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

TL;DR

This paper introduces a Gaussian process ensemble method with stacked generalisation to improve disease risk mapping by capturing complex interactions, significantly enhancing predictive accuracy over traditional models.

Contribution

It presents a novel ensemble approach that integrates multiple non-linear mean functions within Gaussian process regression for better disease prevalence prediction.

Findings

Ensemble method outperforms individual models in predicting malaria prevalence.

Significant improvement in predictive accuracy demonstrated on real-world data.

Method effectively captures complex environmental and socio-economic interactions.

Abstract

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.