Geographically Weighted Surrogate Models for Rapid Small-Area Chronic Disease Estimation
Small-area estimation (SAE) enables researchers and policymakers to identify spatial disparities in health outcomes, but survey-based SAE products carry an inherent lag. Gold-standard estimates such as CDC PLACES are released roughly two years after the underlying survey data are collected, limiting their use for time-sensitive decision-making. This study evaluates the potential for machine learning (ML) to serve as a surrogate, learning the relationship between frequently updated area-level predictors and existing SAE outputs to generate timely, comparable estimates in years when SAE from surveys are unavailable or delayed. We evaluate several global and geographically weighted ML models...
| Authors | Aanya Gupta, Szandra Péter, Sara Von Hoene et al. |
|---|---|
| Primary category | q-bio.QM |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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