Imperial College London
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DrSethFlaxman

Faculty of Natural Sciences, Department of Mathematics

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522Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Bhatt:2017:10.1098/rsif.2017.0520,
author = {Bhatt, S and Cameron, E and Flaxman, SR and Weiss, DJ and Smith, DL and Gething, PW},
doi = {10.1098/rsif.2017.0520},
journal = {Interface},
title = {Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization.},
url = {http://dx.doi.org/10.1098/rsif.2017.0520},
volume = {14},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Bhatt,S
AU  - Cameron,E
AU  - Flaxman,SR
AU  - Weiss,DJ
AU  - Smith,DL
AU  - Gething,PW
DO  - 10.1098/rsif.2017.0520
PY  - 2017///
SN  - 1742-5662
TI  - Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization.
T2  - Interface
UR  - http://dx.doi.org/10.1098/rsif.2017.0520
UR  - https://www.ncbi.nlm.nih.gov/pubmed/28931634
UR  - http://hdl.handle.net/10044/1/52816
VL  - 14
ER  -
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