# Geographically Weighted Surrogate Models for Rapid Small-Area Chronic Disease Estimation

arXiv: https://arxiv.org/abs/2607.28655 · HTML: https://mlsanity.com/paper/2607.28655/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [New Important Developments in Small Area Estimation](https://mlsanity.com/paper/1302.4907/) (2013)
- [The GWmodel R package: Further Topics for Exploring Spatial Heterogeneity using Geographically Weighted Models](https://mlsanity.com/paper/1312.2753/) (2013)
- [A step towards the integration of machine learning and classic model-based survey methods](https://mlsanity.com/paper/2402.07521/) (2024)
- [Evaluation of A Spatial Microsimulation Framework for Small-Area Estimation of Population Health Outcomes Using the Behavioral Risk Factor Surveillance System](https://mlsanity.com/paper/2510.22080/) (2025)

## Abstract (arXiv)

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 for county-level SAE of ten chronic conditions across the US: COPD, asthma, heart disease, arthritis, cancer, depression, diabetes, high blood pressure, high cholesterol, and stroke. Our findings suggest that geographically weighted ML frameworks like geographically weighted random forest and geographically weighted regression offer scalable and open data surrogates for rapidly generating SAE and supporting data driven decision making.