立全球卫生的新模式需要承担更大的举证责任。 为了达到这一标准，我们着手展示PDFM在全球不同环境中嵌入更广泛的各种健康挑战的价值。
为了实现这一目标，我们的全球卫生研究伙伴推动对五个不同的公共卫生挑战进行独立评价，涉及不同的流行病学任务、资源环境和疾病类型：
我们的评价表明，虽然流行病模式受到主权边界的限制，但它们所跟踪的结果却并非如此。 例如，在距加拿大边界150公里以内的146个美国县，只有国内模式往往难以预测当地的疫苗摄入量。
通过补充美国县城嵌入加拿大前进排序区嵌入，西奈山卫生系统和波士顿儿童医院的研究人员开发了能够捕捉跨界行为和流动性外溢效应的模型。 为了帮助公共卫生官员确定可能爆发麻疹的社区，研究人员利用这一新增的跨界背景，将模型解释的孕产妇死亡率疫苗接种覆盖率变化比例从16%提高到22%(增加了36%)，并对470万边境居民的覆盖率估计值至少调整了3个百分点，揭示出仅国内模式可能错过的地方模式。
心血管疾病(CVD)声称在美国每年有916 000多条生命。 但是，国家生命统计系统(NVSS)提供的县级官方死亡率数据通常滞后1至2年，而美国社区调查(ACS)的共变反映了过去2至3年的状况。 PDFM在更多地方可以使用，而且滞后程度要低得多，从而能够提高我们模拟疾病的能力。
我们纽约大学格罗斯曼医学院的合作伙伴在估计美国大约3 100个县的本年度CVD死亡人数时，测试了PDFM是否能够支持这些基于人口普查的投入。 他们发现PDFM和传统普查数据在统计学上没有显著差异，PDFM更新鲜，在更多地方可以提供：
- 与人口普查数据比较：使用PDFM的模型预测了2023个县级CVD的死亡，其准确程度与使用ACS人口和社会经济数据模型的模型相同(目前每县平均有18.7人对19.1人死亡)，同时将大县外误差(RMSE)减少20%(57.7 46.0)。 这些差异在统计学上并不显著，这表明人口行动计划可以取代人口普查的投入。
- 更及时、更广泛：加勒比国家联盟汇集了5年的调查数据，在收集一年后为发布。 本研究中使用的PDFM嵌入法是用单个月的数据构建的。 17个国家也有PDFM，而ACS只覆盖美国。
这些结果表明，PDFM能够使卫生部门根据当前情况指导预防资源，而不是等待多年调查周期。
在处理诸如登革热等病媒传染疾病的爆发时，时间尤其重要。 我们与牛津大学和蒙特雷大学的公共卫生研究人员合作，将PDFM与TimesFM 2.0(我们的开放时间序列基础模型)结合起来，预测2020年至2025年期间墨西哥2 450个城市的登革热病例数。
该模型的最大性能收益发生在预测一个月之前，在多达72%的活跃的登革热传播城市中，预测准确性有所提高，导致总误差减少3.4X大于降解。 这一业绩恰恰发生在及时的病媒控制和临床人员配置决定最为重要的地方，特别是考虑到短期预测的明显效用。
对产妇心理健康状况的筛查，如产后抑郁症(PPD)，通常依赖于临床摄入信息，而临床摄入信息很少能够反映决定母亲风险和获得护理机会的更广泛的社区条件。 为了检验地理空间基础模型是否能够帮助弥补这一差距，华盛顿大学的研究人员评价了PDFM嵌入332,970名CDC PRAMS国家调查参与者。
除了PDFM捕捉社区一级的社会经济条件(R2=0.45)之外，加上嵌入物为预测哪些母亲处于危险中提供了一致的、具有统计意义的动力(AUC+0.0020见州；+0.0038见州)。 关键是，这一信号在培训期间从未被该模型视为补充性的地方环境，当没有这些细节时，它将恢复母亲自己收入和保险记录的预测信号的15%。
这种可转移的背景在筛选资源如何用于新国家方面产生了最大的变化。 在卫生系统能够跟踪20%风险最高的母亲的模拟中，添加PDFM帮助了每年5 640名患有产后抑郁症的农村母亲。 或者，在旨在捕捉80%病例的系统中，PDFM每年减少17 723次虚假警报——显示地理空间嵌入如何有助于保健系统扩大农村外联或提高后续效率。
霍乱等水媒流行病的预警对于预先部署口服霍乱疫苗和清洁供水至关重要。 幸运的是，爆发是罕见的：在任何特定星期，全国403个卫生区中，不到100个卫生区有一个开始。 不幸的是，同样罕见的情况使他们难以预料。
为了解决世界卫生组织非洲区域办事处利用刚果民主共和国综合疾病监测和反应报告的国家监测数据所确定的一个使用案例，我们测试了轻量级、低资源版本的PDFM(适应互联网连接不足的地区)是否能够帮助预测霍乱热点。
好处取决于我们展望的未来。 一两周后，最近的案件数告诉了大部分情况，PDFM没有显著提高准确性。 4到8周后，当还有时间运送物资时，它帮助生产：
- 更尖锐的短名单：反应小组从一个短名单的高风险地区工作，所以每周我们检查这个模型的前五名选手中有多少人继续爆发。 8周前，PDFM 融资每周从1.78到2.10正确选择，提高18%。
- 在霍乱流行地区取得更大的成果：在15个报告霍乱的区中，至少有一半的周内报告霍乱病例，8周短名单中的精准点5，从0.3333增至0.3975，相对增加19%。
这些调查结果表明，虽然短期跟踪可以依靠最近的临床数据，但基础模型嵌入了基础环境、连通性和人口决定因素，补充了历史数据，从而可以提前一至两个月进行积极的规划。
地理空间基础模型能够从被动的、局部的模型化转向行星规模的主动和时间敏感的卫生情报。 目前的限制，如静态快照，正在推动对时间动态嵌入和地理转移学习进行积极研究。
通过将地球背景数据整合到现有的公共卫生工作流程中，我们可以帮助采取措施，改善保健资源、干预措施和疫情警报如何送达最需要这些数据的社区。 详阅此报。
PDFM嵌入在Preview中作为Population Dynamics Insights(人口动态透视)在商业上可供使用，这是一个地理空间嵌入来自Google地图平台的数据集。 学者和公共卫生研究人员也可要求为选定的、非业务性的研究使用案例提供免费的准入。
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本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
October 6, 2026
Arbaaz Muslim, Software Engineer, Google Research, and Gautam Prasad, Software Engineer, Google Research
With Google Earth AI’s Population Dynamics Foundation Model (PDFM), we can address the data gaps and temporal reporting lags of existing epidemiological workflows. In our latest work, we present five partner-driven case studies demonstrating how this model exemplifies the planetary geospatial foundation model paradigm for global public health.
Public health decisions rely heavily on timely, granular data. For health conditions, such as cardiovascular disease or postpartum depression, that data shows us where to focus resources and support. For acute disease outbreaks, such as dengue or cholera, it can help inform urgent operational protocols and resource allocation. However, conventional epidemiological surveillance is often hindered by limitations in data: multi-year reporting lags, data siloed by rigid geopolitical boundaries, and data sparsity. Even without these limitations, traditional modeling approaches require extensive task-specific data collection and custom data engineering pipelines that are difficult to deploy during rapid outbreaks or in resource-constrained settings.
To address these systemic bottlenecks, we introduce a new paradigm in public health leveraging planetary geospatial foundation models. Using Google Earth AI’s Population Dynamics Foundation Model (PDFM) as a proof-of-concept, we demonstrate how self-supervised, pre-trained representations of "place" can be integrated directly into existing health sciences and epidemiological workflows as plug-and-play inputs — enhancing the statistical and machine learning (ML) models epidemiologists already use, rather than building new pipelines from scratch. PDFM compresses privacy-preserving search trends, human mobility, built-environment density, and environmental determinants into location embeddings. Without requiring task-specific fine-tuning, these off-the-shelf location embeddings matched or improved on conventional inputs across a wide variety of disease domains, geographic settings, and epidemiological tasks.
Part of Google Earth AI — our suite of geospatial models connecting satellite imagery, weather, anonymous search trends, human mobility, and other population dynamics — PDFM uses self-supervised learning to synthesize the following diverse, privacy-preserving signals into compact, versatile embeddings that serve as “fingerprints” for locations refreshed at a monthly cadence:
- Aggregated search trends: Search frequencies of topics and resources that have garnered community-level interest
- Built environment and mobility: Local density and busyness of places such as pharmacies, clinics, and parks.
- Environmental determinants: High-resolution weather and air quality metrics and statistics.
Rather than requiring researchers to collect and process these raw data streams themselves, PDFM embeddings can be easily plugged into existing ML workflows to provide ready-to-use geospatial context.
Prior work showed that these embeddings are task-agnostic. Because these everyday signals capture the underlying social, behavioral, and environmental determinants of health, the same embeddings showed strong performance on filling gaps in a wide variety of CDC health metrics.
However, establishing a new paradigm for global health carries a higher burden of proof. To meet this standard, we set out to demonstrate the value of PDFM embeddings across a wider variety of health challenges, and in different environments across the globe.
To achieve this, our global health research partners facilitated independent evaluations across five distinct public health challenges — spanning diverse epidemiological tasks, resource settings, and disease types:
Our evaluations show that while epidemiological models are constrained by sovereign borders, the outcomes they track are not. For example, in the 146 U.S. counties situated within 150 km of the Canadian border, domestic-only models often struggle to predict local vaccine uptake.
By supplementing U.S. county embeddings with Canadian Forward Sortation Area embeddings, researchers at the Mount Sinai Health System and Boston Children’s Hospital developed models that captured cross-border behavioral and mobility spillovers. To help public health officials pinpoint communities at risk of measles outbreaks, the researchers used this added cross-border context to increase the share of variation in MMR vaccination coverage explained by the models from 16% to 22% (a 36% increase) and refine coverage estimates by at least 3 percentage points for 4.7 million border residents, revealing local patterns that domestic-only models may miss.
Cardiovascular disease (CVD) claims over 916,000 lives annually in the U.S. However, unsuppressed official county-level mortality data from the National Vital Statistics System (NVSS) typically lags by 1–2 years, and American Community Survey (ACS) covariates reflect conditions up to 2–3 years in the past. PDFM is available in more places and has much less of a lag, allowing for the ability to improve our capacity to model disease.
Our partners at NYU Grossman School of Medicine tested whether PDFM could stand in for these census-based inputs when estimating current-year CVD deaths across roughly 3,100 U.S. counties. They found no statistically significant differences between PDFM and traditional census data on nowcasting, with PDFM being much fresher and available in far more places:
- Comparable to census data: Models using PDFM predicted 2023 county-level CVD deaths about as accurately as models using ACS demographic and socioeconomic data (an average error of 18.7 vs. 19.1 deaths per county when nowcasting) while cutting large county outlier errors (RMSE) by 20% (57.7 → 46.0). The differences were not statistically significant, suggesting that PDFM can serve as a substitute for census inputs.
- More timely and more widely available: ACS covariates pool five years of survey data and are released up to a year after collection. The PDFM embeddings used in this study were built from a single month of data. PDFM is also available in 17 countries, while ACS covers only the U.S.
These results suggest that PDFM can enable health departments to guide prevention resources using current conditions rather than waiting on multi-year survey cycles.
Time is especially of the essence when addressing outbreaks of vector-borne diseases like dengue. In partnership with public health researchers at the University of Oxford and Tecnológico de Monterrey, we coupled PDFM with TimesFM 2.0 (our open time-series foundation model) to forecast dengue case counts across ~2,450 Mexican municipalities between 2020 and 2025.
The model’s greatest performance gains occurred when predicting one month ahead, improving forecast accuracy in up to 72% of active dengue transmission municipalities, yielding total error reductions 3.4X larger than degradations. This performance occurred precisely where timely vector control and clinical staffing decisions matter most, especially considering the demonstrated utility of short term forecasts.
Screening for maternal mental health conditions like postpartum depression (PPD) typically relies on clinical intake information, which rarely captures the broader community conditions that shape a mother’s risk and access to care. To test whether geospatial foundation models can help bridge this gap, researchers at the University of Washington evaluated PDFM embeddings across 332,970 participants in the CDC PRAMS national survey.
In addition to PDFM capturing community-level socioeconomic conditions (R2=0.45), adding the embeddings provided a consistent, statistically significant boost to predicting which mothers were at risk (AUC +0.0020 in seen states; +0.0038 in unseen states). Crucially, this signal held up in states the model had never seen during training—acting as complementary local context that recovers about 15% of the predictive signal of a mother’s own income and insurance records when those details are unavailable.
This transferable context made the biggest difference in how screening resources are directed in new states. In simulations where a health system can follow up with the 20% highest-risk mothers, adding PDFM helped reach 5,640 more rural mothers with postpartum depression each year. Alternatively, in systems aiming to catch 80% of all cases, PDFM cut 17,723 false alarms annually—showing how geospatial embeddings can help health systems either broaden rural outreach or improve follow-up efficiency.
Early warning for waterborne epidemics like cholera is critical for prepositioning oral cholera vaccines and clean water supplies. Fortunately, outbreaks are rare: in any given week, fewer than 1 in 100 of the country's 403 health zones sees one begin. Unfortunately, this very same rarity makes them hard to anticipate.
To address a use case defined by the World Health Organization Regional Office for Africa using national surveillance data from the Democratic Republic of the Congo’s Integrated Disease Surveillance and Response (IDSR) reporting, we tested whether a lightweight, low-resource version of PDFM (adapted for regions with sparse internet connectivity) could help forecast cholera hotspots.
The benefit depended on how far ahead we looked. One or two weeks out, recent case counts told most of the story and PDFM did not significantly enhance the accuracy. Four to eight weeks out, when there is still time to move supplies, it helped produce:
- Sharper shortlists: Response teams work from a short list of high-risk zones, so each week we checked how many of the model's top five picks went on to have an outbreak. Eight weeks ahead, PDFM raised this from 1.78 to 2.10 correct picks per week, an 18% improvement.
- Bigger gains where cholera is endemic: In the 15 zones that had reported cholera in at least half of all weeks, Precision@5 of eight-week shortlists improved from 0.3333 to 0.3975, a relative gain of 19%.
These findings demonstrate that while short-term tracking can rely on recent clinical data, foundation model embeddings capture underlying environmental, connectivity, and population determinants that supplement historical data, allowing for proactive planning one to two months in advance.
Geospatial foundation models enable moving from reactive, localized modeling to proactive and time-sensitive health intelligence at planetary scale. Current limitations, such as static snapshots, are driving active research into temporally dynamic embeddings and geographic transfer learning for under-connected regions.
By integrating planetary contextual data into existing public health workflows, we can help take steps to improve how healthcare resources, interventions, and outbreak alerts reach the communities that need them most. Read the paper for more details.
PDFM embeddings are commercially available in Preview as Population Dynamics Insights, a geospatial embeddings dataset from Google Maps Platform. Academics and public health researchers can also request no-cost access for select, non-operational research use cases.
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