cs.LGOct 5, 2026
SavePlanetary Geospatial Foundation Models: A New Paradigm for Global Public Health
Organizations: Google, Mountain View, CA, USA · Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA · Center for Child Health Services Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA · Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA · Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA · Computational Epidemiology Lab, Boston Children’s Hospital, Boston, MA, USA · Department of Biology, University of Oxford, Oxford, UK · Pandemic Sciences Institute, University of Oxford, Oxford, UK · Institute on Human Development and Disability, University of Washington, Seattle, WA, USA · Health Stream Analytics, LLC, Seattle, WA, USA · KHAI Ventures, Boston, MA, USA · National Autonomous University of Mexico, Mexico City, Mexico · Dirección General de Calidad y Educación en Salud, Secretaría de Salud, Mexico City, Mexico · Departments of Pediatrics and Biomedical Informatics, Harvard Medical School, Boston, MA, USA · World Health Organization Regional Office for Africa (WHO AFRO), Nairobi, Kenya · Emergency Preparedness and Response, World Health Organization Regional Office for Africa (WHO AFRO), Brazzaville, Republic of the Congo · Department of Anaesthesia, Harvard Medical School, Boston, MA, USA
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.
Figures & tables
Figure 1: Overview of Google Earth AI for global public health. Phase 1 encodes multimodal, privacy-preserving data streams (aggregated search trends, maps, busyness, and weather and air quality) via the Population Dynamics Foundation Model (PDFM) into generalizable regional fingerprint embeddings. Phase 2 integrates these place representations with domain-specific labeled health datasets and downstream statistical or machine-learning models across five epidemiological tasks: spatial extrapolation (MMR vaccination coverage), spatial interpolation and nowcasting (cardiovascular disease mortality), probabilistic forecasting (dengue outbreaks), prospective binary forecasting (cholera emergence), and individual-level risk stratification (postpartum depression).
| ML Problem | Geographic Scope | Thematic Domain | Benchmark Dataset | Prediction Targets | Train / Test Granularity | Train / Test Size |
|---|---|---|---|---|---|---|
| Spatial Extrapolation | Global North (US, Canada) | Vaccine-Preventable (Immunization) | MMR Vaccination Uptake (ONM/MRP) | County-level MMR vaccination rate | US County / Canadian FSA (postal) | 3,080 US counties (10-fold CV); 146 border counties subset |
| Interpolation | Global North (US) | Non-communicable (Cardiovascular) | NVSS CVD Mortality | County-level CVD death counts | US County (Census-equivalent) | 3,091 counties (5-fold CV) |
| Nowcasting | Global North (US) | Non-communicable (Cardiovascular) | NVSS CVD Mortality | County-level CVD death counts | US County (Census-equivalent) | 30,910 county-years (2013–2022) / 3,091 counties (2023) |
| Probabilistic Forecasting | Global South (Mexico) | Communicable (Dengue) | Mexico Dengue Surveillance for the Mexican Ministry of Health | Monthly dengue case counts | GADM Admin Level 2 (municipality) | 2,450 municipalities 68 months (Jan 2020–Aug 2025); 24-month sliding window |
| Prospective Forecasting | Global South (DRC) | Communicable (Cholera) | DRC National Integrated Disease Surveillance | Binary cholera emergence at 1-, 2-, 4-, and 8-week leads | Health zone week | 403 health zones; train: 722 weeks (4 Jan 2010–31 Oct 2023) / test: 89 weeks (1 Nov 2023–14 Jul 2025) |
| Risk Stratification | Global North (US) | Maternal Mental Health (PPD) | CDC PRAMS, Phases 7–8 (births 2012–2021) | Binary postpartum depressive symptoms (CDC indicator: MH_PPDPR or MH_PPINT “always” or “often”) | Respondent; PDFM v0 embedding at state urban/rural (87 locations) | 332,970 respondents; sealed tests: 66,594 holdout (S1), 10 jurisdiction-held-out folds (S2), 2020–21 births (S3) |
Table 1: Summary of Evaluation Benchmarks and Dataset Specifications. Overview of target variables, geographic scopes, and spatial resolutions across the five evaluation paradigms.
| Geography | Model | Correlation | RMSE | MAE | |
|---|---|---|---|---|---|
| All US counties | US embeddings only | 0.611 | 0.058 | 0.045 | 0.373 |
| Canadian context only | 0.113 | 0.072 | 0.059 | 0.013 | |
| US + Canadian context | 0.614 | 0.057 | 0.044 | 0.381 | |
| US–CA border counties | US embeddings only | 0.399 | 0.070 | 0.055 | 0.159 |
| Canadian context only | 0.381 | 0.071 | 0.056 | 0.146 | |
| US + Canadian context | 0.465 | 0.068 | 0.051 | 0.216 |
Table 2: Predictive performance of models using U.S. and Canadian contextual information to predict county-level MMR vaccination coverage. We show results for all 3,080 US counties (top three rows) and for all 146 counties on the US–Canada border (bottom three rows). For each of these test cases, the best results on each of the reported metrics are in bold.
Figure 2: Spatial distribution of cross-border predictive influence between Canadian postal regions and U.S. counties. The map illustrates where Canadian contextual embeddings contribute to improved prediction of U.S. county vaccination rates. Orange shading indicates U.S. counties where incorporating Canadian contextual embeddings reduces prediction error relative to a model using U.S. embeddings alone; darker shades correspond to larger reductions in absolute prediction error. Blue shading indicates Canadian postal regions whose embeddings exert the greatest influence in the cross-border prediction model, measured by the increase in prediction error that occurs when the information from that region is removed from the model through a leave-one-out procedure.
Figure 3: Cumulative population living in U.S. border counties whose predicted vaccination coverage changes by at least a given threshold after incorporating Canadian contextual information. For each threshold, the corresponding affected population is presented as both population count and the percentage of the total border-county population.
| Bayesian spatial model | Gradient boosted decision tree model | |||
| Model configuration | MAE | RMSE | MAE | RMSE |
| Baseline model | 48.44 (44.36, 52.87) | 126.87 (111.42, 153.35) | 74.64 (62.10, 94.37) | 368.57 (171.94, 565.82) |
| + ACS | 42.07 ∗∗ (36.53, 45.94) | 125.89 (87.04, 152.19) | 74.02 (60.07, 98.91) | 406.02 (174.89, 585.12) |
| + PDFM | 44.95 (40.59, 50.12) | 133.70 (96.90, 165.19) | 79.58 (63.74, 107.56) | 411.00 (185.25, 597.87) |
| + ACS + PDFM | 47.77 (38.87, 52.47) | 179.09 (101.72, 245.96) | 75.81 (60.90, 96.62) | 403.91 (166.70, 583.31) |
Table 3: Spatial interpolation results showing mean MAE and pooled mean RMSE (in county-level counts of cardiovascular disease deaths; lower values indicate better accuracy) from 20% county holdout tests in 2023. Values in parentheses denote the minimum–maximum range across 5 cross-validation folds for Bayesian models and the 2.5th–97.5th percentile interval across 100 bootstrap replicates for GBDT models. Bold reflects the lowest MAE and RMSE across all models. ** indicates p<0.01 versus Baseline (clustered sign-flip test significant and clustered bootstrap 95% confidence interval for paired error differences excludes zero); all other differences from Baseline, as well as all pairwise differences between ACS and PDFM configurations, are not statistically significant.
| Bayesian spatial model | Gradient boosted decision tree model | |||
|---|---|---|---|---|
| Model configuration | MAE | RMSE | MAE | RMSE |
| Baseline model | 23.99 | 71.22 | 26.18 (25.51, 27.17) | 216.38 (193.67, 240.93) |
| + ACS | 24.30 | 72.83 | 19.13 ∗ (18.63, 19.86) | 57.69 ∗∗ (48.79, 71.58) |
| + PDFM | 24.28 | 72.65 | 18.66 ∗ (18.43, 19.22) | 46.00 ∗∗ (44.96, 47.61) |
| + ACS + PDFM | 23.59 ∗∗∗ | 69.62 ∗∗∗ | 18.52 ∗∗ (18.39, 19.15) | 46.02 ∗∗ (45.14, 49.18) |
Table 4: Nowcasting results showing MAE and pooled RMSE (in county-level counts of cardiovascular disease deaths; lower values indicate better accuracy) for the 100% county holdout test in 2023 (trained on 2013–2022). Values in parentheses for GBDT models denote the 2.5th–97.5th percentile interval across 100 random-seed replicates. Bold reflects the lowest MAE and RMSE across all models. * indicates p<0.05, ** indicates p<0.01, and *** indicates p<0.001 versus Baseline (clustered sign-flip test significant and clustered bootstrap 95% confidence interval for paired error differences excludes zero); pairwise differences between ACS and PDFM configurations are not statistically significant.
Figure 4: PDFM improves short-horizon dengue forecasting during active transmission in Mexico. A) Monthly reported dengue cases during the January 2020–August 2025 evaluation period. B) Mean difference in Weighted Interval Score ( ) between PDFM-adjusted and baseline forecasts by forecast target month for 1-, 3-, and 6-month forecast horizons. Negative values indicate improved probabilistic forecast performance with PDFM. C) Mean WIS stratified by municipality-level dengue case burden. Municipality-months with zero reported cases are shown separately; non-zero observations are divided into low-, medium-, and high-case groups. Shaded regions indicate 95% confidence intervals estimated using a temporal moving-block bootstrap with three-month blocks. D) Mean WIS by calendar month for 1-, 3-, and 6-month forecast horizons. Shaded regions indicate 95% confidence intervals estimated using the same temporal moving-block bootstrap.
Figure 5: Distribution and cumulative magnitude of time-averaged forecast accuracy differences across Mexican municipalities. A) Histogram displaying the difference in mean Weighted Interval Score ( ) at Horizon 1 between the TimesFM model augmented with PDFM and the baseline TimesFM without PDFM. Differences are computed over the evaluation period from January 2020 to August 2025 across 2,457 municipalities. The solid black vertical line marks zero difference; negative values ( ) indicate a lower average forecast error for the model utilizing PDFM. The solid and dashed blue vertical lines indicate the mean and median, respectively. The distribution has a mean difference of 0.0051 and a median of 0.0013. Overall, the inclusion of PDFM improved average forecast performance in 47.6% (1,170 out of 2,457) of the evaluated municipalities. B) Cumulative magnitude of municipality-level mean WIS after separately ranking municipalities with improved and worsened performance from largest to smallest effect magnitude. Gross improvement, defined as the sum of absolute mean WIS among municipalities with WIS < 0, was 17.61, compared with gross deterioration of 5.10 among municipalities with WIS > 0. Thus, although deterioration occurred in slightly more municipalities, improvements were substantially larger in magnitude.
Figure 6: Geographic distribution of reported dengue burden and change in Weighted Interval Score (WIS) applying PDFM embeddings in Mexico. A) Mean monthly reported dengue case burden by municipality over the evaluation period. Values are displayed as log(1 + mean monthly cases). B) Mean difference in Weighted Interval Score ( ) for one-month-ahead forecasts by municipality over the same evaluation period. Negative WIS indicates improved forecast performance with PDFM relative to baseline. Across 2,457 municipalities, 1,170 (47.6%) had negative mean WIS; the median municipality-level WIS was 0.0013 (interquartile range 0.0111 to 0.0035), indicating that the overall improvement was driven by larger gains in a subset of municipalities rather than uniform improvement across locations.
| Lead Time | Metric | History-only | History+PDFM | (%) | 95% CI | p |
|---|---|---|---|---|---|---|
| 1 wk | PR-AUC | 0.2931 | 0.2969 | 0.0038 ( 1.3) | 0.0161 to 0.0258 | 0.72 |
| Precision@5 | 0.2841 | 0.2614 | 0.0227 ( 8.0) | 0.0432 to 0.0023 | 0.103 | |
| 2 wk | PR-AUC | 0.2990 | 0.2860 | 0.0129 ( 4.3) | 0.0304 to 0.0055 | 0.23 |
| Precision@5 | 0.3011 | 0.3126 | 0.0115 ( 3.8) | 0.0138 to 0.0368 | 0.52 | |
| 4 wk | PR-AUC | 0.2832 | 0.3107 | 0.0275 ( 9.7) | 0.0089 to 0.0479 | 0.019 |
| Precision@5 | 0.3435 | 0.3906 | 0.0471 ( 13.7) | 0.0141 to 0.0776 | 0.019 |
Table 5: Prospective emergence forecasting performance, all health zones. History+PDFM versus History-only, predictions pooled over five random seeds. Lead time is the prediction horizon. is History+PDFM minus History-only. CIs are unadjusted percentile bootstraps while p-values are Benjamini–Hochberg adjusted within this table.
Figure 7: PDFM in individual-level postpartum-depression risk prediction (PRAMS, 332,970 mothers). A) Change in discrimination ( AUC, 95% CI) from adding PDFM embeddings. Rows: seen states (S1, pre-registered primary); unseen states (S2); later years (S3); real vs random embedding in unseen states; PDFM added beyond individual income and Medicaid; and PDFM in place of them. B) Rural mothers in unseen states (exploratory; US-weighted, per year): change in PPD cases found and in false alarms under two screening policies. Fixed screening capacity flags the 20% of mothers at highest predicted risk. The fixed target uses a threshold set on training data for 80% sensitivity. C) Sensitivity by group under the 80% rule, without (blue) and with (orange) PDFM. The dashed line marks 80%. Error bars are 95% bootstrap CIs; S2 CIs resample jurisdictions.
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
| Min. change (pp) | Border counties | % Border counties | Affected population | % Border pop. | % Total pop. |
|---|---|---|---|---|---|
| 10.0 | 3 | 2.1 | 241,045 | 1.3 | 0.1 |
| 7.5 | 9 | 6.2 | 1,672,517 | 9.0 | 0.5 |
| 5.0 | 24 | 16.4 | 2,502,339 | 13.4 | 0.8 |
| 4.0 | 35 | 24.0 | 3,529,624 | 19.0 | 1.1 |
| 3.0 | 55 | 37.7 | 4,706,150 | 25.3 | 1.5 |
| 2.0 | 74 | 50.7 | 8,540,213 | 45.9 | 2.6 |
Extended Data Table 1: Cumulative population in U.S. border counties affected by incorporating Canadian contextual information, by threshold of change in predicted MMR vaccination coverage.
| Regime | Lead Time | Metric | History-only | History+PDFM | (%) | 95% CI | p |
|---|---|---|---|---|---|---|---|
| Endemic | 1 wk | PR-AUC | 0.4679 | 0.4748 | 0.0069 ( 1.5) | 0.0388 to 0.0494 | 0.99 |
| Endemic | 1 wk | Precision@5 | 0.2318 | 0.2250 | 0.0068 ( 2.9) | 0.0159 to 0.0023 | 0.74 |
| Endemic | 2 wk | PR-AUC | 0.4637 | 0.4444 | 0.0193 ( 4.2) | 0.0611 to 0.0251 | 0.75 |
| Endemic | 2 wk | Precision@5 | 0.2621 | 0.2644 | 0.0023 ( 0.9) | 0.0046 to 0.0115 | 1.00 |
| Endemic | 4 wk | PR-AUC | 0.4209 | 0.4729 | 0.0521 ( 12.4) | 0.0172 to 0.0887 | 0.024 |
| Endemic | 4 wk | Precision@5 | 0.3082 | 0.3200 | 0.0118 ( 3.8) | 0.0024 to 0.0259 | 0.47 |
Extended Data Table 2: Performance stratified by pre-evaluation endemicity regime
| Setting | Model | AUC (95% CI) | Brier score | Observed / expected | Calibration slope |
|---|---|---|---|---|---|
| S1: seen states (primary) | Baseline | 0.622 (0.616–0.629) | 0.1174 | 1.00 | 1.05 |
| PDFM (replaces state) | 0.624 (0.618–0.630) | 0.1173 | 1.00 | 1.02 | |
| Baseline + income, Medicaid | 0.636 (0.630–0.642) | 0.1168 | 1.00 | 1.06 | |
| Income, Medicaid + PDFM | 0.637 (0.631–0.643) | 0.1167 | 1.00 | 1.10 | |
| S2: unseen states | Baseline | 0.615 (0.604–0.625) | 0.1177 | 1.00 | 1.02 |
| PDFM (replaces state) | 0.618 (0.607–0.630) | 0.1176 | 1.01 | 1.06 |
Extended Data Table 3: Test-set performance (unweighted)
| Setting | Comparison | Estimate (95% CI) | Pre-defined reading | Holm p (S1) |
|---|---|---|---|---|
| S1: seen states (primary) | PDFM vs baseline | 0.0020 ( 0.0008 to 0.0031) | improvement | 0.012 |
| PDFM instead of income and Medicaid (non-inferiority, margin 0.005) | 0.0116 ( 0.0144 to 0.0089) | harm | 1.000 | |
| Share of the income/Medicaid gain recovered by PDFM ( ) | 0.15 (0.06 to 0.24) | not above 0.5 | — | |
| PDFM vs area poverty share | 0.0022 ( 0.0011 to 0.0033) | improvement | — | |
| PDFM added to income and Medicaid | 0.0011 ( 0.0000 to 0.0022) | improvement | 0.104 | |
| S2: unseen states | PDFM vs baseline | 0.0038 ( 0.0004 to 0.0072) | improvement | — |
Extended Data Table 4: Pre-registered comparisons ( AUC unless stated)
| Fixed screening capacity (20%) | 80% sensitivity target | ||||
|---|---|---|---|---|---|
| Setting (rural, prev.) | Group | Cases found | False alarms | Cases found | False alarms |
| US (observed) | rural | 157 ( 72 to 246) | 653 ( 249 to 1,093) | 48 ( 87 to 18) | 493 ( 891 to 193) |
| urban | 70 ( 171 to 32) | 740 ( 1,188 to 321) | 50 ( 38 to 134) | 288 ( 579 to 1,040) | |
| all births | 87 ( 13 to 191) | 87 ( 191 to 12) | 3 ( 114 to 102) | 205 ( 1,452 to 735) | |
| World (42.7%, 17.2%) | rural | 337 ( 137 to 545) | 948 ( 329 to 1,574) | 175 ( 305 to 63) | 1,300 ( 2,097 to 521) |
| urban | 214 ( 379 to 65) | 1,070 ( 1,667 to 433) | 45 ( 35 to 119) | 187 ( 371 to 676) | |
Extended Data Table 5: Screening simulator: PDFM vs baseline transported to other settings (exploratory)
| Step | Remaining | Excluded |
|---|---|---|
| Phases 7–8 respondents | 390,320 | — |
| Both outcome items answered | 380,689 | 9,631 |
| Core predictors complete | 356,626 | 24,063 |
| NCHS urban/rural known (1 or 2) | 350,723 | 5,903 |
| Jurisdiction present in PDFM (absent: AK, HI) | 332,970 | 17,753 |
| Urban/rural stratum present in PDFM | 332,970 | 0 |
Extended Data Table 6: Cohort flow