# Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisation

arXiv: https://arxiv.org/abs/1612.03278 · HTML: https://mlsanity.com/paper/1612.03278/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2016.

## Built on by (most cited first, 8 of 8)

- [Explaining a Series of Models by Propagating Shapley Values](https://mlsanity.com/paper/2105.00108/) (2021)
- [Bayesian hierarchical stacking: Some models are (somewhere) useful](https://mlsanity.com/paper/2101.08954/) (2021)
- [Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases)](https://mlsanity.com/paper/2103.08182/) (2021)
- [Actually Sparse Variational Gaussian Processes](https://mlsanity.com/paper/2304.05091/) (2023)
- [Beyond Intuition, a Framework for Applying GPs to Real-World Data](https://mlsanity.com/paper/2307.03093/) (2023)
- [Diffusion Models for Inverse Problems in the Exponential Family](https://mlsanity.com/paper/2502.05994/) (2025)
- [Heterogeneous Ensemble Learning for Enhanced Crash Forecasts -- A Frequentest and Machine Learning based Stacking Framework](https://mlsanity.com/paper/2207.10721/) (2022)
- [Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in Kenya](https://mlsanity.com/paper/2305.19779/) (2023)

## Abstract (arXiv)

Maps of infectious disease---charting spatial variations in the force of infection, degree of endemicity, and the burden on human health---provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly acknowledge uncertainties in both the available measurements and their spatial interpolation. The most common such approach is that of Gaussian process regression, a mathematical framework comprised of two components: a mean function harnessing the predictive power of multiple independent variables, and a covariance function yielding spatio-temporal shrinkage against residual variation from the mean. Though many techniques have been developed to improve the flexibility and fitting of the covariance function, models for the mean function have typically been restricted to simple linear terms. For infectious diseases, known to be driven by complex interactions between environmental and socio-economic factors, improved modelling of the mean function can greatly boost predictive power. Here we present an ensemble approach based on stacked generalisation that allows for multiple, non-linear algorithmic mean functions to be jointly embedded within the Gaussian process framework. We apply this method to mapping Plasmodium falciparum prevalence data in Sub-Saharan Africa and show that the generalised ensemble approach markedly out-performs any individual method.