Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization.

Title: Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization.
Authors: Bhatt, S
Cameron, E
Flaxman, SR
Weiss, DJ
Smith, DL
Gething, PW
Item Type: Journal Article
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 Gaussian process regression, a mathematical framework composed 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 generalization that allows for multiple nonlinear 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 generalized ensemble approach markedly outperforms any individual method.
Issue Date: 1-Sep-2017
Date of Acceptance: 30-Aug-2017
URI: http://hdl.handle.net/10044/1/52816
DOI: https://dx.doi.org/10.1098/rsif.2017.0520
ISSN: 1742-5662
Publisher: Royal Society, The
Journal / Book Title: Interface
Volume: 14
Issue: 134
Copyright Statement: © 2017 The Author(s) Published by the Royal Society. All rights reserved.
Sponsor/Funder: Medical Research Council (MRC)
Funder's Grant Number: MR/K010174/1B
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
Gaussian process
malaria
disease mapping
stacked generalization
PLASMODIUM-FALCIPARUM
VARIABLE SELECTION
RANDOM-FIELDS
REGRESSION
APPROXIMATION
INFERENCE
MACHINE
Gaussian process
disease mapping
malaria
stacked generalization
stat.AP
stat.AP
stat.ML
Gaussian process
disease mapping
malaria
stacked generalization
MD Multidisciplinary
General Science & Technology
Publication Status: Published
Conference Place: England
Appears in Collections:Mathematics
Statistics
Faculty of Medicine
Faculty of Natural Sciences
Epidemiology, Public Health and Primary Care



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